From b7707618322f3809831166efc2c1df6816ed023a Mon Sep 17 00:00:00 2001 From: erik Date: Sat, 11 Jul 2026 00:27:05 -0500 Subject: [PATCH] feat: Enhance CSV cleaning service with new endpoints for CSV analysis and preview functionality. Introduce support for job management linked to extraction batches, and update the cleaning rules structure. Add new utility functions for handling CSV data and improve documentation for clarity on usage and available endpoints. --- .gitignore | 1 + docs/database.dbml | 10 +- services/api_gateway/api/main.py | 8 +- services/cleaning/README.md | 17 +- services/cleaning/api/main.py | 388 ++- services/cleaning/domain/csv_cleaning.py | 2130 ----------------- .../cleaning/domain/csv_cleaning/__init__.py | 64 + .../cleaning/domain/csv_cleaning/analyze.py | 94 + .../cleaning/domain/csv_cleaning/apply.py | 105 + .../domain/csv_cleaning/column_hints.py | 210 ++ .../cleaning/domain/csv_cleaning/cop_mod.py | 162 ++ .../cleaning/domain/csv_cleaning/dates.py | 459 ++++ .../cleaning/domain/csv_cleaning/documents.py | 175 ++ services/cleaning/domain/csv_cleaning/io.py | 132 + .../cleaning/domain/csv_cleaning/models.py | 67 + .../cleaning/domain/csv_cleaning/notas.py | 73 + .../cleaning/domain/csv_cleaning/nullish.py | 24 + .../cleaning/domain/csv_cleaning/numbers.py | 258 ++ .../domain/csv_cleaning/rules/__init__.py | 13 + .../domain/csv_cleaning/rules/builtins.py | 135 ++ .../domain/csv_cleaning/rules/dates.py | 141 ++ .../domain/csv_cleaning/rules/documents.py | 23 + .../domain/csv_cleaning/rules/numeric.py | 57 + .../domain/csv_cleaning/rules/registry.py | 80 + .../domain/csv_cleaning/rules/special.py | 8 + .../domain/csv_cleaning/rules/text.py | 40 + .../domain/csv_cleaning/type_inference.py | 143 ++ services/cleaning/infrastructure/database.py | 28 +- services/cleaning/requirements.txt | 1 + .../versions/0003_cleaning_csv_and_job_id.py | 90 + services/desktop_app/.env.minio.example | 11 + services/desktop_app/README.md | 52 +- services/desktop_app/electron/autoUpdate.cjs | 224 ++ services/desktop_app/electron/icon.png | Bin 0 -> 56966 bytes services/desktop_app/electron/main.cjs | 37 + services/desktop_app/electron/preload.cjs | 7 + services/desktop_app/index.html | 1 + services/desktop_app/package-lock.json | 401 +++- services/desktop_app/package.json | 18 +- services/desktop_app/public/app-icon.png | Bin 0 -> 165484 bytes .../desktop_app/resources/icon-source.png | Bin 0 -> 53035 bytes services/desktop_app/resources/icon.ico | Bin 0 -> 91063 bytes services/desktop_app/resources/icon.png | Bin 0 -> 165484 bytes .../desktop_app/scripts/publish-version.mjs | 188 ++ .../desktop_app/src/renderer/App.test.tsx | 167 +- services/desktop_app/src/renderer/App.tsx | 245 +- .../src/renderer/assets/app-icon.png | Bin 0 -> 56966 bytes .../components/BackgroundProgressBanner.tsx | 5 +- .../renderer/components/CleaningStepper.tsx | 44 + .../renderer/components/CountingWizard.tsx | 30 +- .../components/ExtractionResultsTable.tsx | 147 ++ .../renderer/components/ExtractionWizard.tsx | 69 +- .../src/renderer/components/UpdatePrompt.tsx | 172 ++ .../desktop_app/src/renderer/lib/apiClient.ts | 56 +- .../src/renderer/lib/cleaningSettings.test.ts | 32 + .../src/renderer/lib/cleaningSettings.ts | 66 + .../desktop_app/src/renderer/lib/csv.test.ts | 30 +- services/desktop_app/src/renderer/lib/csv.ts | 62 +- .../modules/cleaning/CleaningModule.ts | 18 + .../modules/cleaning/CleaningResults.tsx | 125 + .../modules/cleaning/CleaningWizard.tsx | 967 ++++++++ .../modules/cleaning/CleaningWizardModule.tsx | 26 + .../modules/counting/CountingResults.tsx | 28 +- .../modules/extraction/ExtractionResults.tsx | 89 +- .../extraction/JobExtractionWizard.tsx | 149 +- services/desktop_app/src/renderer/styles.css | 789 +++++- .../src/renderer/types/jobSection.ts | 2 +- .../src/renderer/views/JobResultsView.tsx | 277 ++- .../src/renderer/views/JobView.tsx | 22 +- .../src/renderer/views/SettingsView.tsx | 471 ++++ services/desktop_app/src/shared/types.ts | 107 +- services/extraction/api/main.py | 3 +- .../extraction/infrastructure/database.py | 12 +- 73 files changed, 7614 insertions(+), 2671 deletions(-) delete mode 100644 services/cleaning/domain/csv_cleaning.py create mode 100644 services/cleaning/domain/csv_cleaning/__init__.py create mode 100644 services/cleaning/domain/csv_cleaning/analyze.py create mode 100644 services/cleaning/domain/csv_cleaning/apply.py create mode 100644 services/cleaning/domain/csv_cleaning/column_hints.py create mode 100644 services/cleaning/domain/csv_cleaning/cop_mod.py create mode 100644 services/cleaning/domain/csv_cleaning/dates.py create mode 100644 services/cleaning/domain/csv_cleaning/documents.py create mode 100644 services/cleaning/domain/csv_cleaning/io.py create mode 100644 services/cleaning/domain/csv_cleaning/models.py create mode 100644 services/cleaning/domain/csv_cleaning/notas.py create mode 100644 services/cleaning/domain/csv_cleaning/nullish.py create mode 100644 services/cleaning/domain/csv_cleaning/numbers.py create mode 100644 services/cleaning/domain/csv_cleaning/rules/__init__.py create mode 100644 services/cleaning/domain/csv_cleaning/rules/builtins.py create mode 100644 services/cleaning/domain/csv_cleaning/rules/dates.py create mode 100644 services/cleaning/domain/csv_cleaning/rules/documents.py create mode 100644 services/cleaning/domain/csv_cleaning/rules/numeric.py create mode 100644 services/cleaning/domain/csv_cleaning/rules/registry.py create mode 100644 services/cleaning/domain/csv_cleaning/rules/special.py create mode 100644 services/cleaning/domain/csv_cleaning/rules/text.py create mode 100644 services/cleaning/domain/csv_cleaning/type_inference.py create mode 100644 services/db/alembic/versions/0003_cleaning_csv_and_job_id.py create mode 100644 services/desktop_app/.env.minio.example create mode 100644 services/desktop_app/electron/autoUpdate.cjs create mode 100644 services/desktop_app/electron/icon.png create mode 100644 services/desktop_app/public/app-icon.png create mode 100644 services/desktop_app/resources/icon-source.png create mode 100644 services/desktop_app/resources/icon.ico create mode 100644 services/desktop_app/resources/icon.png create mode 100644 services/desktop_app/scripts/publish-version.mjs create mode 100644 services/desktop_app/src/renderer/assets/app-icon.png create mode 100644 services/desktop_app/src/renderer/components/CleaningStepper.tsx create mode 100644 services/desktop_app/src/renderer/components/ExtractionResultsTable.tsx create mode 100644 services/desktop_app/src/renderer/components/UpdatePrompt.tsx create mode 100644 services/desktop_app/src/renderer/lib/cleaningSettings.test.ts create mode 100644 services/desktop_app/src/renderer/lib/cleaningSettings.ts create mode 100644 services/desktop_app/src/renderer/modules/cleaning/CleaningModule.ts create mode 100644 services/desktop_app/src/renderer/modules/cleaning/CleaningResults.tsx create mode 100644 services/desktop_app/src/renderer/modules/cleaning/CleaningWizard.tsx create mode 100644 services/desktop_app/src/renderer/modules/cleaning/CleaningWizardModule.tsx create mode 100644 services/desktop_app/src/renderer/views/SettingsView.tsx diff --git a/.gitignore b/.gitignore index aea078a..70546ac 100644 --- a/.gitignore +++ b/.gitignore @@ -140,6 +140,7 @@ celerybeat.pid # Environments .env +.env.minio .venv env/ venv/ diff --git a/docs/database.dbml b/docs/database.dbml index 2e29b1b..58929a0 100644 --- a/docs/database.dbml +++ b/docs/database.dbml @@ -167,7 +167,9 @@ Table extraction_rows { Table cleaning_jobs { id varchar(36) [pk, note: 'UUID v4'] - extraction_batch_id varchar(36) [not null, ref: > extraction_batches.id] + extraction_batch_id varchar(36) [null, ref: > extraction_batches.id, note: 'Null si source=csv'] + job_id varchar(36) [null, ref: > jobs.id, note: 'Trabajo Desktop al que pertenece'] + source varchar(32) [not null, default: 'extraction', note: 'extraction | csv'] status varchar(32) [not null, default: 'pending', note: 'pending | running | completed | failed'] request jsonb [not null, default: '{}', note: 'CleaningRequest (rules_per_column, etc.)'] log jsonb [not null, default: '[]', note: 'Lista de mensajes de auditoría del job'] @@ -177,16 +179,18 @@ Table cleaning_jobs { indexes { extraction_batch_id + job_id + source status } - Note: 'Ejecución de reglas de limpieza sobre las filas de un lote de extracción.' + Note: 'Ejecución de reglas de limpieza sobre filas de extracción o CSV subido.' } Table cleaned_rows { id integer [pk, increment] cleaning_job_id varchar(36) [not null, ref: > cleaning_jobs.id] - extraction_batch_id varchar(36) [not null, ref: > extraction_batches.id] + extraction_batch_id varchar(36) [null, ref: > extraction_batches.id] row_index integer [not null, default: 0, note: 'Conserva el orden del lote original'] row_json jsonb [not null, default: '{}', note: 'Fila tras aplicar reglas de limpieza'] created_at timestamptz [not null] diff --git a/services/api_gateway/api/main.py b/services/api_gateway/api/main.py index 3048722..b5960d5 100644 --- a/services/api_gateway/api/main.py +++ b/services/api_gateway/api/main.py @@ -66,7 +66,13 @@ def _client() -> httpx.AsyncClient: def _is_upload_path(path: str) -> bool: - return path.startswith("/count/") or "/ingest" in path or "/upload" in path + return ( + path.startswith("/count/") + or "/ingest" in path + or "/upload" in path + or path.startswith("/cleaning/analyze-csv") + or path.startswith("/cleaning/jobs/from-csv") + ) def _timeout_for_path(path: str) -> httpx.Timeout: diff --git a/services/cleaning/README.md b/services/cleaning/README.md index e5b2b77..57138cb 100644 --- a/services/cleaning/README.md +++ b/services/cleaning/README.md @@ -18,7 +18,11 @@ Recibe lotes de filas extraídas por el servicio `extraction` y aplica reglas co | `GET` | `/health` | Liveness | | `GET` | `/rules` | Lista de reglas de limpieza disponibles | | `GET` | `/batches/{batch_id}/analysis` | Análisis de columnas de un lote de extracción | -| `POST` | `/jobs` | Crear job de limpieza | +| `POST` | `/analyze-csv` | Análisis de columnas desde CSV (multipart) | +| `POST` | `/preview` | Vista previa Antes/Después sobre muestra (sin persistir) | +| `POST` | `/jobs` | Limpieza desde `extraction_batch_id` (+ `job_id` opcional) | +| `POST` | `/jobs/from-csv` | Limpieza desde CSV (multipart + reglas JSON) | +| `GET` | `/jobs?job_id=` | Listar jobs (filtro opcional por trabajo Desktop) | | `GET` | `/jobs/{job_id}` | Estado y detalle del job | | `GET` | `/jobs/{job_id}/rows` | Filas limpias del job | @@ -44,7 +48,7 @@ Las reglas disponibles se listan en `GET /rules`. ## Reglas de limpieza -Definidas en `domain/csv_cleaning.py`. Cada regla es una función que transforma los valores de una columna. Se pueden encadenar múltiples reglas por columna. +Definidas en el paquete `domain/csv_cleaning/` (registro en `rules/builtins.py`). Cada regla transforma los valores de una columna; se pueden encadenar varias por columna. Para agregar una regla nueva, ver `rules/registry.py`. --- @@ -72,11 +76,14 @@ cleaning_jobs — job de limpieza (FK → extraction_batches) ``` api/ - main.py — FastAPI app con todos los endpoints + main.py — FastAPI app con todos los endpoints domain/ - csv_cleaning.py — reglas de limpieza y función de análisis + csv_cleaning/ — paquete de limpieza (análisis + reglas) + analyze.py — resumen por columna + apply.py — aplicación de reglas + rules/ — registro e implementaciones de reglas infrastructure/ - database.py — modelos SQLAlchemy y sesión + database.py — modelos SQLAlchemy y sesión Dockerfile requirements.txt ``` diff --git a/services/cleaning/api/main.py b/services/cleaning/api/main.py index 505da45..dc7e365 100644 --- a/services/cleaning/api/main.py +++ b/services/cleaning/api/main.py @@ -6,16 +6,24 @@ import json from typing import Any import pandas as pd -from fastapi import FastAPI, HTTPException +from fastapi import FastAPI, File, Form, HTTPException, UploadFile from pydantic import BaseModel, Field from sqlalchemy import delete, select -from domain.csv_cleaning import CLEANING_RULES, analyze_dataframe, apply_rules +from domain.csv_cleaning import ( + CLEANING_RULES, + analyze_dataframe, + apply_rules, + load_csv_bytes, + sample_dataframe_rows, +) from infrastructure.database import ( CleanedRow, CleaningJob, ExtractionBatch, ExtractionRow, + Job, + Task, init_db, session_scope, utcnow, @@ -36,12 +44,22 @@ app = FastAPI( class CleaningRequest(BaseModel): - extraction_batch_id: str + extraction_batch_id: str | None = None + job_id: str | None = None rules_per_column: dict[str, list[str]] = Field(default_factory=dict) cop_mod_fecha_column: dict[str, str] = Field(default_factory=dict) cop_mod_config: dict[str, Any] | None = None +class PreviewRequest(BaseModel): + rows: list[dict[str, Any]] = Field(default_factory=list) + extraction_batch_id: str | None = None + rules_per_column: dict[str, list[str]] = Field(default_factory=dict) + cop_mod_fecha_column: dict[str, str] = Field(default_factory=dict) + cop_mod_config: dict[str, Any] | None = None + sample_size: int = 8 + + def dataframe_to_records(df: pd.DataFrame) -> list[dict[str, Any]]: return json.loads(df.to_json(orient="records", date_format="iso")) @@ -50,6 +68,8 @@ def serialize_job(job: CleaningJob) -> dict[str, Any]: return { "id": job.id, "extraction_batch_id": job.extraction_batch_id, + "job_id": job.job_id, + "source": job.source, "status": job.status, "request": job.request, "log": job.log, @@ -59,6 +79,144 @@ def serialize_job(job: CleaningJob) -> dict[str, Any]: } +def _validate_job_id(session: Any, job_id: str | None) -> None: + if not job_id: + return + if session.get(Job, job_id) is None: + raise HTTPException(status_code=404, detail="Trabajo (job) no encontrado") + + +def _create_cleaning_task( + session: Any, + *, + job_id: str | None, + cleaning_job_id: str, + source: str, + status: str, + request: dict[str, Any], + error: str | None = None, + title: str | None = None, +) -> str | None: + """Crea Task(type=cleaning) vinculada al job Desktop. Devuelve task.id o None.""" + if not job_id: + return None + now = utcnow() + task = Task( + job_id=job_id, + type="cleaning", + status=status, + title=title or f"Limpieza ({source})", + request=request, + result={"cleaning_job_id": cleaning_job_id}, + error=error, + started_at=now, + finished_at=now, + updated_at=now, + ) + session.add(task) + session.flush() + return task.id + + +def _persist_cleaned_rows( + session: Any, + *, + job_id: str, + extraction_batch_id: str | None, + output_rows: list[dict[str, Any]], +) -> None: + session.execute(delete(CleanedRow).where(CleanedRow.cleaning_job_id == job_id)) + for idx, row in enumerate(output_rows, start=1): + session.add( + CleanedRow( + cleaning_job_id=job_id, + extraction_batch_id=extraction_batch_id, + row_index=idx, + row_json=row, + ) + ) + + +def _run_rules_on_dataframe( + df: pd.DataFrame, + rules_per_column: dict[str, list[str]], + cop_mod_fecha_column: dict[str, str], + cop_mod_config: dict[str, Any] | None, +) -> tuple[list[dict[str, Any]], list[str]]: + result_df, log = apply_rules( + df, + rules_per_column, + cop_mod_fecha_column=cop_mod_fecha_column, + cop_mod_config=cop_mod_config, + ) + return dataframe_to_records(result_df), log + + +def _parse_json_form(raw: str | None, default: Any) -> Any: + if not raw or not raw.strip(): + return default + try: + return json.loads(raw) + except json.JSONDecodeError as exc: + raise HTTPException(status_code=400, detail=f"JSON inválido: {exc}") from exc + + +def _analysis_payload(df: pd.DataFrame, **extra: Any) -> dict[str, Any]: + return { + **extra, + "row_count": len(df), + "columns": [summary.__dict__ for summary in analyze_dataframe(df)], + "sample_rows": sample_dataframe_rows(df, 8), + } + + +def _cell_str(value: Any) -> str: + if value is None: + return "" + text = str(value) + if text in ("None", "nan", "NaT"): + return "" + if len(text) > 80: + return text[:77] + "..." + return text + + +def _preview_from_dataframe( + df: pd.DataFrame, + rules_per_column: dict[str, list[str]], + cop_mod_fecha_column: dict[str, str], + cop_mod_config: dict[str, Any] | None, +) -> dict[str, Any]: + rows_before = dataframe_to_records(df) + result_df, _log = apply_rules( + df, + rules_per_column, + cop_mod_fecha_column=cop_mod_fecha_column, + cop_mod_config=cop_mod_config, + ) + rows_after = dataframe_to_records(result_df) + + # Emparejar por índice de fila de muestra (misma posición) + n = min(len(rows_before), len(rows_after)) + all_cols = list(dict.fromkeys([*df.columns.astype(str), *result_df.columns.astype(str)])) + columns_preview: list[dict[str, Any]] = [] + for col in all_cols: + before_vals = [_cell_str(rows_before[i].get(col)) for i in range(n)] + after_vals = [_cell_str(rows_after[i].get(col)) for i in range(n)] + columns_preview.append({ + "name": col, + "before": before_vals, + "after": after_vals, + "changed": before_vals != after_vals, + }) + + return { + "rows_before": rows_before, + "rows_after": rows_after, + "columns": columns_preview, + } + + @app.get("/health") def health() -> dict[str, str]: return {"status": "ok"} @@ -90,18 +248,75 @@ def analyze_batch(batch_id: str) -> dict[str, Any]: records = [row[0] for row in rows] df = pd.DataFrame(records) - return { - "batch_id": batch_id, - "columns": [summary.__dict__ for summary in analyze_dataframe(df)], - } + return _analysis_payload(df, batch_id=batch_id) + + +@app.post("/analyze-csv") +async def analyze_csv(file: UploadFile = File(...)) -> dict[str, Any]: + content = await file.read() + if not content: + raise HTTPException(status_code=400, detail="El archivo CSV está vacío") + try: + df, delimiter = load_csv_bytes(content, file.filename or "upload.csv") + except Exception as exc: # noqa: BLE001 + raise HTTPException(status_code=400, detail=f"No se pudo leer el CSV: {exc}") from exc + if df.empty: + raise HTTPException(status_code=400, detail="El CSV no tiene filas") + return _analysis_payload(df, filename=file.filename, delimiter=delimiter) + + +@app.post("/preview") +def preview_cleaning(request: PreviewRequest) -> dict[str, Any]: + """Aplica reglas sobre una muestra pequeña y devuelve Antes/Después.""" + records: list[dict[str, Any]] = list(request.rows or []) + + if not records and request.extraction_batch_id: + with session_scope() as session: + batch = session.get(ExtractionBatch, request.extraction_batch_id) + if batch is None: + raise HTTPException(status_code=404, detail="Lote de extraccion no encontrado") + rows = session.execute( + select(ExtractionRow.row_json) + .where(ExtractionRow.batch_id == request.extraction_batch_id) + .order_by(ExtractionRow.row_index, ExtractionRow.id) + ).all() + all_records = [row[0] for row in rows] + if not all_records: + raise HTTPException(status_code=400, detail="El lote no tiene filas") + df_full = pd.DataFrame(all_records) + records = sample_dataframe_rows(df_full, max(1, min(request.sample_size, 20))) + + if not records: + raise HTTPException( + status_code=400, + detail="Se requieren rows o extraction_batch_id con filas", + ) + + df = pd.DataFrame(records) + try: + return _preview_from_dataframe( + df, + request.rules_per_column, + request.cop_mod_fecha_column, + request.cop_mod_config, + ) + except Exception as exc: # noqa: BLE001 + raise HTTPException(status_code=500, detail=str(exc)) from exc @app.post("/jobs") def run_cleaning(request: CleaningRequest) -> dict[str, Any]: + if not request.extraction_batch_id: + raise HTTPException( + status_code=400, + detail="extraction_batch_id es requerido (use /jobs/from-csv para CSV)", + ) + with session_scope() as session: batch = session.get(ExtractionBatch, request.extraction_batch_id) if batch is None: raise HTTPException(status_code=404, detail="Lote de extraccion no encontrado") + _validate_job_id(session, request.job_id) extracted_rows = session.execute( select(ExtractionRow.row_json) @@ -114,6 +329,8 @@ def run_cleaning(request: CleaningRequest) -> dict[str, Any]: job = CleaningJob( extraction_batch_id=request.extraction_batch_id, + job_id=request.job_id, + source="extraction", status="running", request=request.model_dump(), updated_at=utcnow(), @@ -121,36 +338,45 @@ def run_cleaning(request: CleaningRequest) -> dict[str, Any]: session.add(job) session.flush() job_id = job.id + batch_id = request.extraction_batch_id + desktop_job_id = request.job_id + request_dump = request.model_dump() try: df = pd.DataFrame(records) - result_df, log = apply_rules( + output_rows, log = _run_rules_on_dataframe( df, request.rules_per_column, - cop_mod_fecha_column=request.cop_mod_fecha_column, - cop_mod_config=request.cop_mod_config, + request.cop_mod_fecha_column, + request.cop_mod_config, ) - output_rows = dataframe_to_records(result_df) with session_scope() as session: - session.execute(delete(CleanedRow).where(CleanedRow.cleaning_job_id == job_id)) - for idx, row in enumerate(output_rows, start=1): - session.add( - CleanedRow( - cleaning_job_id=job_id, - extraction_batch_id=request.extraction_batch_id, - row_index=idx, - row_json=row, - ) - ) + _persist_cleaned_rows( + session, + job_id=job_id, + extraction_batch_id=batch_id, + output_rows=output_rows, + ) job = session.get(CleaningJob, job_id) if job is None: raise RuntimeError("No se encontro el job de limpieza recien creado.") job.status = "completed" job.log = log job.updated_at = utcnow() + _create_cleaning_task( + session, + job_id=desktop_job_id, + cleaning_job_id=job_id, + source="extraction", + status="completed", + request=request_dump, + title="Limpieza desde extracción", + ) session.flush() return serialize_job(job) + except HTTPException: + raise except Exception as exc: with session_scope() as session: job = session.get(CleaningJob, job_id) @@ -158,15 +384,127 @@ def run_cleaning(request: CleaningRequest) -> dict[str, Any]: job.status = "failed" job.error = str(exc) job.updated_at = utcnow() + _create_cleaning_task( + session, + job_id=desktop_job_id, + cleaning_job_id=job_id, + source="extraction", + status="failed", + request=request_dump, + error=str(exc), + title="Limpieza desde extracción", + ) + raise HTTPException(status_code=500, detail=str(exc)) from exc + + +@app.post("/jobs/from-csv") +async def run_cleaning_from_csv( + file: UploadFile = File(...), + job_id: str | None = Form(None), + rules_per_column: str | None = Form(None), + cop_mod_fecha_column: str | None = Form(None), + cop_mod_config: str | None = Form(None), +) -> dict[str, Any]: + content = await file.read() + if not content: + raise HTTPException(status_code=400, detail="El archivo CSV está vacío") + + rules = _parse_json_form(rules_per_column, {}) + fecha_map = _parse_json_form(cop_mod_fecha_column, {}) + cop_cfg = _parse_json_form(cop_mod_config, None) + if not isinstance(rules, dict): + raise HTTPException(status_code=400, detail="rules_per_column debe ser un objeto JSON") + if not isinstance(fecha_map, dict): + raise HTTPException(status_code=400, detail="cop_mod_fecha_column debe ser un objeto JSON") + + try: + df, delimiter = load_csv_bytes(content, file.filename or "upload.csv") + except Exception as exc: # noqa: BLE001 + raise HTTPException(status_code=400, detail=f"No se pudo leer el CSV: {exc}") from exc + if df.empty: + raise HTTPException(status_code=400, detail="El CSV no tiene filas") + + request_dump: dict[str, Any] = { + "source": "csv", + "filename": file.filename, + "delimiter": delimiter, + "job_id": job_id, + "rules_per_column": rules, + "cop_mod_fecha_column": fecha_map, + "cop_mod_config": cop_cfg, + } + + with session_scope() as session: + _validate_job_id(session, job_id) + job = CleaningJob( + extraction_batch_id=None, + job_id=job_id, + source="csv", + status="running", + request=request_dump, + updated_at=utcnow(), + ) + session.add(job) + session.flush() + cleaning_job_id = job.id + desktop_job_id = job_id + + try: + output_rows, log = _run_rules_on_dataframe(df, rules, fecha_map, cop_cfg) + + with session_scope() as session: + _persist_cleaned_rows( + session, + job_id=cleaning_job_id, + extraction_batch_id=None, + output_rows=output_rows, + ) + job = session.get(CleaningJob, cleaning_job_id) + if job is None: + raise RuntimeError("No se encontro el job de limpieza recien creado.") + job.status = "completed" + job.log = log + job.updated_at = utcnow() + _create_cleaning_task( + session, + job_id=desktop_job_id, + cleaning_job_id=cleaning_job_id, + source="csv", + status="completed", + request=request_dump, + title=f"Limpieza CSV — {file.filename or 'upload.csv'}", + ) + session.flush() + return serialize_job(job) + except HTTPException: + raise + except Exception as exc: + with session_scope() as session: + job = session.get(CleaningJob, cleaning_job_id) + if job is not None: + job.status = "failed" + job.error = str(exc) + job.updated_at = utcnow() + _create_cleaning_task( + session, + job_id=desktop_job_id, + cleaning_job_id=cleaning_job_id, + source="csv", + status="failed", + request=request_dump, + error=str(exc), + title=f"Limpieza CSV — {file.filename or 'upload.csv'}", + ) raise HTTPException(status_code=500, detail=str(exc)) from exc @app.get("/jobs") -def list_jobs() -> list[dict[str, Any]]: +def list_jobs(job_id: str | None = None) -> list[dict[str, Any]]: with session_scope() as session: - jobs = session.scalars( - select(CleaningJob).order_by(CleaningJob.created_at.desc()) - ).all() + stmt = select(CleaningJob).order_by(CleaningJob.created_at.desc()) + if job_id: + stmt = stmt.where(CleaningJob.job_id == job_id) + jobs = session.scalars(stmt).all() return [serialize_job(job) for job in jobs] diff --git a/services/cleaning/domain/csv_cleaning.py b/services/cleaning/domain/csv_cleaning.py deleted file mode 100644 index 8371708..0000000 --- a/services/cleaning/domain/csv_cleaning.py +++ /dev/null @@ -1,2130 +0,0 @@ -"""Limpieza de CSV con análisis inteligente local y reglas configurables. - -Este módulo es pura lógica (sin GUI, sin LLM). Provee: -- `analyze_dataframe` para obtener un resumen por columna (tipo inferido, - ejemplo aleatorio, nulos, únicos, estadísticas específicas por tipo). -- Un registro `CLEANING_RULES` con reglas aplicables a columnas. -- `apply_rules` que aplica las reglas seleccionadas por columna y devuelve - el DataFrame resultante junto con un log de operaciones/advertencias. -""" -from __future__ import annotations - -import csv as _csv -import logging -import math -import random -import re -import unicodedata -from collections import Counter -from dataclasses import dataclass, field -from datetime import date -from typing import Any, Callable, Literal, Optional - -import numpy as np -import pandas as pd - -logger = logging.getLogger(__name__) - - -# --------------------------------------------------------------------------- -# CUOTA MODERADORA (COP/MOD) -# --------------------------------------------------------------------------- - -DEFAULT_MODERADORA_BY_YEAR: dict[int, set[int]] = { - 2023: {4100, 16400, 43000}, - 2024: {4500, 18200, 47700}, - 2025: {4700, 19200, 50300}, - 2026: {5000, 20100, 52800}, -} - - -def normalize_moderadora_by_year_dict(raw: Any) -> dict[int, set[int]]: - """Normaliza un dict JSON año → lista de enteros (cuotas moderadora).""" - if not isinstance(raw, dict): - return {} - out: dict[int, set[int]] = {} - for k, v in raw.items(): - try: - yi = int(k) - except (TypeError, ValueError): - continue - if not isinstance(v, (list, tuple)): - continue - nums: set[int] = set() - for x in v: - try: - nums.add(int(round(float(x)))) - except (TypeError, ValueError): - continue - if nums: - out[yi] = nums - return out - - -def merge_moderadora_config( - cop_mod_config: Optional[dict[str, Any]], -) -> dict[int, set[int]]: - """Fusiona defaults con `moderadora_by_year` opcional del usuario.""" - merged = {y: set(vals) for y, vals in DEFAULT_MODERADORA_BY_YEAR.items()} - if cop_mod_config and isinstance(cop_mod_config.get("moderadora_by_year"), dict): - custom = normalize_moderadora_by_year_dict( - cop_mod_config["moderadora_by_year"] - ) - for y, vals in custom.items(): - merged[y] = set(vals) - return merged - - -def _alloc_cop_mod_column_names( - col_name: str, existing: set[str] -) -> tuple[str, str]: - """Nombres únicos para copago y moderadora.""" - base = re.sub(r"[^\w]+", "_", str(col_name).strip(), flags=re.ASCII) or "col" - for i in range(0, 200): - suf = f"_{i}" if i else "" - ccol, mcol = f"{base}{suf}_copago", f"{base}{suf}_moderadora" - if ccol not in existing and mcol not in existing: - return ccol, mcol - raise ValueError("No se pudieron asignar nombres de columnas únicos.") - - -def _amount_cell_to_optional_int( - val: Any, - decimal_hint: Optional[Literal[",", "."]], -) -> Optional[int]: - try: - f = _parse_lenient_one(val, decimal_hint) - except Exception: - return None - if f is None or (isinstance(f, float) and not np.isfinite(f)) or ( - isinstance(f, (float, np.floating)) and np.isnan(f) - ): - return None - try: - return int(round(float(f))) - except (TypeError, ValueError, OverflowError): - return None - - -def _apply_cop_mod_split( - result: pd.DataFrame, - amount_col: str, - fecha_column: str, - log: list[str], - *, - moderadora_by_year: dict[int, set[int]], -) -> None: - """Añade columnas copago y moderadora según cuotas por año de la fecha.""" - if fecha_column not in result.columns: - log.append( - f"[WARN] COP/MOD: columna de fecha '{fecha_column}' no existe; se omite." - ) - return - if amount_col not in result.columns: - log.append(f"[WARN] COP/MOD: columna de importe '{amount_col}' no existe; se omite.") - return - - fec_ser = result[fecha_column] - amt_ser = result[amount_col] - stripped = fec_ser.astype(str).str.replace(r"[\s\/\-\.]", "", regex=True) - stripped = stripped.where(fec_ser.notna(), other=pd.NA) - sep_fmt = detect_date_format(fec_ser) or detect_date_format(stripped) - compact8_fmt, compact6_fmt = _detect_compact_date_formats(fec_ser) - amt_hint = _infer_decimal_hint_from_column(amt_ser.astype(str)) - - ccol, mcol = _alloc_cop_mod_column_names(str(amount_col), set(result.columns)) - cop_vals: list[Any] = [] - mod_vals: list[Any] = [] - - for i in range(len(result)): - amt_raw = amt_ser.iloc[i] - fec_raw = fec_ser.iloc[i] - amt_i = _amount_cell_to_optional_int(amt_raw, amt_hint) - if amt_i is None: - cop_vals.append(0) - mod_vals.append(0) - continue - - parsed_d = _parse_any_date_to_tuple( - fec_raw, - sep_fmt=sep_fmt, - compact8_fmt=compact8_fmt, - compact6_fmt=compact6_fmt, - ) - if parsed_d is None: - cop_vals.append(amt_i) - mod_vals.append(0) - continue - _d, _m, year = parsed_d - tiers = moderadora_by_year.get(year) - if tiers is None: - cop_vals.append(amt_i) - mod_vals.append(0) - continue - - if amt_i in tiers: - cop_vals.append(0) - mod_vals.append(amt_i) - else: - cop_vals.append(amt_i) - mod_vals.append(0) - - result[ccol] = cop_vals - result[mcol] = mod_vals - log.append( - f"[OK] '{amount_col}' -> '{ccol}', '{mcol}' <- Separa COP/MOD " - f"(fecha: '{fecha_column}')" - ) - - -# --------------------------------------------------------------------------- -# MODELO DE DATOS -# --------------------------------------------------------------------------- - -TYPE_INTEGER = "integer" -TYPE_DECIMAL = "decimal" -TYPE_DATE = "date" -TYPE_BOOLEAN = "boolean" -TYPE_CATEGORICAL = "categorical" -TYPE_TEXT = "text" -TYPE_MIXED = "mixed" -TYPE_EMPTY = "empty" - - -@dataclass -class ColumnSummary: - """Resumen inteligente para una columna del DataFrame.""" - name: str - inferred_type: str - example: str = "" - null_count: int = 0 - null_pct: float = 0.0 - unique_count: int = 0 - unique_pct: float = 0.0 - min: Optional[float] = None - max: Optional[float] = None - mean: Optional[float] = None - avg_length: Optional[float] = None - top_values: list[tuple[str, int]] = field(default_factory=list) - detected_date_format: Optional[str] = None - applicable_rules: list[str] = field(default_factory=list) - - def info_text(self) -> str: - """Texto humano con información adicional (para subtítulo de la UI).""" - parts: list[str] = [] - parts.append(f"nulos: {self.null_count} ({self.null_pct:.0f}%)") - parts.append(f"únicos: {self.unique_count}") - if self.inferred_type in (TYPE_INTEGER, TYPE_DECIMAL): - if self.min is not None: - parts.append(f"min: {_fmt_num(self.min)}") - if self.max is not None: - parts.append(f"max: {_fmt_num(self.max)}") - if self.mean is not None: - parts.append(f"media: {_fmt_num(self.mean)}") - elif self.inferred_type == TYPE_DATE: - if self.detected_date_format: - parts.append(f"formato: {self.detected_date_format}") - elif self.inferred_type in (TYPE_TEXT, TYPE_CATEGORICAL): - if self.avg_length is not None: - parts.append(f"long. prom: {self.avg_length:.1f}") - if self.top_values: - tops = ", ".join(f'"{v}" ({c})' for v, c in self.top_values[:3]) - parts.append(f"top: {tops}") - return " · ".join(parts) - - -def _fmt_num(x: float) -> str: - if x is None: - return "" - if isinstance(x, float) and x.is_integer(): - return str(int(x)) - if isinstance(x, float): - return f"{x:.2f}" - return str(x) - - -# --------------------------------------------------------------------------- -# DETECCIÓN DE FECHAS -# --------------------------------------------------------------------------- - -_DATE_NUM_RE = re.compile(r"^\s*(\d{1,4})[\s\/\-\.](\d{1,2})[\s\/\-\.](\d{1,4})\s*$") -_DATE_COMPACT_RE = re.compile(r"^\s*(\d{6}|\d{8})\s*$") -# Hora opcional: HH[:MM[:SS]][ AM|PM] al final de la cadena -_TIME_TAIL_RE = re.compile( - r"[\sT]+(\d{1,2})(?::(\d{1,2})(?::(\d{1,2}))?)?\s*([APap]\.?\s*[Mm]\.?)?\s*$" -) - -_MONTH_NAME_TO_NUM = { - "enero": 1, - "ene": 1, - "febrero": 2, - "feb": 2, - "marzo": 3, - "mar": 3, - "abril": 4, - "abr": 4, - "mayo": 5, - "may": 5, - "junio": 6, - "jun": 6, - "julio": 7, - "jul": 7, - "agosto": 8, - "ago": 8, - "septiembre": 9, - "setiembre": 9, - "sep": 9, - "sept": 9, - "octubre": 10, - "oct": 10, - "noviembre": 11, - "nov": 11, - "diciembre": 12, - "dic": 12, -} - - -def _split_date_time(value: str) -> tuple[str, Optional[str]]: - """Separa la parte de fecha de la parte de hora si está presente. - - Devuelve `(date_part, time_part_normalized)`. `time_part_normalized` es - None si no hay hora. La hora se normaliza a `HH:MM[:SS][ AM|PM]`. - """ - if value is None: - return "", None - s = str(value).strip() - if not s: - return "", None - - m = _TIME_TAIL_RE.search(s) - if not m: - return s, None - - hh = int(m.group(1)) - mm = int(m.group(2)) if m.group(2) else 0 - ss = m.group(3) - ampm = m.group(4) - if not (0 <= hh <= 23 and 0 <= mm <= 59): - return s, None - if ss is not None: - try: - ssi = int(ss) - if not (0 <= ssi <= 59): - return s, None - except ValueError: - return s, None - # Validar contra AM/PM (con AM/PM, hora 1-12) - if ampm and not (1 <= hh <= 12): - return s, None - - # Quitar separadores habituales entre fecha y hora (p. ej. "12/27/24, 12:00 AM"). - date_part = s[: m.start()].rstrip(" \t,;T") - if not date_part: - return s, None - - parts: list[str] = [f"{hh:02d}:{mm:02d}"] - if ss is not None: - parts[0] += f":{int(ss):02d}" - if ampm: - norm_ampm = ampm.replace(".", "").replace(" ", "").upper() - if norm_ampm in ("AM", "PM"): - parts.append(norm_ampm) - time_norm = " ".join(parts) - return date_part, time_norm - - -def _extract_date_parts(value: str) -> Optional[tuple[int, int, int, str]]: - """Intenta extraer (a, b, c, layout) de una fecha en cualquier formato común. - - `layout` es uno de: 'sep' (con separadores), 'compact8' (YYYYMMDD o - DDMMYYYY), 'compact6' (DDMMYY o YYMMDD). No interpreta todavía qué - componente es día/mes/año; eso lo decide `detect_date_format` viendo la - columna completa. Acepta valores con hora al final (ignorada para los - componentes de fecha). - """ - if value is None: - return None - s = str(value).strip() - if not s or s.lower() in ("nan", "none", "nat"): - return None - - date_part, _ = _split_date_time(s) - if date_part: - s = date_part - - m = _DATE_NUM_RE.match(s) - if m: - try: - a, b, c = int(m.group(1)), int(m.group(2)), int(m.group(3)) - return (a, b, c, "sep") - except ValueError: - return None - - m2 = _DATE_COMPACT_RE.match(s) - if m2: - digits = m2.group(1) - if len(digits) == 8: - return (int(digits[0:2]), int(digits[2:4]), int(digits[4:8]), "compact8") - if len(digits) == 6: - return (int(digits[0:2]), int(digits[2:4]), int(digits[4:6]), "compact6") - - return None - - -def detect_date_format(series: pd.Series, sample_size: int = 500) -> Optional[str]: - """Analiza la serie y determina el formato de fecha dominante. - - Devuelve un string con el layout detectado (p. ej. "DD/MM/YYYY", - "MM/DD/YYYY", "YYYYMMDD", "DDMMYYYY") o None si no parece fechas. - """ - if series is None or len(series) == 0: - return None - - non_null = series.dropna() - if non_null.empty: - return None - - if len(non_null) > sample_size: - non_null = non_null.sample(sample_size, random_state=42) - - layouts = {"sep": 0, "compact8": 0, "compact6": 0} - sep_first_gt12 = 0 # primer componente > 12 → probable día - sep_second_gt12 = 0 # segundo componente > 12 → probable mes solo posible si MM/DD - sep_third_4digits = 0 # tercer componente de 4 dígitos → año al final - sep_first_4digits = 0 # primer componente de 4 dígitos → año al inicio - compact8_year_first = 0 # primeros 4 dígitos > 1900 → YYYYMMDD - compact8_year_last = 0 # últimos 4 dígitos > 1900 → DDMMYYYY - total_parsed = 0 - - for val in non_null: - parsed = _extract_date_parts(val) - if parsed is None: - continue - total_parsed += 1 - a, b, c, layout = parsed - layouts[layout] = layouts.get(layout, 0) + 1 - - if layout == "sep": - # año puede estar al inicio o al final según cantidad de dígitos - if _digit_count(a) == 4: - sep_first_4digits += 1 - if _digit_count(c) == 4: - sep_third_4digits += 1 - if a > 12: - sep_first_gt12 += 1 - if b > 12: - sep_second_gt12 += 1 - elif layout == "compact8": - # a son los primeros 2 dígitos, c son los últimos 4 - if c >= 1900: - compact8_year_last += 1 - # a concatenado con b son los primeros 4 dígitos - first4 = a * 100 + b - if first4 >= 1900: - compact8_year_first += 1 - - if total_parsed == 0: - return None - - # Requerimos que >=70% de los no nulos parseen como fecha para considerarla - # columna de fechas. - if total_parsed / len(non_null) < 0.7: - return None - - dominant = max(layouts, key=lambda k: layouts[k]) - - if dominant == "compact8": - if compact8_year_first > compact8_year_last: - return "YYYYMMDD" - return "DDMMYYYY" - if dominant == "compact6": - return "DDMMYY" - - # Caso con separadores - if sep_first_4digits > sep_third_4digits: - return "YYYY/MM/DD" - # año al final - if sep_first_gt12 > 0 and sep_second_gt12 == 0: - return "DD/MM/YYYY" - if sep_second_gt12 > 0 and sep_first_gt12 == 0: - return "MM/DD/YYYY" - # ambiguo → preferencia hispana - return "DD/MM/YYYY" - - -def _digit_count(n: int) -> int: - return len(str(abs(n))) - - -def _parse_date_with_format(value: str, fmt: str) -> Optional[tuple[int, int, int]]: - """Convierte un valor de fecha a (día, mes, año) según el layout detectado.""" - parsed = _extract_date_parts(value) - if parsed is None: - return None - a, b, c, layout = parsed - - if fmt == "DD/MM/YYYY": - d, m, y = a, b, c - elif fmt == "MM/DD/YYYY": - m, d, y = a, b, c - elif fmt == "YYYY/MM/DD": - y, m, d = a, b, c - elif fmt == "YYYYMMDD" and layout == "compact8": - # reconstruir: primer4 = a*100 + b, luego mes y día - y = a * 100 + b - m = c // 100 - d = c % 100 - elif fmt == "DDMMYYYY" and layout == "compact8": - d = a - m = b - y = c - elif fmt == "DDMMYY" and layout == "compact6": - d = a - m = b - y = 2000 + c if c < 50 else 1900 + c - else: - # intentar como sep genérico - d, m, y = a, b, c - - if not (1 <= m <= 12 and 1 <= d <= 31 and 1000 <= y <= 9999): - # año en 2 dígitos - if y < 100: - y = 2000 + y if y < 50 else 1900 + y - if not (1 <= m <= 12 and 1 <= d <= 31 and 1000 <= y <= 9999): - return None - return (d, m, y) - - -def _normalize_date_text(value: Any) -> str: - text = unicodedata.normalize("NFKD", str(value or "")) - text = "".join(c for c in text if not unicodedata.combining(c)) - return text.lower() - - -def _valid_date_tuple(d: int, m: int, y: int) -> Optional[tuple[int, int, int]]: - if y < 100: - y = 2000 + y if y < 50 else 1900 + y - if not (1000 <= y <= 9999): - return None - try: - date(y, m, d) - except ValueError: - return None - return (d, m, y) - - -def _split_glued_year_suffix_hhmm(digits: str) -> Optional[tuple[int, int, int]]: - """Detecta `YYYY` + `HHMM` en una sola tira de 8 cifras (p. ej. `20241344`). - - Tras quitar separadores puede quedar pegado al mes (p. ej. `15may20241344`). - Devuelve (año, hora, minuto) si la parte horaria es válida; si no, None. - """ - if len(digits) != 8 or not digits.isdigit(): - return None - y = int(digits[:4]) - hh = int(digits[4:6]) - mm = int(digits[6:8]) - if not (1000 <= y <= 9999 and 0 <= hh <= 23 and 0 <= mm <= 59): - return None - return (y, hh, mm) - - -def _parse_text_month_candidate( - first: tuple[int, str], - month: int, - second: tuple[int, str], - *, - layout: Literal["around", "after", "before"], -) -> Optional[tuple[int, int, int]]: - a, astr = first - b, bstr = second - if layout == "around": - if len(astr) == 4: - return _valid_date_tuple(b, month, a) - if len(bstr) == 4: - return _valid_date_tuple(a, month, b) - return _valid_date_tuple(a, month, b) - if layout == "after": - if len(astr) == 4: - return _valid_date_tuple(b, month, a) - return _valid_date_tuple(a, month, b) - if len(astr) == 4: - return _valid_date_tuple(b, month, a) - return _valid_date_tuple(a, month, b) - - -def _parse_date_with_text_month(raw: Any) -> Optional[tuple[int, int, int]]: - if _is_nullish_scalar(raw): - return None - date_part, _ = _split_date_time(str(raw)) - normalized = _normalize_date_text(date_part or raw) - # `\d{8}` antes que `\d{1,4}` para no partir año+hora pegados tras strip agresivo. - tokens = re.findall(r"[a-z]+|\d{8}|\d{1,4}", normalized) - if not tokens: - return None - - for i, token in enumerate(tokens): - month = _MONTH_NAME_TO_NUM.get(token) - if month is None: - continue - nums_before = [ - (int(t), t) for t in tokens[:i] if t.isdigit() - ] - nums_after = [ - (int(t), t) for t in tokens[i + 1 :] if t.isdigit() - ] - candidates: list[Optional[tuple[int, int, int]]] = [] - if nums_before and len(nums_after) == 1: - glued = _split_glued_year_suffix_hhmm(nums_after[0][1]) - if glued is not None: - y, _hh, _mm = glued - candidates.append( - _parse_text_month_candidate( - nums_before[-1], month, (y, str(y)), layout="around" - ) - ) - if nums_before and nums_after: - candidates.append( - _parse_text_month_candidate( - nums_before[-1], month, nums_after[0], layout="around" - ) - ) - if len(nums_after) >= 2: - candidates.append( - _parse_text_month_candidate( - nums_after[0], month, nums_after[1], layout="after" - ) - ) - if len(nums_before) >= 2: - candidates.append( - _parse_text_month_candidate( - nums_before[-2], month, nums_before[-1], layout="before" - ) - ) - for parsed in candidates: - if parsed is not None: - return parsed - return None - - -# --------------------------------------------------------------------------- -# ANÁLISIS DE COLUMNAS -# --------------------------------------------------------------------------- - -_BOOLEAN_VALUES = {"true", "false", "si", "sí", "no", "1", "0", "yes"} - - -# PARSING numérico tolerante (miles/decimal) sin fijar un solo locale -_HINT_MAJORITY = 0.6 -_MIN_HINT_VOTES = 1 - - -def _is_nullish_scalar(x: Any) -> bool: - if x is None or x is pd.NA: - return True - if isinstance(x, (bool, np.bool_)): - return False - if isinstance(x, (int, float, np.integer, np.floating)) and not isinstance( - x, (bool, np.bool_) - ): - if isinstance(x, float) and (np.isnan(x) or pd.isna(x)): - return True - return False - t = str(x).strip().lower() - if not t or t in ("nan", "none", "nat", "null", ""): - return True - return False - - -def _strip_currency(s: str) -> str: - t = s.replace("\u00a0", " ").replace("\u202f", " ") - t = re.sub(r"[\s]+", "", t) - t = t.strip() - if not t or t.lower() in ("nan", "none", "nat"): - return "" - neg = False - if t[0:1] == "-": - neg, t = not neg, t[1:].lstrip() - if t[0:1] == "(" and t.endswith(")"): - neg, t = not neg, t[1:-1] - t = re.sub( - r"^[\$€\u00a3\u0e3f\uffe5\uffe6\uff04\ufe69]+", - "", - t, - count=1, - ) - t = re.sub(r"[\$€%]+$", "", t) - if not t and neg: - return "-0" - return ("-" if neg else "") + t - - -def _extract_numeric_chunk(s: str) -> str: - t = s.strip() - t = t.replace("$", "").replace("€", "").replace("\u00a3", "") - t = t.replace(" ", "") - cands1 = re.findall( - r"[+\-]?(?:\d{1,3}(?:[.,]\d{3})*|\d+)(?:[.,]\d+)?", t - ) - cands2 = re.findall( - r"[+\-]?\d[.,\d ]*\d", t - ) - cands = cands1 + cands2 - if not cands: - m = re.search(r"[\d.,\-]+", t) - if m: - c = m.group(0).replace("(", "-").replace(")", "") - return c.lstrip("+") - return "" - best = max(cands, key=len) if cands else "" - best = best.replace(" ", "").replace("(", "-").replace(")", "") - if not re.search(r"\d", best): - return "" - return best.lstrip("+") - - -def _as_float_s(s: str) -> Optional[float]: - if not s: - return None - try: - f = float(s) - if not np.isfinite(f) or abs(f) > 1e100: - return None - return f - except (ValueError, TypeError, OverflowError): - return None - - -def _parse_both_separators(body: str) -> Optional[float]: - """El separador más a la DERECHA entre `,` y `.` es el decimal; el otro, miles.""" - if "," not in body or "." not in body: - return None - t = body.strip() - if re.search(r"[^0-9,.\-]", t): - t2 = re.sub(r"[^0-9,.\-]", "", t) - t = t2 - if not t: - return None - neg = t[0:1] == "-" - b = t[1:].lstrip() if neg else t - if not b: - return None - i_comma, i_dot = b.rfind(","), b.rfind(".") - if min(i_comma, i_dot) < 0: - return None - dpos = max(i_comma, i_dot) - th = "," if b[dpos] == "." else "." - intp, fr = b[:dpos], b[dpos + 1 :] - if not re.fullmatch(r"\d{1,4}$", fr): - return None - intc = intp.replace(th, "") - if not intc.isdigit(): - return None - v = _as_float_s(f"{intc}.{fr}") - if v is None: - return None - if neg and v and v is not None: - return -abs(v) - return v - - -def _only_commas(w: str, dec_hint: Optional[Literal[",", "."]]) -> Optional[float]: - neg = w[0:1] == "-" - wn = w[1:].lstrip() if neg else w - if wn and re.search(r"[^0-9,]", wn): - wn = re.sub(r"[^0-9,]", "", wn) - n = wn.count(",") - if n == 0 and wn.isdigit(): - v = float(wn) - return -v if neg else v - if n == 0 or "." in wn: - return None - if n == 1: - i = wn.rfind(",") - h, t = wn[:i], wn[i + 1 :] - if not t.isdigit() or not h.isdigit(): - return None - if len(t) in (1, 2): - v = _as_float_s(f"{h}.{t}") - if v is None: - return None - return -v if neg else v - if len(t) == 3: - if dec_hint is None or dec_hint == ",": - v = _as_float_s(f"{h}{t}") - if v is None: - return None - return -v if neg else v - if dec_hint == ".": - v = _as_float_s(wn.replace(",", "")) - if v is None: - return None - return -v if neg else v - v = _as_float_s(wn.replace(",", "")) - if v is None: - return None - return -v if neg else v - - -def _only_dots(w: str, dec_hint: Optional[Literal[",", "."]]) -> Optional[float]: - neg = w[0:1] == "-" - wn = w[1:].lstrip() if neg else w - if wn and re.search(r"[^0-9.]", wn): - wn = re.sub(r"[^0-9.]", "", wn) - n = wn.count(".") - if n == 0 and wn.isdigit(): - v = float(wn) - return -v if neg else v - if n == 0 or "," in wn: - return None - if n == 1: - i = wn.rfind(".") - h, t = wn[:i], wn[i + 1 :] - if not t.isdigit() or not h.isdigit(): - return None - if len(t) in (1, 2): - v = _as_float_s(f"{h}.{t}") - if v is None: - return None - return -v if neg else v - if len(t) == 3: - if dec_hint is None or dec_hint == ".": - v = _as_float_s(f"{h}{t}") - if v is None: - return None - return -v if neg else v - if dec_hint == ",": - v = _as_float_s(wn.replace(".", "")) - if v is None: - return None - return -v if neg else v - v = _as_float_s(wn.replace(".", "")) - if v is None: - return None - return -v if neg else v - - -def _parse_lenient_one( - raw: Any, - decimal_hint: Optional[Literal[",", "."]] = None, -) -> float: - if _is_nullish_scalar(raw): - return float("nan") - t = _strip_currency(str(raw)) - if not t or t == "-": - return float("nan") - c = _extract_numeric_chunk(t) or t - c = c.strip() - if not c or c == "-": - return float("nan") - n_comma, n_dot = c.count(","), c.count(".") - if n_comma and n_dot: - f = _parse_both_separators(c) - elif n_comma and not n_dot: - f = _only_commas(c, decimal_hint) - elif n_dot and not n_comma: - f = _only_dots(c, decimal_hint) - else: - c2 = re.sub(r"[^0-9.\-]", "", c) - f = _as_float_s(c2) if c2 and re.fullmatch(r"-?\d+$", c2) else None - if f is not None and np.isfinite(f): - return f - return float("nan") - - -def _infer_decimal_hint_from_column( - series: pd.Series, -) -> Optional[Literal[",", "."]]: - """Voto con celdas que traen `,` y `.`: gana el de más a la derecha = decimal.""" - votes: list[Literal[",", "."]] = [] - for raw in series.dropna(): - if _is_nullish_scalar(raw): - continue - t = _strip_currency(str(raw)) - c = _extract_numeric_chunk(t) or t - c = c.strip() - if "," not in c or "." not in c or _parse_both_separators(c) is None: - continue - if c.rfind(",") > c.rfind("."): - votes.append(",") - else: - votes.append(".") - n = len(votes) - if n < _MIN_HINT_VOTES: - return None - a = sum(1 for v in votes if v == ",") - b = sum(1 for v in votes if v == ".") - if a + b == 0: - return None - if a / n >= _HINT_MAJORITY and a > b: - return "," - if b / n >= _HINT_MAJORITY and b > a: - return "." - return None - - -def _to_numeric_lenient( - series: pd.Series, - decimal_hint: Optional[Literal[",", "."]] = None, -) -> pd.Series: - """Convierte a flotante; pista de columna por voto (si `decimal_hint` es None).""" - hint = decimal_hint if decimal_hint is not None else _infer_decimal_hint_from_column( - series - ) - - def _cell(x: Any) -> float: - if _is_nullish_scalar(x): - return float("nan") - return _parse_lenient_one(x, hint) - - return series.map(_cell) - - -def _column_name_suggests_date_like(name: str) -> bool: - """Columnas cuyo nombre pide tratar el contenido como fecha a priori.""" - n = re.sub(r"[_\s.]+", " ", (name or "")).lower().strip() - if not n: - return False - if re.search( - r"\b(fecha|date|año|mes|dia|día|f[_-]?(alta|baja|proc|corte|pago|ini|fin|" - r"desde|hasta|vigenc|vence|venc|caduc|f\s?ec)\b|periodo|anio|ano|expir)", - n, - ) or n.startswith("fec_") or n.startswith("fecha"): - return True - return False - - -def _column_name_suggests_monetary_value(name: str) -> bool: - """importe, valor, neto, IVA, etc. — no interpretar cifras como fechas. - - Encabezados de tipo `fecha_…` no se tratan como moneda salvo mezcla con monto/valor. - """ - n = re.sub(r"[_\s.]+", " ", (name or "")).lower().strip() - if not n: - return False - if _column_name_suggests_date_like(name) and not re.search( - r"\b(monto|valor|importe|pago|total|bruto|neto|import|unit|precio|copago)\b", n - ): - return False - for key in ( - "monto", "importe", "import", "bruto", "neto", "subtotal", "sub total", - "unitario", "unidad", "valor", "precio", "pago", "copago", "sald", "dcto", - "franq", "cuota", "iva", "tarif", "costo", "carg", "reten", "retencion", - ): - if key in n: - if key == "iva" and re.search(r"(priva|giva|diva|viva|cliva)\b", n): - continue - if key == "carg" and re.search(r"descarg|recarg|abord", n): - continue - return True - return False - - -def _column_name_suggests_code_id(name: str) -> bool: - """Código, ref., radicado, tipoynúm de documento — no forzar 6-8 cifras como fechas (DDMMYY, etc.).""" - n = re.sub(r"[_\s.]+", " ", (name or "")).lower().strip() - if not n or _column_name_suggests_date_like(name): - return False - for key in ( - "codigo", "código", "codig", "códig", "cód_", - "sku", "ean", "upc", "isbn", "upn", - "ref", "referen", "radic", "nro_fac", "nro fac", "nit", "cédul", "cedul", - "autoriz", "correlat", "secuenc", "línea de", "linea de", "cupo", - "tipy", "tipi", "tipynum", "typnum", "type_n", "n_doc", "n_docu", - "núm doc", "n_docu", "doc_id", "docum", "id_doc", - ): - if key in n: - return True - if re.search( - r"tipo[_\s\\-]*(&|/|u|e|y)[_\s\\-]*num|tipo[_\s\\-]+doc|tipo_y_n", - n, - re.IGNORECASE, - ) or n.startswith("tip_"): - return True - if n.startswith("cod_") or n.startswith("cód_") or n.startswith("ref_"): - return True - return bool(re.search(r"\b(nro[._]fac|#?\s*autor|#?\s*ref)\b", n, re.IGNORECASE)) - - -def _column_name_suggests_quantity_or_number_not_bool(name: str) -> bool: - """Nombres típicos de cantidad, importe, fechas: no inferir bool por 0/1.""" - n = re.sub(r"[_\s.]+", " ", (name or "")).lower().strip() - if not n: - return False - if _column_name_suggests_monetary_value(name) or _column_name_suggests_code_id(name): - return True - for key in ( - "fecha", - "monto", - "importe", - "bruto", - "neto", - "subtotal", - "unitario", - "unidad", - "valor", - "precio", - "pago", - "copago", - "cantid", - "quantity", - "qty", - "nro", - "núm", - "n°", - ): - if key in n: - return True - return False - - -def _raw_strings_suggest_monetary_presentation(non_null: pd.Series) -> bool: - """$ €, miles 1,234,12 o .00: presentación monetaria aunque el float sea "entero".""" - if non_null is None or len(non_null) == 0: - return False - n_hit = 0 - n_seen = 0 - for raw in non_null.head(500): - if _is_nullish_scalar(raw): - continue - t = str(raw) - s = t.strip() - n_seen += 1 - if re.search(r"[$\u00a3\u20ac\uffe5\uffe6\ufe69\uff04]", t): - n_hit += 1 - continue - if re.search(r"\b\d{1,3}(?:[.,\u00a0]\d{3})+\b", s) or re.search( - r"(?= 0.10 - - -def _cell_looks_cifra_con_moneda_iso_al_final(s: str) -> bool: - """Solo cifra/símbolo (no prefijo de documento) y código de moneda al final. - - Excluye filas al estilo `CC12…` (típico alfanumérico). - """ - t = str(s).strip() - u = t.upper() - if not re.search( - r"(?i)(USD|COP|EUR|CLP|ARS|GBP|MXN|PEN|DOP|UYU|VES|CRC|PYG|BRL|BOB|GTQ)\b\s*\Z", u - ): - return False - if re.match( - r"^(?i)(CC|CE|NIT|NUIP|TI|PA|MS|RC|DNI|NUIP|CN|SC|AS|PE|PT)\D*[\d0]", u - ): - return False - return re.match( - r"^[\$€0-9\s,.\-+%/]", t, - ) is not None - - -def _raw_strings_suggest_alnum_mixed_presentation(non_null: pd.Series) -> bool: - """Letras y dígitos juntos (p. ej. `CC32…`, `A1-2B`); no es puro 'entero/decimal' lógicamente. - - Excluido: columnas con apariencia monetaria, y celdas solo cifra + ISO al final (100 USD). - """ - if _raw_strings_suggest_monetary_presentation(non_null): - return False - n_m = 0 - n_s = 0 - for raw in non_null.head(500): - if _is_nullish_scalar(raw): - continue - t = str(raw).strip() - n_s += 1 - if not re.search(r"\d", t) or not re.search( - r"[A-Za-zÑñ\u00c0-\u024f]", t, re.IGNORECASE - ): - continue - if _cell_looks_cifra_con_moneda_iso_al_final(t): - continue - n_m += 1 - if n_s == 0: - return False - return n_m / n_s >= 0.20 - - -def _cell_looks_like_documento_id_tipo_nit(s: str) -> bool: - """Cadenas estilo `CC32324029`, `CE 1.234.567` — no son fechas (evita 8 cifras como DDMM). - - Al menos una letra al inicio y 5-16 dígitos en la celda (no `Jan`+`2020` = 4 dígitos). - """ - t = str(s).strip() - if not t or len(t) < 3: - return False - if not re.match(r"^[A-Za-z]{1,3}", t): - return False - dig = re.sub(r"[^\d]", "", t) - if len(dig) < 5 or len(dig) > 16: - return False - return True - - -def _content_looks_like_doc_id_letter_prefix_column(non_null: pd.Series) -> bool: - """Muestras con prefijo documental (CC, NIT, …) y ≥5 dígitos → no columna de fecha.""" - n_ok = 0 - n_seen = 0 - for raw in non_null.head(500): - if _is_nullish_scalar(raw): - continue - s = str(raw).strip() - n_seen += 1 - if _cell_looks_like_documento_id_tipo_nit(s): - n_ok += 1 - if n_seen == 0: - return False - return n_ok / n_seen >= 0.20 - - -def _detect_messy_text_date_format_guess(non_null: pd.Series) -> Optional[str]: - """Fechas con ruido: compacta a dígitos; si hay consenso, reutiliza el detector. - (Depende de `_normalize_messy_date_to_compact`, definida más abajo en el módulo.) - """ - # resolución en tiempo de ejecución - norm = non_null.map(_normalize_messy_date_to_compact) # type: ignore[name-defined] - compacts: list[str] = [] - for c in norm: - if isinstance(c, str) and len(c) in (6, 8): - compacts.append(c) - n = max(len(non_null), 1) - if not compacts or len(compacts) < max(2, int(0.25 * n)): - return None - s_compact = pd.Series(compacts, dtype=object) - return detect_date_format(s_compact) - - -def _infer_column_type( - series: pd.Series, column_name: str = "" -) -> tuple[str, Optional[str]]: - """Devuelve (tipo, formato_fecha detectado; solo si aplica a tipo fecha).""" - non_null = series.dropna().astype(str).str.strip() - non_null = non_null[non_null != ""] - if non_null.empty: - return TYPE_EMPTY, None - - cname = str(column_name or "") - - # Boolean: no aplicar 0/1/ sí-no a columnas cuyo nombre pide importe, cantid., fecha… - if not _column_name_suggests_quantity_or_number_not_bool(cname): - lowered = non_null.str.lower() - if lowered.isin(_BOOLEAN_VALUES).all() and lowered.nunique() <= 3: - return TYPE_BOOLEAN, None - - is_money = _column_name_suggests_monetary_value(cname) - is_code = _column_name_suggests_code_id(cname) - is_doc_pattern = _content_looks_like_doc_id_letter_prefix_column(non_null) - - # Fecha: no forzar en importe/código, ni con datos tipo `CC`+cifra (6–8 dígitos sueltos) - if not is_money and not is_code and not is_doc_pattern: - date_fmt = detect_date_format(non_null) - if date_fmt: - return (TYPE_DATE, date_fmt) - messy_date_fmt = _detect_messy_text_date_format_guess(non_null) - if messy_date_fmt: - return (TYPE_DATE, messy_date_fmt) - - numeric_candidate = _to_numeric_lenient(non_null, decimal_hint=None) - numeric_ratio = numeric_candidate.notna().mean() - if numeric_ratio >= 0.9: - if _raw_strings_suggest_alnum_mixed_presentation(non_null): - return (TYPE_MIXED, None) - is_all_integer = (numeric_candidate.dropna() % 1 == 0).all() - if is_all_integer and not _raw_strings_suggest_monetary_presentation(non_null): - return (TYPE_INTEGER, None) - return (TYPE_DECIMAL, None) - - unique_ratio = non_null.nunique() / len(non_null) - if non_null.nunique() <= 15 and unique_ratio < 0.3: - return TYPE_CATEGORICAL, None - if 0.1 < numeric_ratio < 0.9: - return TYPE_MIXED, None - return TYPE_TEXT, None - - -def _applicable_rules_for_type(inferred_type: str) -> list[str]: - """Identificadores de reglas a mostrar según el tipo inferido (sin redundancias).""" - base = ["strip_special_spaces", "strip_special_keep_date_seps", "strip_spaces"] - if inferred_type == TYPE_INTEGER: - return base + ["cop_mod_split"] - if inferred_type == TYPE_DECIMAL: - return base + [ - "to_integer", - "round_decimals_custom", - "doc_id_split_tipo_numero", - "doc_type_homologate", - "cop_mod_split", - ] - if inferred_type == TYPE_DATE: - return base + [ - "doc_id_split_tipo_numero", - "doc_type_homologate", - "date_to_ddmmyyyy", - "nota_paciente_autorizacion", - ] - if inferred_type == TYPE_MIXED: - return base + [ - "to_integer", - "round_decimals_custom", - "doc_id_split_tipo_numero", - "doc_type_homologate", - "date_to_ddmmyyyy", - "cop_mod_split", - "nota_paciente_autorizacion", - ] - # Texto / categórico / boolean / empty → todas las de limpieza de texto + las - # conversoras (el usuario sabrá si aplica). - return base + [ - "to_integer", - "round_decimals_custom", - "doc_id_split_tipo_numero", - "doc_type_homologate", - "date_to_ddmmyyyy", - "cop_mod_split", - "nota_paciente_autorizacion", - ] - - -applicable_rule_ids_for_type = _applicable_rules_for_type - - -def analyze_dataframe(df: pd.DataFrame) -> list[ColumnSummary]: - """Genera un resumen por columna del DataFrame.""" - summaries: list[ColumnSummary] = [] - total_rows = len(df) - rng = random.Random(42) - - for col in df.columns: - series = df[col] - null_count = int(series.isna().sum() + (series.astype(str).str.strip() == "").sum()) - # Evitar doble conteo: considerar vacío solo si no-NaN - null_count = int((series.isna() | (series.astype(str).str.strip() == "")).sum()) - null_pct = (null_count / total_rows * 100) if total_rows else 0.0 - - non_null = series.dropna().astype(str) - non_null = non_null[non_null.str.strip() != ""] - - example = "" - if not non_null.empty: - example = str(rng.choice(non_null.tolist())) - if len(example) > 80: - example = example[:77] + "..." - - unique_count = int(non_null.nunique()) - unique_pct = (unique_count / total_rows * 100) if total_rows else 0.0 - - inferred_type, date_fmt = _infer_column_type(series, str(col)) - - summary = ColumnSummary( - name=str(col), - inferred_type=inferred_type, - example=example, - null_count=null_count, - null_pct=null_pct, - unique_count=unique_count, - unique_pct=unique_pct, - detected_date_format=date_fmt, - ) - - if inferred_type in (TYPE_INTEGER, TYPE_DECIMAL): - numeric = _to_numeric_lenient(non_null) - if numeric.notna().any(): - summary.min = float(numeric.min()) - summary.max = float(numeric.max()) - summary.mean = float(numeric.mean()) - elif inferred_type in (TYPE_TEXT, TYPE_CATEGORICAL): - if not non_null.empty: - summary.avg_length = float(non_null.str.len().mean()) - counts = non_null.value_counts().head(3) - summary.top_values = [(str(v), int(c)) for v, c in counts.items()] - - summary.applicable_rules = _applicable_rules_for_type(inferred_type) - summaries.append(summary) - - return summaries - - -# --------------------------------------------------------------------------- -# REGLAS DE LIMPIEZA -# --------------------------------------------------------------------------- - - -def _rule_strip_special_spaces(series: pd.Series) -> pd.Series: - """Elimina todo lo que no sea alfanumérico (incluye espacios).""" - pattern = re.compile(r"[^A-Za-z0-9]", flags=re.UNICODE) - - def _fn(x): - if pd.isna(x): - return x - return pattern.sub("", str(x)) - - return series.map(_fn) - - -def _rule_strip_special_keep_date_seps(series: pd.Series) -> pd.Series: - """Conserva alfanuméricos, separadores de fecha '/', '-' y hora ':', y espacio.""" - pattern = re.compile(r"[^A-Za-z0-9\/\-: ]", flags=re.UNICODE) - - def _fn(x): - if pd.isna(x): - return x - return pattern.sub("", str(x)) - - return series.map(_fn) - - -def _rule_strip_spaces(series: pd.Series) -> pd.Series: - """Elimina únicamente espacios (incluidos tabs y saltos de línea).""" - def _fn(x): - if pd.isna(x): - return x - return re.sub(r"\s+", "", str(x)) - - return series.map(_fn) - - -def _rule_to_integer(series: pd.Series) -> pd.Series: - """Convierte a entero (Int64 nullable) con parser tolerante.""" - numeric = _to_numeric_lenient(series.astype(str)) - return numeric.round().astype("Int64") - - -def _round_to_int_half_tie_le(x: float) -> int: - """Fracc. > 0,5 hacia arriba; 0,5 o menos hacia abajo. Banda 1e-9 en 0,5 (float). - - Misma noción que en la descripción de la regla. Trabaja sobre |x| y - recompone el signo. - """ - sign = -1 if x < 0 else 1 - if x == 0.0 or x == -0.0: - return 0 - ax = abs(float(x)) - ip = math.trunc(ax) - frac = ax - ip - if frac < 0.5 - 1e-9: - out = ip - elif frac > 0.5 + 1e-9: - out = ip + 1 - else: - out = ip - return -out if sign < 0 else out - - -def _rule_round_decimals_custom(series: pd.Series) -> pd.Series: - """Mismo parser tolerante que convertir a entero; aprox. con fracc. >0,5 arriba.""" - numeric = _to_numeric_lenient(series.astype(str)) - - def _fn(x): - if _is_nullish_scalar(x) or (isinstance(x, (float, np.floating)) and (not np.isfinite(x) or np.isnan(x))): - return pd.NA - try: - f = float(x) - except (TypeError, ValueError, OverflowError): - return pd.NA - if not np.isfinite(f): - return pd.NA - if f == 0.0 or f == -0.0: - return 0 - return _round_to_int_half_tie_le(f) - - return numeric.map(_fn).astype("Int64") - - -def _rule_doc_type_homologate(series: pd.Series) -> pd.Series: - """Homologa una columna que contiene tipos de documento.""" - def _fn(x: Any): - if _is_nullish_scalar(x): - return x - tipo, _num = split_doc_tipo_y_numero_texto(x, homologate=True) - if tipo is not None: - return tipo - return homologate_doc_type(x) - - return series.map(_fn) - - -def _detect_compact_date_formats( - series: pd.Series, -) -> tuple[Optional[str], Optional[str]]: - cser = series.map(_normalize_messy_date_to_compact) - c8s = pd.Series( - [x for x in cser.tolist() if isinstance(x, str) and len(x) == 8] - ) - c6s = pd.Series( - [x for x in cser.tolist() if isinstance(x, str) and len(x) == 6] - ) - fmt8 = detect_date_format(c8s) if not c8s.empty else None - fmt6 = detect_date_format(c6s) if not c6s.empty else None - return fmt8, fmt6 - - -def _parse_any_date_to_tuple( - raw: Any, - *, - sep_fmt: Optional[str], - compact8_fmt: Optional[str], - compact6_fmt: Optional[str], -) -> Optional[tuple[int, int, int]]: - parsed_text = _parse_date_with_text_month(raw) - if parsed_text is not None: - return parsed_text - - if _is_nullish_scalar(raw): - return None - - raw_text = str(raw) - date_part, _ = _split_date_time(raw_text) - target = date_part if date_part else raw_text - if sep_fmt: - parsed = _parse_date_with_format(target, sep_fmt) - if parsed is not None: - return parsed - for fallback_fmt in ("DD/MM/YYYY", "MM/DD/YYYY", "YYYY/MM/DD"): - if fallback_fmt == sep_fmt: - continue - parsed = _parse_date_with_format(target, fallback_fmt) - if parsed is not None: - return parsed - - compact = _normalize_messy_date_to_compact(raw) - if not isinstance(compact, str): - return None - if len(compact) == 8: - for fmt in (compact8_fmt, "DDMMYYYY", "YYYYMMDD"): - if not fmt: - continue - parsed = _parse_date_with_format(compact, fmt) - if parsed is not None: - return parsed - if len(compact) == 6: - parsed = _parse_date_with_format(compact, compact6_fmt or "DDMMYY") - if parsed is not None: - return parsed - return None - - -def _rule_date_to_ddmmyyyy(series: pd.Series) -> pd.Series: - """Normaliza fechas numéricas, alfanuméricas, con meses en texto y con ruido.""" - stripped = series.astype(str).str.replace(r"[\s\/\-\.]", "", regex=True) - stripped = stripped.where(series.notna(), other=pd.NA) - sep_fmt = detect_date_format(series) or detect_date_format(stripped) - compact8_fmt, compact6_fmt = _detect_compact_date_formats(series) - - def _fn(x: Any): - if pd.isna(x) or str(x).strip() == "": - return x - parsed = _parse_any_date_to_tuple( - x, - sep_fmt=sep_fmt, - compact8_fmt=compact8_fmt, - compact6_fmt=compact6_fmt, - ) - if parsed is None: - return x - d, m, y = parsed - return f"{d:02d}/{m:02d}/{y:04d}" - - return series.map(_fn) - - -def _rule_date_numeric_to_ddmmyyyy(series: pd.Series) -> pd.Series: - """Entrada: fechas en formato numérico (ej. 20240315 o 15032024). - - Salida: "DD/MM/AAAA" tras detectar el formato predominante. - """ - # Para esta regla consideramos los valores "numéricos" (quitamos separadores - # comunes si los hay para detectar un YYYYMMDD/DDMMYYYY confiable). - stripped = series.astype(str).str.replace(r"[\s\/\-\.]", "", regex=True) - stripped = stripped.where(series.notna(), other=pd.NA) - fmt = detect_date_format(stripped) - if fmt is None: - # fallback: intentar con detector normal - fmt = detect_date_format(series) - return _format_dates_to_ddmmyyyy(series, fmt) - - -def _rule_date_alpha_to_ddmmyyyy(series: pd.Series) -> pd.Series: - """Entrada: fechas en formato alfanumérico con separadores. - - Salida: "DD/MM/AAAA". - """ - fmt = detect_date_format(series) - return _format_dates_to_ddmmyyyy(series, fmt) - - -def _format_dates_to_ddmmyyyy(series: pd.Series, fmt: Optional[str]) -> pd.Series: - """Convierte cada valor al formato DD/MM/AAAA usando `fmt` como layout de - entrada detectado. La parte de hora (si existe) se descarta. Mantiene el - valor original si no puede parsear la fecha. - """ - if fmt is None: - return series.copy() - - def _fn(x): - if pd.isna(x) or str(x).strip() == "": - return x - raw = str(x) - date_part, _time_part = _split_date_time(raw) - target = date_part if date_part else raw - parsed = _parse_date_with_format(target, fmt) - if parsed is None: - return x - d, m, y = parsed - return f"{d:02d}/{m:02d}/{y:04d}" - - return series.map(_fn) - - -# --------------------------------------------------------------------------- -# Fechas con ruido (texto sucio) → compacto numérico + DD/MM/AAAA -# --------------------------------------------------------------------------- - - -def _normalize_messy_date_to_compact(raw: Any) -> Optional[str]: - """Quita ruido (* + _ / espacios, etc.); deja 6 o 8 dígitos o None. - - Soporta separadores entremezclados; hora al final vía `_split_date_time`. - Con 9+ dígitos, heurística: tomar 8 iniciales (p. ej. 9.º = 0 colado) y - validar luego con `_parse_date_with_format`; con más de 8 sobrantes, - 8 iniciales. - """ - if _is_nullish_scalar(raw): - return None - s = str(raw).strip() - if s.lower() in ("nan", "none", "nat", ""): - return None - date_part, _ = _split_date_time(s) - body = date_part if date_part else s - d = re.sub(r"[^\d]", "", body) - if not d: - return None - n = len(d) - if n in (6, 8): - return d - if n < 6 or n == 7: - return None - # 9+ dígitos: 8 iniciales (p. ej. 9.º 0 sobrante, o ruido al final) - return d[:8] - - -def _rule_date_messy_text_to_ddmmyyyy(series: pd.Series) -> pd.Series: - """Fechas con símbolos y basura entre cifras → DD/MM/AAAA. - - Normaliza a solo 6/8 dígitos, vota el formato con `detect_date_format` por - separado en compact8 y compact6; si el detector no devuelve nada, usa - `DDMMYYYY` / `DDMMYY` (alineado con la preferencia hispana en fechas - numéricas). - """ - cser = series.map(_normalize_messy_date_to_compact) - c8s = pd.Series( - [x for x in cser.tolist() if isinstance(x, str) and len(x) == 8] - ) - c6s = pd.Series( - [x for x in cser.tolist() if isinstance(x, str) and len(x) == 6] - ) - if c8s.empty and c6s.empty: - return series.copy() - - fmt8: Optional[str] = None - fmt6: Optional[str] = None - if not c8s.empty: - fmt8 = detect_date_format(c8s) or "DDMMYYYY" - if not c6s.empty: - fmt6 = detect_date_format(c6s) or "DDMMYY" - - def _one(x: Any, c: Any) -> Any: - if _is_nullish_scalar(x): - return x - if not isinstance(c, str) or len(c) not in (6, 8): - return x - f = (fmt8 or "DDMMYYYY") if len(c) == 8 else (fmt6 or "DDMMYY") - t = _parse_date_with_format(c, f) - if t is None and len(c) == 8: - t = _parse_date_with_format(c, "DDMMYYYY") or _parse_date_with_format( - c, "YYYYMMDD" - ) - if t is None and len(c) == 6: - t = _parse_date_with_format(c, "DDMMYY") - if t is None: - return x - d, m, y = t - return f"{d:02d}/{m:02d}/{y:04d}" - - return pd.Series( - [ - _one(v, cser.iloc[i] if i < len(cser) else None) for i, v in enumerate(series) - ], - index=series.index, - dtype=object, - ) - - -# --------------------------------------------------------------------------- -# Tipo y número de documento (CC, NIT, CE, MS, …) → 2 columnas -# --------------------------------------------------------------------------- - -_DOC_TYPE_ALIASES: dict[str, tuple[str, ...]] = { - "CC": ("CC", "Cedula", "CD", "C", "C.C", "C C", "CCC"), - "TI": ("TI", "Tarjeta", "Tarjeta Identidad", "T1", "T", "TL"), - "CN": ("CN", "Certificado", "Nacido Vivo", "Certificado Nacido Vivo"), - "CE": ("CE", "Cedula de Extranjeria", "Extranjera"), - "ME": ("ME", "Menor sin Identificacion"), - "PS": ("PS", "Pasaporte"), - "PT": ("PT", "Permiso por proteccion Temporal"), - "RC": ("RC", "Registro Civil", "Registro", "TC"), - "SC": ("SC", "Salvo Conducto", "Salvoconducto"), - "PE": ("PE", "Permiso especial de permanencia", "Permiso"), -} - - -def _ascii_upper(value: Any) -> str: - text = unicodedata.normalize("NFKD", str(value or "")) - text = "".join(c for c in text if not unicodedata.combining(c)) - return text.upper() - - -def _normalize_doc_type_key(value: Any) -> str: - """Clave comparable: sin acentos, espacios, puntos ni símbolos.""" - return re.sub(r"[^A-Z0-9]", "", _ascii_upper(value)) - - -def _clean_doc_type_text(value: Any) -> Optional[str]: - cleaned = re.sub(r"[^A-Z0-9]+", " ", _ascii_upper(value)).strip() - cleaned = re.sub(r"\s+", " ", cleaned) - return cleaned or None - - -_DOC_TYPE_ALIAS_BY_KEY: dict[str, str] = { - _normalize_doc_type_key(alias): canonical - for canonical, aliases in _DOC_TYPE_ALIASES.items() - for alias in aliases -} - -_DOC_TYPE_ALIAS_DISPLAY_BY_KEY: dict[str, str] = {} -for _canonical, _aliases in _DOC_TYPE_ALIASES.items(): - for _alias in _aliases: - _alias_key = _normalize_doc_type_key(_alias) - _alias_display = _clean_doc_type_text(_alias) - if _alias_key and _alias_display: - _DOC_TYPE_ALIAS_DISPLAY_BY_KEY.setdefault(_alias_key, _alias_display) - -_DOC_TYPE_ALIAS_KEYS_SORTED: list[str] = sorted( - _DOC_TYPE_ALIAS_BY_KEY, - key=lambda k: (len(k), k), - reverse=True, -) - - -def homologate_doc_type(value: Any) -> Optional[str]: - """Homologa un tipo documental a sigla; si no existe, conserva texto limpio.""" - if _is_nullish_scalar(value): - return None - key = _normalize_doc_type_key(value) - if not key or key.lower() in ("nan", "none", "nat"): - return None - return _DOC_TYPE_ALIAS_BY_KEY.get(key) or _clean_doc_type_text(value) - - -def _compact_doc_text(value: Any) -> str: - return _normalize_doc_type_key(value) - - -def _looks_like_short_alias_prefix(compact: str, alias_key: str) -> bool: - """Evita que alias de 1-2 chars capturen palabras como CODIGO o TOTAL.""" - if not compact.startswith(alias_key): - return False - rest = compact[len(alias_key):] - if not rest: - return True - return bool(rest[0].isdigit()) - - -def _match_known_doc_type_prefix(compact: str) -> Optional[tuple[str, str]]: - for alias_key in _DOC_TYPE_ALIAS_KEYS_SORTED: - if len(alias_key) <= 2: - if not _looks_like_short_alias_prefix(compact, alias_key): - continue - elif not compact.startswith(alias_key): - continue - return _DOC_TYPE_ALIAS_BY_KEY[alias_key], alias_key - return None - - -def split_doc_tipo_y_numero_texto( - raw: Any, - *, - homologate: bool = True, -) -> tuple[Optional[str], Optional[str]]: - """Separa tipo documental y número, tolerando nombres largos y símbolos. - - Si `homologate` es True, tipos conocidos se devuelven como sigla (CC, TI, - etc.). Los tipos desconocidos se conservan limpios en mayúsculas. - """ - if _is_nullish_scalar(raw) and not (isinstance(raw, str) and not str(raw).strip()): - return (None, None) - t = str(raw).strip() - if t.lower() in ("nan", "none", "nat", ""): - return (None, None) - if t and t[0].isdigit(): - return (None, None) - - compact = _compact_doc_text(t) - if not compact: - return (None, None) - - match = _match_known_doc_type_prefix(compact) - if match is not None: - canonical, alias_key = match - rest = compact[len(alias_key):] - nums = re.sub(r"[^\d]", "", rest) - if homologate: - return (canonical, nums if nums else None) - raw_type = _DOC_TYPE_ALIAS_DISPLAY_BY_KEY.get(alias_key) or canonical - return (raw_type, nums if nums else None) - - m = re.match( - r"^([^\d]+?)\s*([0-9].*)\s*$", - t, - ) - if m: - to_t = _clean_doc_type_text(m.group(1)) - nums = re.sub(r"[^\d]", "", m.group(2)) - return (homologate_doc_type(to_t) if homologate else to_t, nums if nums else None) - - if not re.search(r"\d", t): - to_t = homologate_doc_type(t) if homologate else _clean_doc_type_text(t) - return (to_t, None) - return (None, None) - - -def _alloc_doc_split_column_names( - col_name: str, existing: set[str] -) -> tuple[str, str]: - """Nombres únicos para las dos columnas derivadas.""" - base = re.sub(r"[^\w]+", "_", str(col_name).strip(), flags=re.ASCII) or "col" - for i in range(0, 200): - suf = f"_{i}" if i else "" - t, n = f"{base}{suf}_doc_tipo", f"{base}{suf}_doc_numeros" - if t not in existing and n not in existing: - return t, n - raise ValueError("No se pudieron asignar nombres de columnas únicos.") - - -def _rule_passthrough_series(s: pd.Series) -> pd.Series: - return s.copy() - - -def _apply_doc_id_split_columns( - result: pd.DataFrame, col: str, log: list[str], *, homologate_type: bool = True -) -> None: - tcol, ncol = _alloc_doc_split_column_names(str(col), set(result.columns)) - st_tipo: list[Optional[str]] = [] - st_num: list[Any] = [] - for v in result[col].tolist(): - tt, nn = split_doc_tipo_y_numero_texto(v, homologate=homologate_type) - st_tipo.append(tt) - st_num.append(nn if nn is not None else pd.NA) - result[tcol] = st_tipo - result[ncol] = st_num - log.append( - f"[OK] '{str(col)}' -> columnas '{tcol}' y '{ncol}' <- Dividir tipo y número (documento)" - ) - - -# --- Texto tipo nota (Paciente / Autorización) -------------------------------- - -_PACIENTE_DOC_NOTA_RE = re.compile(r"(?i)Paciente:\s*(\d+)\s*-\s*") -_AUTORIZACION_NOTA_RE = re.compile(r"(?i)Autorizaci[oó]n\s*No\.?\s*(\d+)") - - -def _normalize_nota_text(raw: Any) -> str: - if _is_nullish_scalar(raw): - return "" - try: - if pd.isna(raw): - return "" - except TypeError: - pass - s = str(raw).strip() - if not s or s.lower() in ("nan", "none", "nat", ""): - return "" - return re.sub(r"\s+", " ", s) - - -def extract_paciente_documento_nota(raw: Any) -> Optional[str]: - """Número de documento tras ``Paciente:`` y antes del guión del nombre.""" - t = _normalize_nota_text(raw) - if not t: - return None - m = _PACIENTE_DOC_NOTA_RE.search(t) - return m.group(1) if m else None - - -def extract_autorizacion_nota(raw: Any) -> Optional[str]: - """Número tras ``Autorización No.`` (opcional por fila).""" - t = _normalize_nota_text(raw) - if not t: - return None - m = _AUTORIZACION_NOTA_RE.search(t) - return m.group(1) if m else None - - -def _alloc_nota_extract_column_names( - col_name: str, existing: set[str] -) -> tuple[str, str]: - """Nombres únicos para documento paciente y autorización.""" - base = re.sub(r"[^\w]+", "_", str(col_name).strip(), flags=re.ASCII) or "col" - for i in range(0, 200): - suf = f"_{i}" if i else "" - dcol, acol = f"{base}{suf}_nota_paciente_doc", f"{base}{suf}_nota_autorizacion" - if dcol not in existing and acol not in existing: - return dcol, acol - raise ValueError("No se pudieron asignar nombres de columnas únicos.") - - -def _apply_nota_extract_columns(result: pd.DataFrame, col: str, log: list[str]) -> None: - """Añade columnas con documento (Paciente:) y número de autorización si existe.""" - dcol, acol = _alloc_nota_extract_column_names(str(col), set(result.columns)) - docs: list[Any] = [] - auths: list[Any] = [] - for v in result[col].tolist(): - dd = extract_paciente_documento_nota(v) - aa = extract_autorizacion_nota(v) - docs.append(dd if dd is not None else pd.NA) - auths.append(aa if aa is not None else pd.NA) - result[dcol] = docs - result[acol] = auths - log.append( - f"[OK] '{str(col)}' -> columnas '{dcol}' y '{acol}' <- Extraer datos de notas" - ) - - -# Registro de reglas -CLEANING_RULES: list[dict[str, Any]] = [ - { - "id": "strip_special_spaces", - "name": "Eliminar caracteres especiales y espacios", - "description": ( - "Deja solo caracteres alfanuméricos (A-Z, a-z, 0-9). No usar antes de " - "«Normalizar fecha» en columnas con fecha y hora: pega cifras y puede impedir " - "interpretar el valor (aplique primero la normalización de fecha o use la regla " - "que conserva separadores)." - ), - "fn": _rule_strip_special_spaces, - }, - { - "id": "strip_special_keep_date_seps", - "name": "Quitar símbolos; conserva /, -, : y espacios (fechas o texto con fechas)", - "description": ( - "Quita comas, $, comillas, etc. y deja letras, números, /, -, : (hora) y espacios. " - "Adecuado p. ej. en columna 'Fecha Servicio'." - ), - "fn": _rule_strip_special_keep_date_seps, - }, - { - "id": "strip_spaces", - "name": "Eliminar únicamente espacios", - "description": "Elimina todos los caracteres de espacio en blanco.", - "fn": _rule_strip_spaces, - }, - { - "id": "to_integer", - "name": "Convertir a Entero", - "description": "Convierte a enteros con detección de miles/decimal (`,`/`.`) y moneda; redondea decimales.", - "fn": _rule_to_integer, - }, - { - "id": "date_to_ddmmyyyy", - "name": "Normalizar fecha → DD/MM/AAAA", - "description": ( - "Normaliza fechas numéricas, con separadores, con ruido o con meses en " - "texto (ej. 26-abril-2026) al formato DD/MM/AAAA." - ), - "fn": _rule_date_to_ddmmyyyy, - }, - { - "id": "date_numeric_to_ddmmyyyy", - "name": "Conversión de formato numérico (Fechas) → DD/MM/AAAA", - "description": "Detecta el formato numérico predominante (YYYYMMDD, DDMMYYYY, etc.) y normaliza a DD/MM/AAAA.", - "fn": _rule_date_numeric_to_ddmmyyyy, - }, - { - "id": "date_alpha_to_ddmmyyyy", - "name": "Conversión de formato alfanumérico (Fechas) → DD/MM/AAAA", - "description": "Detecta el formato con separadores predominante (DD/MM/YYYY, MM/DD/YYYY, YYYY/MM/DD, etc.) y normaliza a DD/MM/AAAA.", - "fn": _rule_date_alpha_to_ddmmyyyy, - }, - { - "id": "date_messy_text_to_ddmmyyyy", - "name": "Texto a fecha (ruido en fechas) → DD/MM/AAAA", - "description": "Quita ruido entre cifras (*, +, _, /, etc.), detecta el orden en la columna (DDMMYYYY, YYYYMMDD, DDMMYY) y unifica a DD/MM/AAAA.", - "fn": _rule_date_messy_text_to_ddmmyyyy, - }, - { - "id": "round_decimals_custom", - "name": "Aproximación Decimales (>0,5 sube, ≤0,5 baja)", - "description": "Usa el mismo análisis de miles, decimales y moneda que «Entero». Tras leer el número, si la parte fraccionaria de |valor| es mayor a 0,5 se aproxima al entero hacia lejos de cero; en caso contrario, hacia cero. El 0,5 exacto se baja.", - "fn": _rule_round_decimals_custom, - }, - { - "id": "doc_id_split_tipo_numero", - "name": "Dividir tipo y número (documento)", - "description": ( - "Añade dos columnas (nombres únicos p. ej. TipoID_doc_tipo, TipoID_doc_numeros): " - "prefijo con letras (NIT, CC, CE, …) y cifras sin separadores. No modifica la columna de origen." - ), - "fn": _rule_passthrough_series, - }, - { - "id": "doc_type_homologate", - "name": "Homologar tipo de documento", - "description": ( - "Convierte tipos de documento escritos como nombre largo, sigla o texto con " - "símbolos a su abreviatura estándar (CC, TI, CE, RC, etc.)." - ), - "fn": _rule_doc_type_homologate, - }, - { - "id": "cop_mod_split", - "name": "Separa COP/MOD (copago / moderadora)", - "description": ( - "Según el año en la columna Fecha Servicio que elija en la tarjeta, clasifica " - "el importe en cuota moderadora (valores por año en Configuración) o copago. " - "Se ejecuta al final del proceso de limpieza para usar fechas ya normalizadas." - ), - "fn": _rule_passthrough_series, - }, - { - "id": "nota_paciente_autorizacion", - "name": "Extraer datos de notas (documento y autorización)", - "description": ( - "Desde texto tipo nota hospitalaria, extrae el número tras «Paciente:» (antes del " - "guión del nombre) y, si existe, el de «Autorización No.». Añade dos columnas; " - "no modifica el texto original." - ), - "fn": _rule_passthrough_series, - }, -] - -_RULE_BY_ID: dict[str, Callable[[pd.Series], pd.Series]] = { - r["id"]: r["fn"] for r in CLEANING_RULES -} - -_RULE_NAME_BY_ID: dict[str, str] = {r["id"]: r["name"] for r in CLEANING_RULES} - - -def get_rule(rule_id: str) -> Optional[dict[str, Any]]: - """Devuelve el registro de una regla por id.""" - for r in CLEANING_RULES: - if r["id"] == rule_id: - return r - return None - - -# --------------------------------------------------------------------------- -# APLICACIÓN DE REGLAS -# --------------------------------------------------------------------------- - - -def apply_rules( - df: pd.DataFrame, - rules_per_column: dict[str, list[str]], - *, - cop_mod_fecha_column: Optional[dict[str, str]] = None, - cop_mod_config: Optional[dict[str, Any]] = None, -) -> tuple[pd.DataFrame, list[str]]: - """Aplica las reglas seleccionadas a cada columna. - - `rules_per_column` es un dict de `columna -> lista de IDs de reglas en el - orden a aplicar`. La regla `cop_mod_split` se difiere a una segunda pasada - tras el resto, para que la columna de fecha pueda normalizarse antes. - - `cop_mod_fecha_column`: mapa columna de importe -> nombre de columna Fecha Servicio. - `cop_mod_config`: opcional; puede incluir ``moderadora_by_year`` (dict año → lista). - """ - result = df.copy() - log: list[str] = [] - - moderadora_years = merge_moderadora_config(cop_mod_config) - - def _run_rules_for_column(col: str, rule_ids: list[str]) -> None: - if not rule_ids: - return - if col not in result.columns: - log.append(f"[WARN] Columna no encontrada: {col}") - return - split_has_homologation = ( - "doc_id_split_tipo_numero" in rule_ids and "doc_type_homologate" in rule_ids - ) - for rule_id in rule_ids: - if rule_id == "doc_id_split_tipo_numero": - try: - _apply_doc_id_split_columns( - result, col, log, homologate_type=split_has_homologation - ) - except Exception as exc: # noqa: BLE001 - logger.exception("Error aplicando regla %s a %s", rule_id, col) - log.append(f"[ERROR] '{col}' regla '{rule_id}': {exc}") - continue - if rule_id == "nota_paciente_autorizacion": - try: - _apply_nota_extract_columns(result, col, log) - except Exception as exc: # noqa: BLE001 - logger.exception("Error aplicando regla %s a %s", rule_id, col) - log.append(f"[ERROR] '{col}' regla '{rule_id}': {exc}") - continue - if rule_id == "doc_type_homologate" and split_has_homologation: - continue - fn = _RULE_BY_ID.get(rule_id) - if fn is None: - log.append(f"[WARN] Regla desconocida '{rule_id}' en columna '{col}'") - continue - try: - result[col] = fn(result[col]) - log.append(f"[OK] '{col}' <- {_RULE_NAME_BY_ID.get(rule_id, rule_id)}") - except Exception as exc: # noqa: BLE001 - logger.exception("Error aplicando regla %s a %s", rule_id, col) - log.append(f"[ERROR] '{col}' regla '{rule_id}': {exc}") - - # Fase 1: todas las reglas excepto cop_mod_split - for col, rule_ids in rules_per_column.items(): - filtered = [r for r in rule_ids if r != "cop_mod_split"] - _run_rules_for_column(col, filtered) - - # Fase 2: Separa COP/MOD sobre el DataFrame ya transformado - fecha_map = cop_mod_fecha_column or {} - for col, rule_ids in rules_per_column.items(): - if "cop_mod_split" not in rule_ids: - continue - fecha_col = fecha_map.get(col) - if not fecha_col: - log.append( - f"[WARN] COP/MOD en '{col}': indique la columna «Fecha Servicio» en la tarjeta; se omite." - ) - continue - try: - _apply_cop_mod_split( - result, - col, - fecha_col, - log, - moderadora_by_year=moderadora_years, - ) - except Exception as exc: # noqa: BLE001 - logger.exception("Error aplicando COP/MOD a %s", col) - log.append(f"[ERROR] '{col}' regla 'cop_mod_split': {exc}") - - return result, log - - -# --------------------------------------------------------------------------- -# CARGA ROBUSTA DE CSV -# --------------------------------------------------------------------------- - -_CANDIDATE_DELIMITERS = ["|", ";", "\t", ",", ":"] - - -def detect_delimiter(filepath: str) -> str: - """Detecta el delimitador del CSV de forma robusta. - - Estrategia: - 1. Intentar `csv.Sniffer` con un sample grande. - 2. Si falla o el resultado parece incorrecto, contar ocurrencias por línea - de cada delimitador candidato y elegir el que produce el mayor número - de columnas estable entre líneas. - """ - encodings = ("utf-8", "utf-8-sig", "latin-1", "cp1252") - sample = "" - for enc in encodings: - try: - with open(filepath, "r", encoding=enc, errors="replace") as f: - sample = f.read(65536) - break - except Exception: - continue - - if not sample: - return "," - - try: - dialect = _csv.Sniffer().sniff(sample, delimiters="".join(_CANDIDATE_DELIMITERS)) - sniffed = dialect.delimiter - except _csv.Error: - sniffed = None - - lines = [ln for ln in sample.splitlines() if ln.strip()] - if not lines: - return sniffed or "," - - # Para cada candidato, calcular el número promedio de columnas y la - # consistencia (cuántas líneas tienen el mismo conteo dominante). - scores: dict[str, tuple[int, int]] = {} - for delim in _CANDIDATE_DELIMITERS: - counts = [ln.count(delim) for ln in lines[:50]] - if max(counts) == 0: - continue - # Modo (valor más frecuente) - common = Counter(counts).most_common(1)[0] - mode_count, mode_freq = common - if mode_count == 0: - continue - scores[delim] = (mode_count, mode_freq) - - if scores: - # Elegir el delimitador con mayor número de columnas (mode_count) y, - # como desempate, mayor consistencia (mode_freq). - best = max(scores.items(), key=lambda kv: (kv[1][0], kv[1][1])) - best_delim = best[0] - # Si el sniffer dio algo razonable y coincide, prevalece; si difiere y - # nuestro candidato es claramente mejor, usamos el nuestro. - if sniffed and sniffed in scores: - sniffed_score = scores[sniffed] - if sniffed_score[0] >= best[1][0] - 1: - return sniffed - return best_delim - - return sniffed or "," - - -def load_csv(filepath: str) -> tuple[pd.DataFrame, str]: - """Carga un CSV con detección de delimitador y tolerancia a filas malas. - - Devuelve `(df, delimiter)`. Lanza excepción si todos los intentos fallan. - """ - delimiter = detect_delimiter(filepath) - - encodings_to_try = ["utf-8", "utf-8-sig", "latin-1", "cp1252"] - last_error: Optional[Exception] = None - - for enc in encodings_to_try: - try: - df = pd.read_csv( - filepath, - delimiter=delimiter, - dtype=str, - encoding=enc, - engine="python", - on_bad_lines="skip", - ) - return df, delimiter - except Exception as exc: # noqa: BLE001 - last_error = exc - continue - - # Último intento con quoting permisivo y sin separador detectado - try: - df = pd.read_csv( - filepath, - sep=None, - dtype=str, - engine="python", - on_bad_lines="skip", - ) - return df, "(auto)" - except Exception as exc: # noqa: BLE001 - last_error = exc - - raise RuntimeError(f"No se pudo leer el CSV: {last_error}") diff --git a/services/cleaning/domain/csv_cleaning/__init__.py b/services/cleaning/domain/csv_cleaning/__init__.py new file mode 100644 index 0000000..52edbe3 --- /dev/null +++ b/services/cleaning/domain/csv_cleaning/__init__.py @@ -0,0 +1,64 @@ +"""Limpieza de CSV con análisis inteligente local y reglas configurables. + +API pública (estable): +- ``analyze_dataframe`` — resumen por columna +- ``CLEANING_RULES`` / ``register_rule`` / ``get_rule`` — catálogo de reglas +- ``apply_rules`` — aplica reglas por columna +- ``load_csv`` / ``load_csv_bytes`` / ``detect_delimiter`` — I/O + +Para agregar una regla nueva, ver ``rules/registry.py`` y ``rules/builtins.py``. +""" +from __future__ import annotations + +from .analyze import analyze_dataframe, sample_dataframe_rows +from .apply import apply_rules +from .cop_mod import ( + DEFAULT_MODERADORA_BY_YEAR, + merge_moderadora_config, + normalize_moderadora_by_year_dict, +) +from .documents import homologate_doc_type, split_doc_tipo_y_numero_texto +from .io import detect_delimiter, load_csv, load_csv_bytes +from .models import ( + TYPE_BOOLEAN, + TYPE_CATEGORICAL, + TYPE_DATE, + TYPE_DECIMAL, + TYPE_EMPTY, + TYPE_INTEGER, + TYPE_MIXED, + TYPE_TEXT, + ColumnSummary, +) +from .notas import extract_autorizacion_nota, extract_paciente_documento_nota +from .rules import CLEANING_RULES, get_rule, register_rule +from .type_inference import applicable_rule_ids_for_type + +__all__ = [ + "CLEANING_RULES", + "ColumnSummary", + "DEFAULT_MODERADORA_BY_YEAR", + "TYPE_BOOLEAN", + "TYPE_CATEGORICAL", + "TYPE_DATE", + "TYPE_DECIMAL", + "TYPE_EMPTY", + "TYPE_INTEGER", + "TYPE_MIXED", + "TYPE_TEXT", + "analyze_dataframe", + "applicable_rule_ids_for_type", + "apply_rules", + "detect_delimiter", + "extract_autorizacion_nota", + "extract_paciente_documento_nota", + "get_rule", + "homologate_doc_type", + "load_csv", + "load_csv_bytes", + "merge_moderadora_config", + "normalize_moderadora_by_year_dict", + "register_rule", + "sample_dataframe_rows", + "split_doc_tipo_y_numero_texto", +] diff --git a/services/cleaning/domain/csv_cleaning/analyze.py b/services/cleaning/domain/csv_cleaning/analyze.py new file mode 100644 index 0000000..ee051f4 --- /dev/null +++ b/services/cleaning/domain/csv_cleaning/analyze.py @@ -0,0 +1,94 @@ +"""Análisis de DataFrames: resumen por columna y muestras.""" +from __future__ import annotations + +import json +import random +from typing import Any + +import pandas as pd + +from .models import ( + TYPE_CATEGORICAL, + TYPE_DATE, + TYPE_DECIMAL, + TYPE_INTEGER, + TYPE_TEXT, + ColumnSummary, +) +from .numbers import _to_numeric_lenient +from .type_inference import _applicable_rules_for_type, _infer_column_type + + +def analyze_dataframe(df: pd.DataFrame) -> list[ColumnSummary]: + """Genera un resumen por columna del DataFrame.""" + summaries: list[ColumnSummary] = [] + total_rows = len(df) + rng = random.Random(42) + + for col in df.columns: + series = df[col] + null_count = int(series.isna().sum() + (series.astype(str).str.strip() == "").sum()) + # Evitar doble conteo: considerar vacío solo si no-NaN + null_count = int((series.isna() | (series.astype(str).str.strip() == "")).sum()) + null_pct = (null_count / total_rows * 100) if total_rows else 0.0 + + non_null = series.dropna().astype(str) + non_null = non_null[non_null.str.strip() != ""] + + example = "" + samples: list[str] = [] + if not non_null.empty: + values = non_null.tolist() + k = min(5, len(values)) + picked = rng.sample(values, k) if len(values) >= k else list(values) + for raw in picked: + text = str(raw) + if len(text) > 80: + text = text[:77] + "..." + samples.append(text) + example = samples[0] if samples else "" + + unique_count = int(non_null.nunique()) + unique_pct = (unique_count / total_rows * 100) if total_rows else 0.0 + + inferred_type, date_fmt = _infer_column_type(series, str(col)) + + summary = ColumnSummary( + name=str(col), + inferred_type=inferred_type, + example=example, + samples=samples, + null_count=null_count, + null_pct=null_pct, + unique_count=unique_count, + unique_pct=unique_pct, + detected_date_format=date_fmt, + ) + + if inferred_type in (TYPE_INTEGER, TYPE_DECIMAL): + numeric = _to_numeric_lenient(non_null) + if numeric.notna().any(): + summary.min = float(numeric.min()) + summary.max = float(numeric.max()) + summary.mean = float(numeric.mean()) + elif inferred_type in (TYPE_TEXT, TYPE_CATEGORICAL): + if not non_null.empty: + summary.avg_length = float(non_null.str.len().mean()) + counts = non_null.value_counts().head(3) + summary.top_values = [(str(v), int(c)) for v, c in counts.items()] + + summary.applicable_rules = _applicable_rules_for_type(inferred_type) + summaries.append(summary) + + return summaries + + +def sample_dataframe_rows(df: pd.DataFrame, n: int = 8) -> list[dict[str, Any]]: + """Devuelve hasta `n` filas aleatorias como dicts JSON-serializables.""" + if df.empty or n <= 0: + return [] + take = min(n, len(df)) + rng = random.Random(42) + indices = rng.sample(list(range(len(df))), take) + subset = df.iloc[indices] + return json.loads(subset.to_json(orient="records", date_format="iso")) diff --git a/services/cleaning/domain/csv_cleaning/apply.py b/services/cleaning/domain/csv_cleaning/apply.py new file mode 100644 index 0000000..72d96dd --- /dev/null +++ b/services/cleaning/domain/csv_cleaning/apply.py @@ -0,0 +1,105 @@ +"""Aplicación de reglas seleccionadas sobre un DataFrame.""" +from __future__ import annotations + +import logging +from typing import Any, Optional + +import pandas as pd + +from .cop_mod import _apply_cop_mod_split, merge_moderadora_config +from .documents import _apply_doc_id_split_columns +from .notas import _apply_nota_extract_columns +from .rules import get_rule_fn, get_rule_name + +logger = logging.getLogger(__name__) + + +def apply_rules( + df: pd.DataFrame, + rules_per_column: dict[str, list[str]], + *, + cop_mod_fecha_column: Optional[dict[str, str]] = None, + cop_mod_config: Optional[dict[str, Any]] = None, +) -> tuple[pd.DataFrame, list[str]]: + """Aplica las reglas seleccionadas a cada columna. + + `rules_per_column` es un dict de `columna -> lista de IDs de reglas en el + orden a aplicar`. La regla `cop_mod_split` se difiere a una segunda pasada + tras el resto, para que la columna de fecha pueda normalizarse antes. + + `cop_mod_fecha_column`: mapa columna de importe -> nombre de columna Fecha Servicio. + `cop_mod_config`: opcional; puede incluir ``moderadora_by_year`` (dict año → lista). + """ + result = df.copy() + log: list[str] = [] + + moderadora_years = merge_moderadora_config(cop_mod_config) + + def _run_rules_for_column(col: str, rule_ids: list[str]) -> None: + if not rule_ids: + return + if col not in result.columns: + log.append(f"[WARN] Columna no encontrada: {col}") + return + split_has_homologation = ( + "doc_id_split_tipo_numero" in rule_ids and "doc_type_homologate" in rule_ids + ) + for rule_id in rule_ids: + if rule_id == "doc_id_split_tipo_numero": + try: + _apply_doc_id_split_columns( + result, col, log, homologate_type=split_has_homologation + ) + except Exception as exc: # noqa: BLE001 + logger.exception("Error aplicando regla %s a %s", rule_id, col) + log.append(f"[ERROR] '{col}' regla '{rule_id}': {exc}") + continue + if rule_id == "nota_paciente_autorizacion": + try: + _apply_nota_extract_columns(result, col, log) + except Exception as exc: # noqa: BLE001 + logger.exception("Error aplicando regla %s a %s", rule_id, col) + log.append(f"[ERROR] '{col}' regla '{rule_id}': {exc}") + continue + if rule_id == "doc_type_homologate" and split_has_homologation: + continue + fn = get_rule_fn(rule_id) + if fn is None: + log.append(f"[WARN] Regla desconocida '{rule_id}' en columna '{col}'") + continue + try: + result[col] = fn(result[col]) + log.append(f"[OK] '{col}' <- {get_rule_name(rule_id)}") + except Exception as exc: # noqa: BLE001 + logger.exception("Error aplicando regla %s a %s", rule_id, col) + log.append(f"[ERROR] '{col}' regla '{rule_id}': {exc}") + + # Fase 1: todas las reglas excepto cop_mod_split + for col, rule_ids in rules_per_column.items(): + filtered = [r for r in rule_ids if r != "cop_mod_split"] + _run_rules_for_column(col, filtered) + + # Fase 2: Separa COP/MOD sobre el DataFrame ya transformado + fecha_map = cop_mod_fecha_column or {} + for col, rule_ids in rules_per_column.items(): + if "cop_mod_split" not in rule_ids: + continue + fecha_col = fecha_map.get(col) + if not fecha_col: + log.append( + f"[WARN] COP/MOD en '{col}': indique la columna «Fecha Servicio» en la tarjeta; se omite." + ) + continue + try: + _apply_cop_mod_split( + result, + col, + fecha_col, + log, + moderadora_by_year=moderadora_years, + ) + except Exception as exc: # noqa: BLE001 + logger.exception("Error aplicando COP/MOD a %s", col) + log.append(f"[ERROR] '{col}' regla 'cop_mod_split': {exc}") + + return result, log diff --git a/services/cleaning/domain/csv_cleaning/column_hints.py b/services/cleaning/domain/csv_cleaning/column_hints.py new file mode 100644 index 0000000..342b95b --- /dev/null +++ b/services/cleaning/domain/csv_cleaning/column_hints.py @@ -0,0 +1,210 @@ +"""Heurísticas por nombre de columna y presentación del contenido.""" +from __future__ import annotations + +import re +from typing import Optional + +import pandas as pd + +from .nullish import _is_nullish_scalar + + +def _column_name_suggests_date_like(name: str) -> bool: + """Columnas cuyo nombre pide tratar el contenido como fecha a priori.""" + n = re.sub(r"[_\s.]+", " ", (name or "")).lower().strip() + if not n: + return False + if re.search( + r"\b(fecha|date|año|mes|dia|día|f[_-]?(alta|baja|proc|corte|pago|ini|fin|" + r"desde|hasta|vigenc|vence|venc|caduc|f\s?ec)\b|periodo|anio|ano|expir)", + n, + ) or n.startswith("fec_") or n.startswith("fecha"): + return True + return False + + +def _column_name_suggests_monetary_value(name: str) -> bool: + """importe, valor, neto, IVA, etc. — no interpretar cifras como fechas. + + Encabezados de tipo `fecha_…` no se tratan como moneda salvo mezcla con monto/valor. + """ + n = re.sub(r"[_\s.]+", " ", (name or "")).lower().strip() + if not n: + return False + if _column_name_suggests_date_like(name) and not re.search( + r"\b(monto|valor|importe|pago|total|bruto|neto|import|unit|precio|copago)\b", n + ): + return False + for key in ( + "monto", "importe", "import", "bruto", "neto", "subtotal", "sub total", + "total", "unitario", "unidad", "valor", "precio", "pago", "copago", "sald", "dcto", + "franq", "cuota", "iva", "tarif", "costo", "carg", "reten", "retencion", + ): + if key in n: + if key == "iva" and re.search(r"(priva|giva|diva|viva|cliva)\b", n): + continue + if key == "carg" and re.search(r"descarg|recarg|abord", n): + continue + return True + return False + + +def _column_name_suggests_code_id(name: str) -> bool: + """Código, ref., radicado, tipoynúm de documento — no forzar 6-8 cifras como fechas (DDMMYY, etc.).""" + n = re.sub(r"[_\s.]+", " ", (name or "")).lower().strip() + if not n or _column_name_suggests_date_like(name): + return False + for key in ( + "codigo", "código", "codig", "códig", "cód_", + "sku", "ean", "upc", "isbn", "upn", + "ref", "referen", "radic", "nro_fac", "nro fac", "nit", "cédul", "cedul", + "autoriz", "correlat", "secuenc", "línea de", "linea de", "cupo", + "tipy", "tipi", "tipynum", "typnum", "type_n", "n_doc", "n_docu", + "núm doc", "n_docu", "doc_id", "docum", "id_doc", + ): + if key in n: + return True + if re.search( + r"tipo[_\s\\-]*(&|/|u|e|y)[_\s\\-]*num|tipo[_\s\\-]+doc|tipo_y_n", + n, + re.IGNORECASE, + ) or n.startswith("tip_"): + return True + if n.startswith("cod_") or n.startswith("cód_") or n.startswith("ref_"): + return True + return bool(re.search(r"\b(nro[._]fac|#?\s*autor|#?\s*ref)\b", n, re.IGNORECASE)) + + +def _column_name_suggests_quantity_or_number_not_bool(name: str) -> bool: + """Nombres típicos de cantidad, importe, fechas: no inferir bool por 0/1.""" + n = re.sub(r"[_\s.]+", " ", (name or "")).lower().strip() + if not n: + return False + if _column_name_suggests_monetary_value(name) or _column_name_suggests_code_id(name): + return True + for key in ( + "fecha", + "monto", + "importe", + "bruto", + "neto", + "subtotal", + "unitario", + "unidad", + "valor", + "precio", + "pago", + "copago", + "cantid", + "quantity", + "qty", + "nro", + "núm", + "n°", + ): + if key in n: + return True + return False + + +def _raw_strings_suggest_monetary_presentation(non_null: pd.Series) -> bool: + """$ €, miles 1,234,12 o .00: presentación monetaria aunque el float sea "entero".""" + if non_null is None or len(non_null) == 0: + return False + n_hit = 0 + n_seen = 0 + for raw in non_null.head(500): + if _is_nullish_scalar(raw): + continue + t = str(raw) + s = t.strip() + n_seen += 1 + if re.search(r"[$\u00a3\u20ac\uffe5\uffe6\ufe69\uff04]", t): + n_hit += 1 + continue + if re.search(r"\b\d{1,3}(?:[.,\u00a0]\d{3})+\b", s) or re.search( + r"(?= 0.10 + + +def _cell_looks_cifra_con_moneda_iso_al_final(s: str) -> bool: + """Solo cifra/símbolo (no prefijo de documento) y código de moneda al final. + + Excluye filas al estilo `CC12…` (típico alfanumérico). + """ + t = str(s).strip() + u = t.upper() + if not re.search( + r"(?i)(USD|COP|EUR|CLP|ARS|GBP|MXN|PEN|DOP|UYU|VES|CRC|PYG|BRL|BOB|GTQ)\b\s*\Z", u + ): + return False + if re.match( + r"^(?i)(CC|CE|NIT|NUIP|TI|PA|MS|RC|DNI|NUIP|CN|SC|AS|PE|PT)\D*[\d0]", u + ): + return False + return re.match( + r"^[\$€0-9\s,.\-+%/]", t, + ) is not None + + +def _raw_strings_suggest_alnum_mixed_presentation(non_null: pd.Series) -> bool: + """Letras y dígitos juntos (p. ej. `CC32…`, `A1-2B`); no es puro 'entero/decimal' lógicamente. + + Excluido: columnas con apariencia monetaria, y celdas solo cifra + ISO al final (100 USD). + """ + if _raw_strings_suggest_monetary_presentation(non_null): + return False + n_m = 0 + n_s = 0 + for raw in non_null.head(500): + if _is_nullish_scalar(raw): + continue + t = str(raw).strip() + n_s += 1 + if not re.search(r"\d", t) or not re.search( + r"[A-Za-zÑñ\u00c0-\u024f]", t, re.IGNORECASE + ): + continue + if _cell_looks_cifra_con_moneda_iso_al_final(t): + continue + n_m += 1 + if n_s == 0: + return False + return n_m / n_s >= 0.20 + + +def _cell_looks_like_documento_id_tipo_nit(s: str) -> bool: + """Cadenas estilo `CC32324029`, `CE 1.234.567` — no son fechas (evita 8 cifras como DDMM). + + Al menos una letra al inicio y 5-16 dígitos en la celda (no `Jan`+`2020` = 4 dígitos). + """ + t = str(s).strip() + if not t or len(t) < 3: + return False + if not re.match(r"^[A-Za-z]{1,3}", t): + return False + dig = re.sub(r"[^\d]", "", t) + if len(dig) < 5 or len(dig) > 16: + return False + return True + + +def _content_looks_like_doc_id_letter_prefix_column(non_null: pd.Series) -> bool: + """Muestras con prefijo documental (CC, NIT, …) y ≥5 dígitos → no columna de fecha.""" + n_ok = 0 + n_seen = 0 + for raw in non_null.head(500): + if _is_nullish_scalar(raw): + continue + s = str(raw).strip() + n_seen += 1 + if _cell_looks_like_documento_id_tipo_nit(s): + n_ok += 1 + if n_seen == 0: + return False + return n_ok / n_seen >= 0.20 + diff --git a/services/cleaning/domain/csv_cleaning/cop_mod.py b/services/cleaning/domain/csv_cleaning/cop_mod.py new file mode 100644 index 0000000..0ffae04 --- /dev/null +++ b/services/cleaning/domain/csv_cleaning/cop_mod.py @@ -0,0 +1,162 @@ +"""Separación de copago / cuota moderadora por año.""" +from __future__ import annotations + +import re +from typing import Any, Literal, Optional + +import numpy as np +import pandas as pd + +from .dates import ( + _detect_compact_date_formats, + _parse_any_date_to_tuple, + detect_date_format, +) +from .numbers import _infer_decimal_hint_from_column, _parse_lenient_one + + +DEFAULT_MODERADORA_BY_YEAR: dict[int, set[int]] = { + 2023: {4100, 16400, 43000}, + 2024: {4500, 18200, 47700}, + 2025: {4700, 19200, 50300}, + 2026: {5000, 20100, 52800}, +} + + +def normalize_moderadora_by_year_dict(raw: Any) -> dict[int, set[int]]: + """Normaliza un dict JSON año → lista de enteros (cuotas moderadora).""" + if not isinstance(raw, dict): + return {} + out: dict[int, set[int]] = {} + for k, v in raw.items(): + try: + yi = int(k) + except (TypeError, ValueError): + continue + if not isinstance(v, (list, tuple)): + continue + nums: set[int] = set() + for x in v: + try: + nums.add(int(round(float(x)))) + except (TypeError, ValueError): + continue + if nums: + out[yi] = nums + return out + + +def merge_moderadora_config( + cop_mod_config: Optional[dict[str, Any]], +) -> dict[int, set[int]]: + """Fusiona defaults con `moderadora_by_year` opcional del usuario.""" + merged = {y: set(vals) for y, vals in DEFAULT_MODERADORA_BY_YEAR.items()} + if cop_mod_config and isinstance(cop_mod_config.get("moderadora_by_year"), dict): + custom = normalize_moderadora_by_year_dict( + cop_mod_config["moderadora_by_year"] + ) + for y, vals in custom.items(): + merged[y] = set(vals) + return merged + + +def _alloc_cop_mod_column_names( + col_name: str, existing: set[str] +) -> tuple[str, str]: + """Nombres únicos para copago y moderadora.""" + base = re.sub(r"[^\w]+", "_", str(col_name).strip(), flags=re.ASCII) or "col" + for i in range(0, 200): + suf = f"_{i}" if i else "" + ccol, mcol = f"{base}{suf}_copago", f"{base}{suf}_moderadora" + if ccol not in existing and mcol not in existing: + return ccol, mcol + raise ValueError("No se pudieron asignar nombres de columnas únicos.") + + +def _amount_cell_to_optional_int( + val: Any, + decimal_hint: Optional[Literal[",", "."]], +) -> Optional[int]: + try: + f = _parse_lenient_one(val, decimal_hint) + except Exception: + return None + if f is None or (isinstance(f, float) and not np.isfinite(f)) or ( + isinstance(f, (float, np.floating)) and np.isnan(f) + ): + return None + try: + return int(round(float(f))) + except (TypeError, ValueError, OverflowError): + return None + + +def _apply_cop_mod_split( + result: pd.DataFrame, + amount_col: str, + fecha_column: str, + log: list[str], + *, + moderadora_by_year: dict[int, set[int]], +) -> None: + """Añade columnas copago y moderadora según cuotas por año de la fecha.""" + if fecha_column not in result.columns: + log.append( + f"[WARN] COP/MOD: columna de fecha '{fecha_column}' no existe; se omite." + ) + return + if amount_col not in result.columns: + log.append(f"[WARN] COP/MOD: columna de importe '{amount_col}' no existe; se omite.") + return + + fec_ser = result[fecha_column] + amt_ser = result[amount_col] + stripped = fec_ser.astype(str).str.replace(r"[\s\/\-\.]", "", regex=True) + stripped = stripped.where(fec_ser.notna(), other=pd.NA) + sep_fmt = detect_date_format(fec_ser) or detect_date_format(stripped) + compact8_fmt, compact6_fmt = _detect_compact_date_formats(fec_ser) + amt_hint = _infer_decimal_hint_from_column(amt_ser.astype(str)) + + ccol, mcol = _alloc_cop_mod_column_names(str(amount_col), set(result.columns)) + cop_vals: list[Any] = [] + mod_vals: list[Any] = [] + + for i in range(len(result)): + amt_raw = amt_ser.iloc[i] + fec_raw = fec_ser.iloc[i] + amt_i = _amount_cell_to_optional_int(amt_raw, amt_hint) + if amt_i is None: + cop_vals.append(0) + mod_vals.append(0) + continue + + parsed_d = _parse_any_date_to_tuple( + fec_raw, + sep_fmt=sep_fmt, + compact8_fmt=compact8_fmt, + compact6_fmt=compact6_fmt, + ) + if parsed_d is None: + cop_vals.append(amt_i) + mod_vals.append(0) + continue + _d, _m, year = parsed_d + tiers = moderadora_by_year.get(year) + if tiers is None: + cop_vals.append(amt_i) + mod_vals.append(0) + continue + + if amt_i in tiers: + cop_vals.append(0) + mod_vals.append(amt_i) + else: + cop_vals.append(amt_i) + mod_vals.append(0) + + result[ccol] = cop_vals + result[mcol] = mod_vals + log.append( + f"[OK] '{amount_col}' -> '{ccol}', '{mcol}' <- Separa COP/MOD " + f"(fecha: '{fecha_column}')" + ) diff --git a/services/cleaning/domain/csv_cleaning/dates.py b/services/cleaning/domain/csv_cleaning/dates.py new file mode 100644 index 0000000..2c50d39 --- /dev/null +++ b/services/cleaning/domain/csv_cleaning/dates.py @@ -0,0 +1,459 @@ +"""Detección y normalización de fechas.""" +from __future__ import annotations + +import re +import unicodedata +from datetime import date +from typing import Any, Optional + +import pandas as pd + +from .nullish import _is_nullish_scalar + +_DATE_NUM_RE = re.compile(r"^\s*(\d{1,4})[\s\/\-\.](\d{1,2})[\s\/\-\.](\d{1,4})\s*$") +_DATE_COMPACT_RE = re.compile(r"^\s*(\d{6}|\d{8})\s*$") +# Hora opcional: HH[:MM[:SS]][ AM|PM] al final de la cadena +_TIME_TAIL_RE = re.compile( + r"[\sT]+(\d{1,2})(?::(\d{1,2})(?::(\d{1,2}))?)?\s*([APap]\.?\s*[Mm]\.?)?\s*$" +) + +_MONTH_NAME_TO_NUM = { + "enero": 1, + "ene": 1, + "febrero": 2, + "feb": 2, + "marzo": 3, + "mar": 3, + "abril": 4, + "abr": 4, + "mayo": 5, + "may": 5, + "junio": 6, + "jun": 6, + "julio": 7, + "jul": 7, + "agosto": 8, + "ago": 8, + "septiembre": 9, + "setiembre": 9, + "sep": 9, + "sept": 9, + "octubre": 10, + "oct": 10, + "noviembre": 11, + "nov": 11, + "diciembre": 12, + "dic": 12, +} + + +def _split_date_time(value: str) -> tuple[str, Optional[str]]: + """Separa la parte de fecha de la parte de hora si está presente. + + Devuelve `(date_part, time_part_normalized)`. `time_part_normalized` es + None si no hay hora. La hora se normaliza a `HH:MM[:SS][ AM|PM]`. + """ + if value is None: + return "", None + s = str(value).strip() + if not s: + return "", None + + m = _TIME_TAIL_RE.search(s) + if not m: + return s, None + + hh = int(m.group(1)) + mm = int(m.group(2)) if m.group(2) else 0 + ss = m.group(3) + ampm = m.group(4) + if not (0 <= hh <= 23 and 0 <= mm <= 59): + return s, None + if ss is not None: + try: + ssi = int(ss) + if not (0 <= ssi <= 59): + return s, None + except ValueError: + return s, None + # Validar contra AM/PM (con AM/PM, hora 1-12) + if ampm and not (1 <= hh <= 12): + return s, None + + # Quitar separadores habituales entre fecha y hora (p. ej. "12/27/24, 12:00 AM"). + date_part = s[: m.start()].rstrip(" \t,;T") + if not date_part: + return s, None + + parts: list[str] = [f"{hh:02d}:{mm:02d}"] + if ss is not None: + parts[0] += f":{int(ss):02d}" + if ampm: + norm_ampm = ampm.replace(".", "").replace(" ", "").upper() + if norm_ampm in ("AM", "PM"): + parts.append(norm_ampm) + time_norm = " ".join(parts) + return date_part, time_norm + + +def _extract_date_parts(value: str) -> Optional[tuple[int, int, int, str]]: + """Intenta extraer (a, b, c, layout) de una fecha en cualquier formato común. + + `layout` es uno de: 'sep' (con separadores), 'compact8' (YYYYMMDD o + DDMMYYYY), 'compact6' (DDMMYY o YYMMDD). No interpreta todavía qué + componente es día/mes/año; eso lo decide `detect_date_format` viendo la + columna completa. Acepta valores con hora al final (ignorada para los + componentes de fecha). + """ + if value is None: + return None + s = str(value).strip() + if not s or s.lower() in ("nan", "none", "nat"): + return None + + date_part, _ = _split_date_time(s) + if date_part: + s = date_part + + m = _DATE_NUM_RE.match(s) + if m: + try: + a, b, c = int(m.group(1)), int(m.group(2)), int(m.group(3)) + return (a, b, c, "sep") + except ValueError: + return None + + m2 = _DATE_COMPACT_RE.match(s) + if m2: + digits = m2.group(1) + if len(digits) == 8: + return (int(digits[0:2]), int(digits[2:4]), int(digits[4:8]), "compact8") + if len(digits) == 6: + return (int(digits[0:2]), int(digits[2:4]), int(digits[4:6]), "compact6") + + return None + + +def detect_date_format(series: pd.Series, sample_size: int = 500) -> Optional[str]: + """Analiza la serie y determina el formato de fecha dominante. + + Devuelve un string con el layout detectado (p. ej. "DD/MM/YYYY", + "MM/DD/YYYY", "YYYYMMDD", "DDMMYYYY") o None si no parece fechas. + """ + if series is None or len(series) == 0: + return None + + non_null = series.dropna() + if non_null.empty: + return None + + if len(non_null) > sample_size: + non_null = non_null.sample(sample_size, random_state=42) + + layouts = {"sep": 0, "compact8": 0, "compact6": 0} + sep_first_gt12 = 0 # primer componente > 12 → probable día + sep_second_gt12 = 0 # segundo componente > 12 → probable mes solo posible si MM/DD + sep_third_4digits = 0 # tercer componente de 4 dígitos → año al final + sep_first_4digits = 0 # primer componente de 4 dígitos → año al inicio + compact8_year_first = 0 # primeros 4 dígitos > 1900 → YYYYMMDD + compact8_year_last = 0 # últimos 4 dígitos > 1900 → DDMMYYYY + total_parsed = 0 + + for val in non_null: + parsed = _extract_date_parts(val) + if parsed is None: + continue + total_parsed += 1 + a, b, c, layout = parsed + layouts[layout] = layouts.get(layout, 0) + 1 + + if layout == "sep": + # año puede estar al inicio o al final según cantidad de dígitos + if _digit_count(a) == 4: + sep_first_4digits += 1 + if _digit_count(c) == 4: + sep_third_4digits += 1 + if a > 12: + sep_first_gt12 += 1 + if b > 12: + sep_second_gt12 += 1 + elif layout == "compact8": + # a son los primeros 2 dígitos, c son los últimos 4 + if c >= 1900: + compact8_year_last += 1 + # a concatenado con b son los primeros 4 dígitos + first4 = a * 100 + b + if first4 >= 1900: + compact8_year_first += 1 + + if total_parsed == 0: + return None + + # Requerimos que >=70% de los no nulos parseen como fecha para considerarla + # columna de fechas. + if total_parsed / len(non_null) < 0.7: + return None + + dominant = max(layouts, key=lambda k: layouts[k]) + + if dominant == "compact8": + if compact8_year_first > compact8_year_last: + return "YYYYMMDD" + return "DDMMYYYY" + if dominant == "compact6": + return "DDMMYY" + + # Caso con separadores + if sep_first_4digits > sep_third_4digits: + return "YYYY/MM/DD" + # año al final + if sep_first_gt12 > 0 and sep_second_gt12 == 0: + return "DD/MM/YYYY" + if sep_second_gt12 > 0 and sep_first_gt12 == 0: + return "MM/DD/YYYY" + # ambiguo → preferencia hispana + return "DD/MM/YYYY" + + +def _digit_count(n: int) -> int: + return len(str(abs(n))) + + +def _parse_date_with_format(value: str, fmt: str) -> Optional[tuple[int, int, int]]: + """Convierte un valor de fecha a (día, mes, año) según el layout detectado.""" + parsed = _extract_date_parts(value) + if parsed is None: + return None + a, b, c, layout = parsed + + if fmt == "DD/MM/YYYY": + d, m, y = a, b, c + elif fmt == "MM/DD/YYYY": + m, d, y = a, b, c + elif fmt == "YYYY/MM/DD": + y, m, d = a, b, c + elif fmt == "YYYYMMDD" and layout == "compact8": + # reconstruir: primer4 = a*100 + b, luego mes y día + y = a * 100 + b + m = c // 100 + d = c % 100 + elif fmt == "DDMMYYYY" and layout == "compact8": + d = a + m = b + y = c + elif fmt == "DDMMYY" and layout == "compact6": + d = a + m = b + y = 2000 + c if c < 50 else 1900 + c + else: + # intentar como sep genérico + d, m, y = a, b, c + + if not (1 <= m <= 12 and 1 <= d <= 31 and 1000 <= y <= 9999): + # año en 2 dígitos + if y < 100: + y = 2000 + y if y < 50 else 1900 + y + if not (1 <= m <= 12 and 1 <= d <= 31 and 1000 <= y <= 9999): + return None + return (d, m, y) + + +def _normalize_date_text(value: Any) -> str: + text = unicodedata.normalize("NFKD", str(value or "")) + text = "".join(c for c in text if not unicodedata.combining(c)) + return text.lower() + + +def _valid_date_tuple(d: int, m: int, y: int) -> Optional[tuple[int, int, int]]: + if y < 100: + y = 2000 + y if y < 50 else 1900 + y + if not (1000 <= y <= 9999): + return None + try: + date(y, m, d) + except ValueError: + return None + return (d, m, y) + + +def _split_glued_year_suffix_hhmm(digits: str) -> Optional[tuple[int, int, int]]: + """Detecta `YYYY` + `HHMM` en una sola tira de 8 cifras (p. ej. `20241344`). + + Tras quitar separadores puede quedar pegado al mes (p. ej. `15may20241344`). + Devuelve (año, hora, minuto) si la parte horaria es válida; si no, None. + """ + if len(digits) != 8 or not digits.isdigit(): + return None + y = int(digits[:4]) + hh = int(digits[4:6]) + mm = int(digits[6:8]) + if not (1000 <= y <= 9999 and 0 <= hh <= 23 and 0 <= mm <= 59): + return None + return (y, hh, mm) + + +def _parse_text_month_candidate( + first: tuple[int, str], + month: int, + second: tuple[int, str], + *, + layout: Literal["around", "after", "before"], +) -> Optional[tuple[int, int, int]]: + a, astr = first + b, bstr = second + if layout == "around": + if len(astr) == 4: + return _valid_date_tuple(b, month, a) + if len(bstr) == 4: + return _valid_date_tuple(a, month, b) + return _valid_date_tuple(a, month, b) + if layout == "after": + if len(astr) == 4: + return _valid_date_tuple(b, month, a) + return _valid_date_tuple(a, month, b) + if len(astr) == 4: + return _valid_date_tuple(b, month, a) + return _valid_date_tuple(a, month, b) + + +def _parse_date_with_text_month(raw: Any) -> Optional[tuple[int, int, int]]: + if _is_nullish_scalar(raw): + return None + date_part, _ = _split_date_time(str(raw)) + normalized = _normalize_date_text(date_part or raw) + # `\d{8}` antes que `\d{1,4}` para no partir año+hora pegados tras strip agresivo. + tokens = re.findall(r"[a-z]+|\d{8}|\d{1,4}", normalized) + if not tokens: + return None + + for i, token in enumerate(tokens): + month = _MONTH_NAME_TO_NUM.get(token) + if month is None: + continue + nums_before = [ + (int(t), t) for t in tokens[:i] if t.isdigit() + ] + nums_after = [ + (int(t), t) for t in tokens[i + 1 :] if t.isdigit() + ] + candidates: list[Optional[tuple[int, int, int]]] = [] + if nums_before and len(nums_after) == 1: + glued = _split_glued_year_suffix_hhmm(nums_after[0][1]) + if glued is not None: + y, _hh, _mm = glued + candidates.append( + _parse_text_month_candidate( + nums_before[-1], month, (y, str(y)), layout="around" + ) + ) + if nums_before and nums_after: + candidates.append( + _parse_text_month_candidate( + nums_before[-1], month, nums_after[0], layout="around" + ) + ) + if len(nums_after) >= 2: + candidates.append( + _parse_text_month_candidate( + nums_after[0], month, nums_after[1], layout="after" + ) + ) + if len(nums_before) >= 2: + candidates.append( + _parse_text_month_candidate( + nums_before[-2], month, nums_before[-1], layout="before" + ) + ) + for parsed in candidates: + if parsed is not None: + return parsed + return None + + + +def _detect_compact_date_formats( + series: pd.Series, +) -> tuple[Optional[str], Optional[str]]: + cser = series.map(_normalize_messy_date_to_compact) + c8s = pd.Series( + [x for x in cser.tolist() if isinstance(x, str) and len(x) == 8] + ) + c6s = pd.Series( + [x for x in cser.tolist() if isinstance(x, str) and len(x) == 6] + ) + fmt8 = detect_date_format(c8s) if not c8s.empty else None + fmt6 = detect_date_format(c6s) if not c6s.empty else None + return fmt8, fmt6 + + +def _parse_any_date_to_tuple( + raw: Any, + *, + sep_fmt: Optional[str], + compact8_fmt: Optional[str], + compact6_fmt: Optional[str], +) -> Optional[tuple[int, int, int]]: + parsed_text = _parse_date_with_text_month(raw) + if parsed_text is not None: + return parsed_text + + if _is_nullish_scalar(raw): + return None + + raw_text = str(raw) + date_part, _ = _split_date_time(raw_text) + target = date_part if date_part else raw_text + if sep_fmt: + parsed = _parse_date_with_format(target, sep_fmt) + if parsed is not None: + return parsed + for fallback_fmt in ("DD/MM/YYYY", "MM/DD/YYYY", "YYYY/MM/DD"): + if fallback_fmt == sep_fmt: + continue + parsed = _parse_date_with_format(target, fallback_fmt) + if parsed is not None: + return parsed + + compact = _normalize_messy_date_to_compact(raw) + if not isinstance(compact, str): + return None + if len(compact) == 8: + for fmt in (compact8_fmt, "DDMMYYYY", "YYYYMMDD"): + if not fmt: + continue + parsed = _parse_date_with_format(compact, fmt) + if parsed is not None: + return parsed + if len(compact) == 6: + parsed = _parse_date_with_format(compact, compact6_fmt or "DDMMYY") + if parsed is not None: + return parsed + return None + + +def _normalize_messy_date_to_compact(raw: Any) -> Optional[str]: + """Quita ruido (* + _ / espacios, etc.); deja 6 o 8 dígitos o None. + + Soporta separadores entremezclados; hora al final vía `_split_date_time`. + Con 9+ dígitos, heurística: tomar 8 iniciales (p. ej. 9.º = 0 colado) y + validar luego con `_parse_date_with_format`; con más de 8 sobrantes, + 8 iniciales. + """ + if _is_nullish_scalar(raw): + return None + s = str(raw).strip() + if s.lower() in ("nan", "none", "nat", ""): + return None + date_part, _ = _split_date_time(s) + body = date_part if date_part else s + d = re.sub(r"[^\d]", "", body) + if not d: + return None + n = len(d) + if n in (6, 8): + return d + if n < 6 or n == 7: + return None + # 9+ dígitos: 8 iniciales (p. ej. 9.º 0 sobrante, o ruido al final) + return d[:8] + diff --git a/services/cleaning/domain/csv_cleaning/documents.py b/services/cleaning/domain/csv_cleaning/documents.py new file mode 100644 index 0000000..a32e3ce --- /dev/null +++ b/services/cleaning/domain/csv_cleaning/documents.py @@ -0,0 +1,175 @@ +"""Homologación y división de tipo/número de documento.""" +from __future__ import annotations + +import re +import unicodedata +from typing import Any, Optional + +import pandas as pd + +from .nullish import _is_nullish_scalar + + +_DOC_TYPE_ALIASES: dict[str, tuple[str, ...]] = { + "CC": ("CC", "Cedula", "CD", "C", "C.C", "C C", "CCC"), + "TI": ("TI", "Tarjeta", "Tarjeta Identidad", "T1", "T", "TL"), + "CN": ("CN", "Certificado", "Nacido Vivo", "Certificado Nacido Vivo"), + "CE": ("CE", "Cedula de Extranjeria", "Extranjera"), + "ME": ("ME", "Menor sin Identificacion"), + "PS": ("PS", "Pasaporte"), + "PT": ("PT", "Permiso por proteccion Temporal"), + "RC": ("RC", "Registro Civil", "Registro", "TC"), + "SC": ("SC", "Salvo Conducto", "Salvoconducto"), + "PE": ("PE", "Permiso especial de permanencia", "Permiso"), +} + + +def _ascii_upper(value: Any) -> str: + text = unicodedata.normalize("NFKD", str(value or "")) + text = "".join(c for c in text if not unicodedata.combining(c)) + return text.upper() + + +def _normalize_doc_type_key(value: Any) -> str: + """Clave comparable: sin acentos, espacios, puntos ni símbolos.""" + return re.sub(r"[^A-Z0-9]", "", _ascii_upper(value)) + + +def _clean_doc_type_text(value: Any) -> Optional[str]: + cleaned = re.sub(r"[^A-Z0-9]+", " ", _ascii_upper(value)).strip() + cleaned = re.sub(r"\s+", " ", cleaned) + return cleaned or None + + +_DOC_TYPE_ALIAS_BY_KEY: dict[str, str] = { + _normalize_doc_type_key(alias): canonical + for canonical, aliases in _DOC_TYPE_ALIASES.items() + for alias in aliases +} + +_DOC_TYPE_ALIAS_DISPLAY_BY_KEY: dict[str, str] = {} +for _canonical, _aliases in _DOC_TYPE_ALIASES.items(): + for _alias in _aliases: + _alias_key = _normalize_doc_type_key(_alias) + _alias_display = _clean_doc_type_text(_alias) + if _alias_key and _alias_display: + _DOC_TYPE_ALIAS_DISPLAY_BY_KEY.setdefault(_alias_key, _alias_display) + +_DOC_TYPE_ALIAS_KEYS_SORTED: list[str] = sorted( + _DOC_TYPE_ALIAS_BY_KEY, + key=lambda k: (len(k), k), + reverse=True, +) + + +def homologate_doc_type(value: Any) -> Optional[str]: + """Homologa un tipo documental a sigla; si no existe, conserva texto limpio.""" + if _is_nullish_scalar(value): + return None + key = _normalize_doc_type_key(value) + if not key or key.lower() in ("nan", "none", "nat"): + return None + return _DOC_TYPE_ALIAS_BY_KEY.get(key) or _clean_doc_type_text(value) + + +def _compact_doc_text(value: Any) -> str: + return _normalize_doc_type_key(value) + + +def _looks_like_short_alias_prefix(compact: str, alias_key: str) -> bool: + """Evita que alias de 1-2 chars capturen palabras como CODIGO o TOTAL.""" + if not compact.startswith(alias_key): + return False + rest = compact[len(alias_key):] + if not rest: + return True + return bool(rest[0].isdigit()) + + +def _match_known_doc_type_prefix(compact: str) -> Optional[tuple[str, str]]: + for alias_key in _DOC_TYPE_ALIAS_KEYS_SORTED: + if len(alias_key) <= 2: + if not _looks_like_short_alias_prefix(compact, alias_key): + continue + elif not compact.startswith(alias_key): + continue + return _DOC_TYPE_ALIAS_BY_KEY[alias_key], alias_key + return None + + +def split_doc_tipo_y_numero_texto( + raw: Any, + *, + homologate: bool = True, +) -> tuple[Optional[str], Optional[str]]: + """Separa tipo documental y número, tolerando nombres largos y símbolos. + + Si `homologate` es True, tipos conocidos se devuelven como sigla (CC, TI, + etc.). Los tipos desconocidos se conservan limpios en mayúsculas. + """ + if _is_nullish_scalar(raw) and not (isinstance(raw, str) and not str(raw).strip()): + return (None, None) + t = str(raw).strip() + if t.lower() in ("nan", "none", "nat", ""): + return (None, None) + if t and t[0].isdigit(): + return (None, None) + + compact = _compact_doc_text(t) + if not compact: + return (None, None) + + match = _match_known_doc_type_prefix(compact) + if match is not None: + canonical, alias_key = match + rest = compact[len(alias_key):] + nums = re.sub(r"[^\d]", "", rest) + if homologate: + return (canonical, nums if nums else None) + raw_type = _DOC_TYPE_ALIAS_DISPLAY_BY_KEY.get(alias_key) or canonical + return (raw_type, nums if nums else None) + + m = re.match( + r"^([^\d]+?)\s*([0-9].*)\s*$", + t, + ) + if m: + to_t = _clean_doc_type_text(m.group(1)) + nums = re.sub(r"[^\d]", "", m.group(2)) + return (homologate_doc_type(to_t) if homologate else to_t, nums if nums else None) + + if not re.search(r"\d", t): + to_t = homologate_doc_type(t) if homologate else _clean_doc_type_text(t) + return (to_t, None) + return (None, None) + + +def _alloc_doc_split_column_names( + col_name: str, existing: set[str] +) -> tuple[str, str]: + """Nombres únicos para las dos columnas derivadas.""" + base = re.sub(r"[^\w]+", "_", str(col_name).strip(), flags=re.ASCII) or "col" + for i in range(0, 200): + suf = f"_{i}" if i else "" + t, n = f"{base}{suf}_doc_tipo", f"{base}{suf}_doc_numeros" + if t not in existing and n not in existing: + return t, n + raise ValueError("No se pudieron asignar nombres de columnas únicos.") + + +def _apply_doc_id_split_columns( + result: pd.DataFrame, col: str, log: list[str], *, homologate_type: bool = True +) -> None: + tcol, ncol = _alloc_doc_split_column_names(str(col), set(result.columns)) + st_tipo: list[Optional[str]] = [] + st_num: list[Any] = [] + for v in result[col].tolist(): + tt, nn = split_doc_tipo_y_numero_texto(v, homologate=homologate_type) + st_tipo.append(tt) + st_num.append(nn if nn is not None else pd.NA) + result[tcol] = st_tipo + result[ncol] = st_num + log.append( + f"[OK] '{str(col)}' -> columnas '{tcol}' y '{ncol}' <- Dividir tipo y número (documento)" + ) + diff --git a/services/cleaning/domain/csv_cleaning/io.py b/services/cleaning/domain/csv_cleaning/io.py new file mode 100644 index 0000000..a62380f --- /dev/null +++ b/services/cleaning/domain/csv_cleaning/io.py @@ -0,0 +1,132 @@ +"""Carga robusta de CSV (delimitador, encoding).""" +from __future__ import annotations + +import csv as _csv +from collections import Counter +from typing import Optional + +import pandas as pd + + +_CANDIDATE_DELIMITERS = ["|", ";", "\t", ",", ":"] + + +def detect_delimiter(filepath: str) -> str: + """Detecta el delimitador del CSV de forma robusta. + + Estrategia: + 1. Intentar `csv.Sniffer` con un sample grande. + 2. Si falla o el resultado parece incorrecto, contar ocurrencias por línea + de cada delimitador candidato y elegir el que produce el mayor número + de columnas estable entre líneas. + """ + encodings = ("utf-8", "utf-8-sig", "latin-1", "cp1252") + sample = "" + for enc in encodings: + try: + with open(filepath, "r", encoding=enc, errors="replace") as f: + sample = f.read(65536) + break + except Exception: + continue + + if not sample: + return "," + + try: + dialect = _csv.Sniffer().sniff(sample, delimiters="".join(_CANDIDATE_DELIMITERS)) + sniffed = dialect.delimiter + except _csv.Error: + sniffed = None + + lines = [ln for ln in sample.splitlines() if ln.strip()] + if not lines: + return sniffed or "," + + # Para cada candidato, calcular el número promedio de columnas y la + # consistencia (cuántas líneas tienen el mismo conteo dominante). + scores: dict[str, tuple[int, int]] = {} + for delim in _CANDIDATE_DELIMITERS: + counts = [ln.count(delim) for ln in lines[:50]] + if max(counts) == 0: + continue + # Modo (valor más frecuente) + common = Counter(counts).most_common(1)[0] + mode_count, mode_freq = common + if mode_count == 0: + continue + scores[delim] = (mode_count, mode_freq) + + if scores: + # Elegir el delimitador con mayor número de columnas (mode_count) y, + # como desempate, mayor consistencia (mode_freq). + best = max(scores.items(), key=lambda kv: (kv[1][0], kv[1][1])) + best_delim = best[0] + # Si el sniffer dio algo razonable y coincide, prevalece; si difiere y + # nuestro candidato es claramente mejor, usamos el nuestro. + if sniffed and sniffed in scores: + sniffed_score = scores[sniffed] + if sniffed_score[0] >= best[1][0] - 1: + return sniffed + return best_delim + + return sniffed or "," + + +def load_csv(filepath: str) -> tuple[pd.DataFrame, str]: + """Carga un CSV con detección de delimitador y tolerancia a filas malas. + + Devuelve `(df, delimiter)`. Lanza excepción si todos los intentos fallan. + """ + delimiter = detect_delimiter(filepath) + + encodings_to_try = ["utf-8", "utf-8-sig", "latin-1", "cp1252"] + last_error: Optional[Exception] = None + + for enc in encodings_to_try: + try: + df = pd.read_csv( + filepath, + delimiter=delimiter, + dtype=str, + encoding=enc, + engine="python", + on_bad_lines="skip", + ) + return df, delimiter + except Exception as exc: # noqa: BLE001 + last_error = exc + continue + + # Último intento con quoting permisivo y sin separador detectado + try: + df = pd.read_csv( + filepath, + sep=None, + dtype=str, + engine="python", + on_bad_lines="skip", + ) + return df, "(auto)" + except Exception as exc: # noqa: BLE001 + last_error = exc + + raise RuntimeError(f"No se pudo leer el CSV: {last_error}") + + +def load_csv_bytes(content: bytes, filename: str = "upload.csv") -> tuple[pd.DataFrame, str]: + """Carga un CSV desde bytes (upload HTTP) vía archivo temporal.""" + import os + import tempfile + + suffix = os.path.splitext(filename)[1] or ".csv" + with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp: + tmp.write(content) + tmp_path = tmp.name + try: + return load_csv(tmp_path) + finally: + try: + os.unlink(tmp_path) + except OSError: + pass diff --git a/services/cleaning/domain/csv_cleaning/models.py b/services/cleaning/domain/csv_cleaning/models.py new file mode 100644 index 0000000..2940813 --- /dev/null +++ b/services/cleaning/domain/csv_cleaning/models.py @@ -0,0 +1,67 @@ +"""Tipos de columna y resumen de análisis.""" +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Optional + +TYPE_INTEGER = "integer" +TYPE_DECIMAL = "decimal" +TYPE_DATE = "date" +TYPE_BOOLEAN = "boolean" +TYPE_CATEGORICAL = "categorical" +TYPE_TEXT = "text" +TYPE_MIXED = "mixed" +TYPE_EMPTY = "empty" + + +@dataclass +class ColumnSummary: + """Resumen inteligente para una columna del DataFrame.""" + name: str + inferred_type: str + example: str = "" + samples: list[str] = field(default_factory=list) + null_count: int = 0 + null_pct: float = 0.0 + unique_count: int = 0 + unique_pct: float = 0.0 + min: Optional[float] = None + max: Optional[float] = None + mean: Optional[float] = None + avg_length: Optional[float] = None + top_values: list[tuple[str, int]] = field(default_factory=list) + detected_date_format: Optional[str] = None + applicable_rules: list[str] = field(default_factory=list) + + def info_text(self) -> str: + """Texto humano con información adicional (para subtítulo de la UI).""" + parts: list[str] = [] + parts.append(f"nulos: {self.null_count} ({self.null_pct:.0f}%)") + parts.append(f"únicos: {self.unique_count}") + if self.inferred_type in (TYPE_INTEGER, TYPE_DECIMAL): + if self.min is not None: + parts.append(f"min: {_fmt_num(self.min)}") + if self.max is not None: + parts.append(f"max: {_fmt_num(self.max)}") + if self.mean is not None: + parts.append(f"media: {_fmt_num(self.mean)}") + elif self.inferred_type == TYPE_DATE: + if self.detected_date_format: + parts.append(f"formato: {self.detected_date_format}") + elif self.inferred_type in (TYPE_TEXT, TYPE_CATEGORICAL): + if self.avg_length is not None: + parts.append(f"long. prom: {self.avg_length:.1f}") + if self.top_values: + tops = ", ".join(f'"{v}" ({c})' for v, c in self.top_values[:3]) + parts.append(f"top: {tops}") + return " · ".join(parts) + + +def _fmt_num(x: float) -> str: + if x is None: + return "" + if isinstance(x, float) and x.is_integer(): + return str(int(x)) + if isinstance(x, float): + return f"{x:.2f}" + return str(x) diff --git a/services/cleaning/domain/csv_cleaning/notas.py b/services/cleaning/domain/csv_cleaning/notas.py new file mode 100644 index 0000000..84ad67a --- /dev/null +++ b/services/cleaning/domain/csv_cleaning/notas.py @@ -0,0 +1,73 @@ +"""Extracción de documento y autorización desde notas de texto.""" +from __future__ import annotations + +import re +from typing import Any, Optional + +import pandas as pd + +from .nullish import _is_nullish_scalar + + +def _normalize_nota_text(raw: Any) -> str: + if _is_nullish_scalar(raw): + return "" + try: + if pd.isna(raw): + return "" + except TypeError: + pass + s = str(raw).strip() + if not s or s.lower() in ("nan", "none", "nat", ""): + return "" + return re.sub(r"\s+", " ", s) + + +def extract_paciente_documento_nota(raw: Any) -> Optional[str]: + """Número de documento tras ``Paciente:`` y antes del guión del nombre.""" + t = _normalize_nota_text(raw) + if not t: + return None + m = _PACIENTE_DOC_NOTA_RE.search(t) + return m.group(1) if m else None + + +def extract_autorizacion_nota(raw: Any) -> Optional[str]: + """Número tras ``Autorización No.`` (opcional por fila).""" + t = _normalize_nota_text(raw) + if not t: + return None + m = _AUTORIZACION_NOTA_RE.search(t) + return m.group(1) if m else None + + +def _alloc_nota_extract_column_names( + col_name: str, existing: set[str] +) -> tuple[str, str]: + """Nombres únicos para documento paciente y autorización.""" + base = re.sub(r"[^\w]+", "_", str(col_name).strip(), flags=re.ASCII) or "col" + for i in range(0, 200): + suf = f"_{i}" if i else "" + dcol, acol = f"{base}{suf}_nota_paciente_doc", f"{base}{suf}_nota_autorizacion" + if dcol not in existing and acol not in existing: + return dcol, acol + raise ValueError("No se pudieron asignar nombres de columnas únicos.") + + +def _apply_nota_extract_columns(result: pd.DataFrame, col: str, log: list[str]) -> None: + """Añade columnas con documento (Paciente:) y número de autorización si existe.""" + dcol, acol = _alloc_nota_extract_column_names(str(col), set(result.columns)) + docs: list[Any] = [] + auths: list[Any] = [] + for v in result[col].tolist(): + dd = extract_paciente_documento_nota(v) + aa = extract_autorizacion_nota(v) + docs.append(dd if dd is not None else pd.NA) + auths.append(aa if aa is not None else pd.NA) + result[dcol] = docs + result[acol] = auths + log.append( + f"[OK] '{str(col)}' -> columnas '{dcol}' y '{acol}' <- Extraer datos de notas" + ) + + diff --git a/services/cleaning/domain/csv_cleaning/nullish.py b/services/cleaning/domain/csv_cleaning/nullish.py new file mode 100644 index 0000000..65638a8 --- /dev/null +++ b/services/cleaning/domain/csv_cleaning/nullish.py @@ -0,0 +1,24 @@ +"""Detección de valores nulos / vacíos.""" +from __future__ import annotations + +from typing import Any + +import numpy as np +import pandas as pd + + +def _is_nullish_scalar(x: Any) -> bool: + if x is None or x is pd.NA: + return True + if isinstance(x, (bool, np.bool_)): + return False + if isinstance(x, (int, float, np.integer, np.floating)) and not isinstance( + x, (bool, np.bool_) + ): + if isinstance(x, float) and (np.isnan(x) or pd.isna(x)): + return True + return False + t = str(x).strip().lower() + if not t or t in ("nan", "none", "nat", "null", ""): + return True + return False diff --git a/services/cleaning/domain/csv_cleaning/numbers.py b/services/cleaning/domain/csv_cleaning/numbers.py new file mode 100644 index 0000000..e2051d7 --- /dev/null +++ b/services/cleaning/domain/csv_cleaning/numbers.py @@ -0,0 +1,258 @@ +"""Parsing numérico tolerante (miles, decimales, moneda).""" +from __future__ import annotations + +import re +from typing import Any, Literal, Optional + +import numpy as np +import pandas as pd + +from .nullish import _is_nullish_scalar + +_HINT_MAJORITY = 0.6 +_MIN_HINT_VOTES = 1 + + +def _strip_currency(s: str) -> str: + t = s.replace("\u00a0", " ").replace("\u202f", " ") + t = re.sub(r"[\s]+", "", t) + t = t.strip() + if not t or t.lower() in ("nan", "none", "nat"): + return "" + neg = False + if t[0:1] == "-": + neg, t = not neg, t[1:].lstrip() + if t[0:1] == "(" and t.endswith(")"): + neg, t = not neg, t[1:-1] + t = re.sub( + r"^[\$€\u00a3\u0e3f\uffe5\uffe6\uff04\ufe69]+", + "", + t, + count=1, + ) + t = re.sub(r"[\$€%]+$", "", t) + if not t and neg: + return "-0" + return ("-" if neg else "") + t + + +def _extract_numeric_chunk(s: str) -> str: + t = s.strip() + t = t.replace("$", "").replace("€", "").replace("\u00a3", "") + t = t.replace(" ", "") + cands1 = re.findall( + r"[+\-]?(?:\d{1,3}(?:[.,]\d{3})*|\d+)(?:[.,]\d+)?", t + ) + cands2 = re.findall( + r"[+\-]?\d[.,\d ]*\d", t + ) + cands = cands1 + cands2 + if not cands: + m = re.search(r"[\d.,\-]+", t) + if m: + c = m.group(0).replace("(", "-").replace(")", "") + return c.lstrip("+") + return "" + best = max(cands, key=len) if cands else "" + best = best.replace(" ", "").replace("(", "-").replace(")", "") + if not re.search(r"\d", best): + return "" + return best.lstrip("+") + + +def _as_float_s(s: str) -> Optional[float]: + if not s: + return None + try: + f = float(s) + if not np.isfinite(f) or abs(f) > 1e100: + return None + return f + except (ValueError, TypeError, OverflowError): + return None + + +def _parse_both_separators(body: str) -> Optional[float]: + """El separador más a la DERECHA entre `,` y `.` es el decimal; el otro, miles.""" + if "," not in body or "." not in body: + return None + t = body.strip() + if re.search(r"[^0-9,.\-]", t): + t2 = re.sub(r"[^0-9,.\-]", "", t) + t = t2 + if not t: + return None + neg = t[0:1] == "-" + b = t[1:].lstrip() if neg else t + if not b: + return None + i_comma, i_dot = b.rfind(","), b.rfind(".") + if min(i_comma, i_dot) < 0: + return None + dpos = max(i_comma, i_dot) + th = "," if b[dpos] == "." else "." + intp, fr = b[:dpos], b[dpos + 1 :] + if not re.fullmatch(r"\d{1,4}$", fr): + return None + intc = intp.replace(th, "") + if not intc.isdigit(): + return None + v = _as_float_s(f"{intc}.{fr}") + if v is None: + return None + if neg and v and v is not None: + return -abs(v) + return v + + +def _only_commas(w: str, dec_hint: Optional[Literal[",", "."]]) -> Optional[float]: + neg = w[0:1] == "-" + wn = w[1:].lstrip() if neg else w + if wn and re.search(r"[^0-9,]", wn): + wn = re.sub(r"[^0-9,]", "", wn) + n = wn.count(",") + if n == 0 and wn.isdigit(): + v = float(wn) + return -v if neg else v + if n == 0 or "." in wn: + return None + if n == 1: + i = wn.rfind(",") + h, t = wn[:i], wn[i + 1 :] + if not t.isdigit() or not h.isdigit(): + return None + if len(t) in (1, 2): + v = _as_float_s(f"{h}.{t}") + if v is None: + return None + return -v if neg else v + if len(t) == 3: + if dec_hint is None or dec_hint == ",": + v = _as_float_s(f"{h}{t}") + if v is None: + return None + return -v if neg else v + if dec_hint == ".": + v = _as_float_s(wn.replace(",", "")) + if v is None: + return None + return -v if neg else v + v = _as_float_s(wn.replace(",", "")) + if v is None: + return None + return -v if neg else v + + +def _only_dots(w: str, dec_hint: Optional[Literal[",", "."]]) -> Optional[float]: + neg = w[0:1] == "-" + wn = w[1:].lstrip() if neg else w + if wn and re.search(r"[^0-9.]", wn): + wn = re.sub(r"[^0-9.]", "", wn) + n = wn.count(".") + if n == 0 and wn.isdigit(): + v = float(wn) + return -v if neg else v + if n == 0 or "," in wn: + return None + if n == 1: + i = wn.rfind(".") + h, t = wn[:i], wn[i + 1 :] + if not t.isdigit() or not h.isdigit(): + return None + if len(t) in (1, 2): + v = _as_float_s(f"{h}.{t}") + if v is None: + return None + return -v if neg else v + if len(t) == 3: + if dec_hint is None or dec_hint == ".": + v = _as_float_s(f"{h}{t}") + if v is None: + return None + return -v if neg else v + if dec_hint == ",": + v = _as_float_s(wn.replace(".", "")) + if v is None: + return None + return -v if neg else v + v = _as_float_s(wn.replace(".", "")) + if v is None: + return None + return -v if neg else v + + +def _parse_lenient_one( + raw: Any, + decimal_hint: Optional[Literal[",", "."]] = None, +) -> float: + if _is_nullish_scalar(raw): + return float("nan") + t = _strip_currency(str(raw)) + if not t or t == "-": + return float("nan") + c = _extract_numeric_chunk(t) or t + c = c.strip() + if not c or c == "-": + return float("nan") + n_comma, n_dot = c.count(","), c.count(".") + if n_comma and n_dot: + f = _parse_both_separators(c) + elif n_comma and not n_dot: + f = _only_commas(c, decimal_hint) + elif n_dot and not n_comma: + f = _only_dots(c, decimal_hint) + else: + c2 = re.sub(r"[^0-9.\-]", "", c) + f = _as_float_s(c2) if c2 and re.fullmatch(r"-?\d+$", c2) else None + if f is not None and np.isfinite(f): + return f + return float("nan") + + +def _infer_decimal_hint_from_column( + series: pd.Series, +) -> Optional[Literal[",", "."]]: + """Voto con celdas que traen `,` y `.`: gana el de más a la derecha = decimal.""" + votes: list[Literal[",", "."]] = [] + for raw in series.dropna(): + if _is_nullish_scalar(raw): + continue + t = _strip_currency(str(raw)) + c = _extract_numeric_chunk(t) or t + c = c.strip() + if "," not in c or "." not in c or _parse_both_separators(c) is None: + continue + if c.rfind(",") > c.rfind("."): + votes.append(",") + else: + votes.append(".") + n = len(votes) + if n < _MIN_HINT_VOTES: + return None + a = sum(1 for v in votes if v == ",") + b = sum(1 for v in votes if v == ".") + if a + b == 0: + return None + if a / n >= _HINT_MAJORITY and a > b: + return "," + if b / n >= _HINT_MAJORITY and b > a: + return "." + return None + + +def _to_numeric_lenient( + series: pd.Series, + decimal_hint: Optional[Literal[",", "."]] = None, +) -> pd.Series: + """Convierte a flotante; pista de columna por voto (si `decimal_hint` es None).""" + hint = decimal_hint if decimal_hint is not None else _infer_decimal_hint_from_column( + series + ) + + def _cell(x: Any) -> float: + if _is_nullish_scalar(x): + return float("nan") + return _parse_lenient_one(x, hint) + + return series.map(_cell) + diff --git a/services/cleaning/domain/csv_cleaning/rules/__init__.py b/services/cleaning/domain/csv_cleaning/rules/__init__.py new file mode 100644 index 0000000..071ae2e --- /dev/null +++ b/services/cleaning/domain/csv_cleaning/rules/__init__.py @@ -0,0 +1,13 @@ +"""Reglas de limpieza registradas.""" +from __future__ import annotations + +from . import builtins as _builtins # noqa: F401 — registra reglas al importar +from .registry import CLEANING_RULES, get_rule, get_rule_fn, get_rule_name, register_rule + +__all__ = [ + "CLEANING_RULES", + "get_rule", + "get_rule_fn", + "get_rule_name", + "register_rule", +] diff --git a/services/cleaning/domain/csv_cleaning/rules/builtins.py b/services/cleaning/domain/csv_cleaning/rules/builtins.py new file mode 100644 index 0000000..2be0016 --- /dev/null +++ b/services/cleaning/domain/csv_cleaning/rules/builtins.py @@ -0,0 +1,135 @@ +"""Registro de todas las reglas built-in. + +Añadir una regla nueva: implementa la función y llama a register_rule abajo. +""" +from __future__ import annotations + +from . import dates as date_rules +from . import documents as doc_rules +from . import numeric as num_rules +from . import special +from . import text as text_rules +from .registry import rebuild_cleaning_rules, register_rule + +register_rule( + "strip_special_spaces", + name="Eliminar caracteres especiales y espacios", + description=( + "Deja solo caracteres alfanuméricos (A-Z, a-z, 0-9). No usar antes de " + "«Normalizar fecha» en columnas con fecha y hora: pega cifras y puede impedir " + "interpretar el valor (aplique primero la normalización de fecha o use la regla " + "que conserva separadores)." + ), + fn=text_rules._rule_strip_special_spaces, +) +register_rule( + "strip_special_keep_date_seps", + name="Quitar símbolos; conserva /, -, : y espacios (fechas o texto con fechas)", + description=( + "Quita comas, $, comillas, etc. y deja letras, números, /, -, : (hora) y espacios. " + "Adecuado p. ej. en columna 'Fecha Servicio'." + ), + fn=text_rules._rule_strip_special_keep_date_seps, +) +register_rule( + "strip_spaces", + name="Eliminar únicamente espacios", + description="Elimina todos los caracteres de espacio en blanco.", + fn=text_rules._rule_strip_spaces, +) +register_rule( + "to_integer", + name="Convertir a Entero", + description=( + "Convierte a enteros con detección de miles/decimal (`,`/`.`) y moneda; " + "redondea decimales." + ), + fn=num_rules._rule_to_integer, +) +register_rule( + "date_to_ddmmyyyy", + name="Normalizar fecha → DD/MM/AAAA", + description=( + "Normaliza fechas numéricas, con separadores, con ruido o con meses en " + "texto (ej. 26-abril-2026) al formato DD/MM/AAAA." + ), + fn=date_rules._rule_date_to_ddmmyyyy, +) +register_rule( + "date_numeric_to_ddmmyyyy", + name="Conversión de formato numérico (Fechas) → DD/MM/AAAA", + description=( + "Detecta el formato numérico predominante (YYYYMMDD, DDMMYYYY, etc.) " + "y normaliza a DD/MM/AAAA." + ), + fn=date_rules._rule_date_numeric_to_ddmmyyyy, +) +register_rule( + "date_alpha_to_ddmmyyyy", + name="Conversión de formato alfanumérico (Fechas) → DD/MM/AAAA", + description=( + "Detecta el formato con separadores predominante " + "(DD/MM/YYYY, MM/DD/YYYY, YYYY/MM/DD, etc.) y normaliza a DD/MM/AAAA." + ), + fn=date_rules._rule_date_alpha_to_ddmmyyyy, +) +register_rule( + "date_messy_text_to_ddmmyyyy", + name="Texto a fecha (ruido en fechas) → DD/MM/AAAA", + description=( + "Quita ruido entre cifras (*, +, _, /, etc.), detecta el orden en la " + "columna (DDMMYYYY, YYYYMMDD, DDMMYY) y unifica a DD/MM/AAAA." + ), + fn=date_rules._rule_date_messy_text_to_ddmmyyyy, +) +register_rule( + "round_decimals_custom", + name="Aproximación Decimales (>0,5 sube, ≤0,5 baja)", + description=( + "Usa el mismo análisis de miles, decimales y moneda que «Entero». Tras leer " + "el número, si la parte fraccionaria de |valor| es mayor a 0,5 se aproxima al " + "entero hacia lejos de cero; en caso contrario, hacia cero. El 0,5 exacto se baja." + ), + fn=num_rules._rule_round_decimals_custom, +) +register_rule( + "doc_id_split_tipo_numero", + name="Dividir tipo y número (documento)", + description=( + "Añade dos columnas (nombres únicos p. ej. TipoID_doc_tipo, TipoID_doc_numeros): " + "prefijo con letras (NIT, CC, CE, …) y cifras sin separadores. No modifica la " + "columna de origen." + ), + fn=special._rule_passthrough_series, +) +register_rule( + "doc_type_homologate", + name="Homologar tipo de documento", + description=( + "Convierte tipos de documento escritos como nombre largo, sigla o texto con " + "símbolos a su abreviatura estándar (CC, TI, CE, RC, etc.)." + ), + fn=doc_rules._rule_doc_type_homologate, +) +register_rule( + "cop_mod_split", + name="Separa COP/MOD (copago / moderadora)", + description=( + "Según el año en la columna Fecha Servicio que elija en la tarjeta, clasifica " + "el importe en cuota moderadora (valores por año en Configuración) o copago. " + "Se ejecuta al final del proceso de limpieza para usar fechas ya normalizadas." + ), + fn=special._rule_passthrough_series, +) +register_rule( + "nota_paciente_autorizacion", + name="Extraer datos de notas (documento y autorización)", + description=( + "Desde texto tipo nota hospitalaria, extrae el número tras «Paciente:» (antes del " + "guión del nombre) y, si existe, el de «Autorización No.». Añade dos columnas; " + "no modifica el texto original." + ), + fn=special._rule_passthrough_series, +) + +rebuild_cleaning_rules() diff --git a/services/cleaning/domain/csv_cleaning/rules/dates.py b/services/cleaning/domain/csv_cleaning/rules/dates.py new file mode 100644 index 0000000..07d645c --- /dev/null +++ b/services/cleaning/domain/csv_cleaning/rules/dates.py @@ -0,0 +1,141 @@ +"""Reglas de normalización de fechas a DD/MM/AAAA.""" +from __future__ import annotations + +from typing import Any, Optional + +import pandas as pd + +from ..dates import ( + _detect_compact_date_formats, + _normalize_messy_date_to_compact, + _parse_any_date_to_tuple, + _parse_date_with_format, + _parse_date_with_text_month, + detect_date_format, +) +from ..nullish import _is_nullish_scalar + + +def _rule_date_to_ddmmyyyy(series: pd.Series) -> pd.Series: + """Normaliza fechas numéricas, alfanuméricas, con meses en texto y con ruido.""" + stripped = series.astype(str).str.replace(r"[\s\/\-\.]", "", regex=True) + stripped = stripped.where(series.notna(), other=pd.NA) + sep_fmt = detect_date_format(series) or detect_date_format(stripped) + compact8_fmt, compact6_fmt = _detect_compact_date_formats(series) + + def _fn(x: Any): + if pd.isna(x) or str(x).strip() == "": + return x + parsed = _parse_any_date_to_tuple( + x, + sep_fmt=sep_fmt, + compact8_fmt=compact8_fmt, + compact6_fmt=compact6_fmt, + ) + if parsed is None: + return x + d, m, y = parsed + return f"{d:02d}/{m:02d}/{y:04d}" + + return series.map(_fn) + + +def _rule_date_numeric_to_ddmmyyyy(series: pd.Series) -> pd.Series: + """Entrada: fechas en formato numérico (ej. 20240315 o 15032024). + + Salida: "DD/MM/AAAA" tras detectar el formato predominante. + """ + # Para esta regla consideramos los valores "numéricos" (quitamos separadores + # comunes si los hay para detectar un YYYYMMDD/DDMMYYYY confiable). + stripped = series.astype(str).str.replace(r"[\s\/\-\.]", "", regex=True) + stripped = stripped.where(series.notna(), other=pd.NA) + fmt = detect_date_format(stripped) + if fmt is None: + # fallback: intentar con detector normal + fmt = detect_date_format(series) + return _format_dates_to_ddmmyyyy(series, fmt) + + +def _rule_date_alpha_to_ddmmyyyy(series: pd.Series) -> pd.Series: + """Entrada: fechas en formato alfanumérico con separadores. + + Salida: "DD/MM/AAAA". + """ + fmt = detect_date_format(series) + return _format_dates_to_ddmmyyyy(series, fmt) + + +def _format_dates_to_ddmmyyyy(series: pd.Series, fmt: Optional[str]) -> pd.Series: + """Convierte cada valor al formato DD/MM/AAAA usando `fmt` como layout de + entrada detectado. La parte de hora (si existe) se descarta. Mantiene el + valor original si no puede parsear la fecha. + """ + if fmt is None: + return series.copy() + + def _fn(x): + if pd.isna(x) or str(x).strip() == "": + return x + raw = str(x) + date_part, _time_part = _split_date_time(raw) + target = date_part if date_part else raw + parsed = _parse_date_with_format(target, fmt) + if parsed is None: + return x + d, m, y = parsed + return f"{d:02d}/{m:02d}/{y:04d}" + + return series.map(_fn) + + +def _rule_date_messy_text_to_ddmmyyyy(series: pd.Series) -> pd.Series: + """Fechas con símbolos y basura entre cifras → DD/MM/AAAA. + + Normaliza a solo 6/8 dígitos, vota el formato con `detect_date_format` por + separado en compact8 y compact6; si el detector no devuelve nada, usa + `DDMMYYYY` / `DDMMYY` (alineado con la preferencia hispana en fechas + numéricas). + """ + cser = series.map(_normalize_messy_date_to_compact) + c8s = pd.Series( + [x for x in cser.tolist() if isinstance(x, str) and len(x) == 8] + ) + c6s = pd.Series( + [x for x in cser.tolist() if isinstance(x, str) and len(x) == 6] + ) + if c8s.empty and c6s.empty: + return series.copy() + + fmt8: Optional[str] = None + fmt6: Optional[str] = None + if not c8s.empty: + fmt8 = detect_date_format(c8s) or "DDMMYYYY" + if not c6s.empty: + fmt6 = detect_date_format(c6s) or "DDMMYY" + + def _one(x: Any, c: Any) -> Any: + if _is_nullish_scalar(x): + return x + if not isinstance(c, str) or len(c) not in (6, 8): + return x + f = (fmt8 or "DDMMYYYY") if len(c) == 8 else (fmt6 or "DDMMYY") + t = _parse_date_with_format(c, f) + if t is None and len(c) == 8: + t = _parse_date_with_format(c, "DDMMYYYY") or _parse_date_with_format( + c, "YYYYMMDD" + ) + if t is None and len(c) == 6: + t = _parse_date_with_format(c, "DDMMYY") + if t is None: + return x + d, m, y = t + return f"{d:02d}/{m:02d}/{y:04d}" + + return pd.Series( + [ + _one(v, cser.iloc[i] if i < len(cser) else None) for i, v in enumerate(series) + ], + index=series.index, + dtype=object, + ) + diff --git a/services/cleaning/domain/csv_cleaning/rules/documents.py b/services/cleaning/domain/csv_cleaning/rules/documents.py new file mode 100644 index 0000000..0fa3fc9 --- /dev/null +++ b/services/cleaning/domain/csv_cleaning/rules/documents.py @@ -0,0 +1,23 @@ +"""Reglas de documento (homologación; split se aplica en apply.py).""" +from __future__ import annotations + +from typing import Any + +import pandas as pd + +from ..documents import homologate_doc_type, split_doc_tipo_y_numero_texto +from ..nullish import _is_nullish_scalar + + +def _rule_doc_type_homologate(series: pd.Series) -> pd.Series: + """Homologa una columna que contiene tipos de documento.""" + + def _fn(x: Any): + if _is_nullish_scalar(x): + return x + tipo, _num = split_doc_tipo_y_numero_texto(x, homologate=True) + if tipo is not None: + return tipo + return homologate_doc_type(x) + + return series.map(_fn) diff --git a/services/cleaning/domain/csv_cleaning/rules/numeric.py b/services/cleaning/domain/csv_cleaning/rules/numeric.py new file mode 100644 index 0000000..be5047e --- /dev/null +++ b/services/cleaning/domain/csv_cleaning/rules/numeric.py @@ -0,0 +1,57 @@ +"""Reglas numéricas (entero, redondeo).""" +from __future__ import annotations + +import math + +import numpy as np +import pandas as pd + +from ..nullish import _is_nullish_scalar +from ..numbers import _to_numeric_lenient + + +def _rule_to_integer(series: pd.Series) -> pd.Series: + """Convierte a entero (Int64 nullable) con parser tolerante.""" + numeric = _to_numeric_lenient(series.astype(str)) + return numeric.round().astype("Int64") + + +def _round_to_int_half_tie_le(x: float) -> int: + """Fracc. > 0,5 hacia arriba; 0,5 o menos hacia abajo. Banda 1e-9 en 0,5 (float). + + Misma noción que en la descripción de la regla. Trabaja sobre |x| y + recompone el signo. + """ + sign = -1 if x < 0 else 1 + if x == 0.0 or x == -0.0: + return 0 + ax = abs(float(x)) + ip = math.trunc(ax) + frac = ax - ip + if frac < 0.5 - 1e-9: + out = ip + elif frac > 0.5 + 1e-9: + out = ip + 1 + else: + out = ip + return -out if sign < 0 else out + + +def _rule_round_decimals_custom(series: pd.Series) -> pd.Series: + """Mismo parser tolerante que convertir a entero; aprox. con fracc. >0,5 arriba.""" + numeric = _to_numeric_lenient(series.astype(str)) + + def _fn(x): + if _is_nullish_scalar(x) or (isinstance(x, (float, np.floating)) and (not np.isfinite(x) or np.isnan(x))): + return pd.NA + try: + f = float(x) + except (TypeError, ValueError, OverflowError): + return pd.NA + if not np.isfinite(f): + return pd.NA + if f == 0.0 or f == -0.0: + return 0 + return _round_to_int_half_tie_le(f) + + return numeric.map(_fn).astype("Int64") diff --git a/services/cleaning/domain/csv_cleaning/rules/registry.py b/services/cleaning/domain/csv_cleaning/rules/registry.py new file mode 100644 index 0000000..d0ad72a --- /dev/null +++ b/services/cleaning/domain/csv_cleaning/rules/registry.py @@ -0,0 +1,80 @@ +"""Registro de reglas de limpieza. + +Para agregar una regla nueva: +1. Implementa ``fn(series: pd.Series) -> pd.Series`` (o passthrough si es especial). +2. Llama a ``register_rule(...)`` en ``rules/builtins.py``. +3. Si la regla es especial (añade columnas, usa config), maneja el id en ``apply.py``. +4. Incluye el id en ``_applicable_rules_for_type`` (type_inference.py) si debe sugerirse. +""" +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, Callable, Optional + +import pandas as pd + +RuleFn = Callable[[pd.Series], pd.Series] + + +@dataclass(frozen=True) +class RuleSpec: + id: str + name: str + description: str + fn: RuleFn + + def as_dict(self) -> dict[str, Any]: + return { + "id": self.id, + "name": self.name, + "description": self.description, + "fn": self.fn, + } + + +_REGISTRY: dict[str, RuleSpec] = {} +_ORDER: list[str] = [] + + +def register_rule( + rule_id: str, + *, + name: str, + description: str, + fn: RuleFn, +) -> RuleSpec: + """Registra una regla. Si el id ya existe, lo reemplaza manteniendo el orden.""" + spec = RuleSpec(id=rule_id, name=name, description=description, fn=fn) + if rule_id not in _REGISTRY: + _ORDER.append(rule_id) + _REGISTRY[rule_id] = spec + return spec + + +def get_rule(rule_id: str) -> Optional[dict[str, Any]]: + spec = _REGISTRY.get(rule_id) + return spec.as_dict() if spec else None + + +def get_rule_fn(rule_id: str) -> Optional[RuleFn]: + spec = _REGISTRY.get(rule_id) + return spec.fn if spec else None + + +def get_rule_name(rule_id: str) -> str: + spec = _REGISTRY.get(rule_id) + return spec.name if spec else rule_id + + +def list_rules() -> list[dict[str, Any]]: + return [_REGISTRY[rid].as_dict() for rid in _ORDER if rid in _REGISTRY] + + +# Compat: lista mutable usada por la API (se rellena al importar builtins). +CLEANING_RULES: list[dict[str, Any]] = [] + + +def rebuild_cleaning_rules() -> list[dict[str, Any]]: + CLEANING_RULES.clear() + CLEANING_RULES.extend(list_rules()) + return CLEANING_RULES diff --git a/services/cleaning/domain/csv_cleaning/rules/special.py b/services/cleaning/domain/csv_cleaning/rules/special.py new file mode 100644 index 0000000..04c1521 --- /dev/null +++ b/services/cleaning/domain/csv_cleaning/rules/special.py @@ -0,0 +1,8 @@ +"""Marcadores passthrough para reglas que no transforman la serie in-place.""" +from __future__ import annotations + +import pandas as pd + + +def _rule_passthrough_series(s: pd.Series) -> pd.Series: + return s.copy() diff --git a/services/cleaning/domain/csv_cleaning/rules/text.py b/services/cleaning/domain/csv_cleaning/rules/text.py new file mode 100644 index 0000000..66e6777 --- /dev/null +++ b/services/cleaning/domain/csv_cleaning/rules/text.py @@ -0,0 +1,40 @@ +"""Reglas de limpieza de texto / espacios.""" +from __future__ import annotations + +import re + +import pandas as pd + + +def _rule_strip_special_spaces(series: pd.Series) -> pd.Series: + """Elimina todo lo que no sea alfanumérico (incluye espacios).""" + pattern = re.compile(r"[^A-Za-z0-9]", flags=re.UNICODE) + + def _fn(x): + if pd.isna(x): + return x + return pattern.sub("", str(x)) + + return series.map(_fn) + + +def _rule_strip_special_keep_date_seps(series: pd.Series) -> pd.Series: + """Conserva alfanuméricos, separadores de fecha '/', '-' y hora ':', y espacio.""" + pattern = re.compile(r"[^A-Za-z0-9\/\-: ]", flags=re.UNICODE) + + def _fn(x): + if pd.isna(x): + return x + return pattern.sub("", str(x)) + + return series.map(_fn) + + +def _rule_strip_spaces(series: pd.Series) -> pd.Series: + """Elimina únicamente espacios (incluidos tabs y saltos de línea).""" + def _fn(x): + if pd.isna(x): + return x + return re.sub(r"\s+", "", str(x)) + + return series.map(_fn) diff --git a/services/cleaning/domain/csv_cleaning/type_inference.py b/services/cleaning/domain/csv_cleaning/type_inference.py new file mode 100644 index 0000000..d85d219 --- /dev/null +++ b/services/cleaning/domain/csv_cleaning/type_inference.py @@ -0,0 +1,143 @@ +"""Inferencia de tipo de columna y reglas aplicables.""" +from __future__ import annotations + +from typing import Optional + +import pandas as pd + +from .column_hints import ( + _column_name_suggests_code_id, + _column_name_suggests_monetary_value, + _column_name_suggests_quantity_or_number_not_bool, + _content_looks_like_doc_id_letter_prefix_column, + _raw_strings_suggest_alnum_mixed_presentation, + _raw_strings_suggest_monetary_presentation, +) +from .dates import _normalize_messy_date_to_compact, detect_date_format +from .models import ( + TYPE_BOOLEAN, + TYPE_CATEGORICAL, + TYPE_DATE, + TYPE_DECIMAL, + TYPE_EMPTY, + TYPE_INTEGER, + TYPE_MIXED, + TYPE_TEXT, +) +from .numbers import _to_numeric_lenient + +_BOOLEAN_VALUES = {"true", "false", "si", "sí", "no", "1", "0", "yes"} + + +def _detect_messy_text_date_format_guess(non_null: pd.Series) -> Optional[str]: + """Fechas con ruido: compacta a dígitos; si hay consenso, reutiliza el detector. + (Depende de `_normalize_messy_date_to_compact`, definida más abajo en el módulo.) + """ + # resolución en tiempo de ejecución + norm = non_null.map(_normalize_messy_date_to_compact) # type: ignore[name-defined] + compacts: list[str] = [] + for c in norm: + if isinstance(c, str) and len(c) in (6, 8): + compacts.append(c) + n = max(len(non_null), 1) + if not compacts or len(compacts) < max(2, int(0.25 * n)): + return None + s_compact = pd.Series(compacts, dtype=object) + return detect_date_format(s_compact) + + +def _infer_column_type( + series: pd.Series, column_name: str = "" +) -> tuple[str, Optional[str]]: + """Devuelve (tipo, formato_fecha detectado; solo si aplica a tipo fecha).""" + non_null = series.dropna().astype(str).str.strip() + non_null = non_null[non_null != ""] + if non_null.empty: + return TYPE_EMPTY, None + + cname = str(column_name or "") + + # Boolean: no aplicar 0/1/ sí-no a columnas cuyo nombre pide importe, cantid., fecha… + if not _column_name_suggests_quantity_or_number_not_bool(cname): + lowered = non_null.str.lower() + if lowered.isin(_BOOLEAN_VALUES).all() and lowered.nunique() <= 3: + return TYPE_BOOLEAN, None + + is_money = _column_name_suggests_monetary_value(cname) or _raw_strings_suggest_monetary_presentation( + non_null + ) + is_code = _column_name_suggests_code_id(cname) + is_doc_pattern = _content_looks_like_doc_id_letter_prefix_column(non_null) + + # Fecha: no forzar en importe/código, ni con datos tipo `CC`+cifra (6–8 dígitos sueltos) + # ni cuando el contenido parece moneda (p. ej. 113,400.00 → no es DDMMYYYY). + if not is_money and not is_code and not is_doc_pattern: + date_fmt = detect_date_format(non_null) + if date_fmt: + return (TYPE_DATE, date_fmt) + messy_date_fmt = _detect_messy_text_date_format_guess(non_null) + if messy_date_fmt: + return (TYPE_DATE, messy_date_fmt) + + numeric_candidate = _to_numeric_lenient(non_null, decimal_hint=None) + numeric_ratio = numeric_candidate.notna().mean() + if numeric_ratio >= 0.9: + if _raw_strings_suggest_alnum_mixed_presentation(non_null): + return (TYPE_MIXED, None) + is_all_integer = (numeric_candidate.dropna() % 1 == 0).all() + if is_all_integer and not _raw_strings_suggest_monetary_presentation(non_null): + return (TYPE_INTEGER, None) + return (TYPE_DECIMAL, None) + + unique_ratio = non_null.nunique() / len(non_null) + if non_null.nunique() <= 15 and unique_ratio < 0.3: + return TYPE_CATEGORICAL, None + if 0.1 < numeric_ratio < 0.9: + return TYPE_MIXED, None + return TYPE_TEXT, None + + +def _applicable_rules_for_type(inferred_type: str) -> list[str]: + """Identificadores de reglas a mostrar según el tipo inferido (sin redundancias).""" + base = ["strip_special_spaces", "strip_special_keep_date_seps", "strip_spaces"] + if inferred_type == TYPE_INTEGER: + return base + ["cop_mod_split"] + if inferred_type == TYPE_DECIMAL: + return base + [ + "to_integer", + "round_decimals_custom", + "doc_id_split_tipo_numero", + "doc_type_homologate", + "cop_mod_split", + ] + if inferred_type == TYPE_DATE: + return base + [ + "doc_id_split_tipo_numero", + "doc_type_homologate", + "date_to_ddmmyyyy", + "nota_paciente_autorizacion", + ] + if inferred_type == TYPE_MIXED: + return base + [ + "to_integer", + "round_decimals_custom", + "doc_id_split_tipo_numero", + "doc_type_homologate", + "date_to_ddmmyyyy", + "cop_mod_split", + "nota_paciente_autorizacion", + ] + # Texto / categórico / boolean / empty → todas las de limpieza de texto + las + # conversoras (el usuario sabrá si aplica). + return base + [ + "to_integer", + "round_decimals_custom", + "doc_id_split_tipo_numero", + "doc_type_homologate", + "date_to_ddmmyyyy", + "cop_mod_split", + "nota_paciente_autorizacion", + ] + + +applicable_rule_ids_for_type = _applicable_rules_for_type diff --git a/services/cleaning/infrastructure/database.py b/services/cleaning/infrastructure/database.py index 697580e..e32fa0e 100644 --- a/services/cleaning/infrastructure/database.py +++ b/services/cleaning/infrastructure/database.py @@ -54,10 +54,26 @@ class ExtractionBatch(Base): updated_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow) +class Job(Base): + """Contenedor de trabajo del usuario (lectura/validación de FK).""" + __tablename__ = "jobs" + + id: Mapped[str] = mapped_column(String(36), primary_key=True, default=new_id) + status: Mapped[str] = mapped_column(String(32), default="pending", index=True) + title: Mapped[str | None] = mapped_column(Text, nullable=True) + job_metadata: Mapped[dict[str, Any]] = mapped_column("metadata", JSON, default=dict) + created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow) + updated_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow) + + class Task(Base): + """Módulo/resultado dentro de un Job; un job puede tener N tasks (una por servicio).""" __tablename__ = "tasks" id: Mapped[str] = mapped_column(String(36), primary_key=True, default=new_id) + job_id: Mapped[str | None] = mapped_column( + String(36), ForeignKey("jobs.id"), nullable=True, index=True + ) type: Mapped[str] = mapped_column(String(64), index=True) status: Mapped[str] = mapped_column(String(32), default="pending", index=True) title: Mapped[str | None] = mapped_column(Text, nullable=True) @@ -88,7 +104,13 @@ class CleaningJob(Base): __tablename__ = "cleaning_jobs" id: Mapped[str] = mapped_column(String(36), primary_key=True, default=new_id) - extraction_batch_id: Mapped[str] = mapped_column(ForeignKey("extraction_batches.id"), index=True) + extraction_batch_id: Mapped[str | None] = mapped_column( + ForeignKey("extraction_batches.id"), nullable=True, index=True + ) + job_id: Mapped[str | None] = mapped_column( + String(36), ForeignKey("jobs.id"), nullable=True, index=True + ) + source: Mapped[str] = mapped_column(String(32), default="extraction", index=True) status: Mapped[str] = mapped_column(String(32), default="pending", index=True) request: Mapped[dict[str, Any]] = mapped_column(JSON, default=dict) log: Mapped[list[str]] = mapped_column(JSON, default=list) @@ -102,7 +124,9 @@ class CleanedRow(Base): id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True) cleaning_job_id: Mapped[str] = mapped_column(ForeignKey("cleaning_jobs.id"), index=True) - extraction_batch_id: Mapped[str] = mapped_column(ForeignKey("extraction_batches.id"), index=True) + extraction_batch_id: Mapped[str | None] = mapped_column( + ForeignKey("extraction_batches.id"), nullable=True, index=True + ) row_index: Mapped[int] = mapped_column(Integer, default=0) row_json: Mapped[dict[str, Any]] = mapped_column(JSON, default=dict) created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow) diff --git a/services/cleaning/requirements.txt b/services/cleaning/requirements.txt index 4c07d5e..e8835ed 100644 --- a/services/cleaning/requirements.txt +++ b/services/cleaning/requirements.txt @@ -3,5 +3,6 @@ numpy==2.2.6 pandas==2.2.3 psycopg[binary]==3.2.3 pydantic==2.11.9 +python-multipart==0.0.20 SQLAlchemy==2.0.36 uvicorn[standard]==0.32.1 diff --git a/services/db/alembic/versions/0003_cleaning_csv_and_job_id.py b/services/db/alembic/versions/0003_cleaning_csv_and_job_id.py new file mode 100644 index 0000000..d592c6c --- /dev/null +++ b/services/db/alembic/versions/0003_cleaning_csv_and_job_id.py @@ -0,0 +1,90 @@ +"""cleaning_jobs: batch_id nullable, job_id, source; cleaned_rows.batch_id nullable. + +Revision ID: 0003 +Revises: 0002 +Create Date: 2026-07-10 + +""" +from __future__ import annotations + +from typing import Sequence, Union + +import sqlalchemy as sa +from alembic import op + +revision: str = "0003" +down_revision: Union[str, None] = "0002" +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def _col_exists(conn: sa.engine.Connection, table: str, column: str) -> bool: + inspector = sa.inspect(conn) + return any(c["name"] == column for c in inspector.get_columns(table)) + + +def upgrade() -> None: + conn = op.get_bind() + + if _col_exists(conn, "cleaning_jobs", "extraction_batch_id"): + op.alter_column( + "cleaning_jobs", + "extraction_batch_id", + existing_type=sa.String(36), + nullable=True, + ) + + if not _col_exists(conn, "cleaning_jobs", "job_id"): + op.add_column( + "cleaning_jobs", + sa.Column("job_id", sa.String(36), sa.ForeignKey("jobs.id"), nullable=True), + ) + op.create_index("ix_cleaning_jobs_job_id", "cleaning_jobs", ["job_id"]) + + if not _col_exists(conn, "cleaning_jobs", "source"): + op.add_column( + "cleaning_jobs", + sa.Column( + "source", + sa.String(32), + nullable=False, + server_default="extraction", + ), + ) + op.create_index("ix_cleaning_jobs_source", "cleaning_jobs", ["source"]) + + if _col_exists(conn, "cleaned_rows", "extraction_batch_id"): + op.alter_column( + "cleaned_rows", + "extraction_batch_id", + existing_type=sa.String(36), + nullable=True, + ) + + +def downgrade() -> None: + conn = op.get_bind() + + if _col_exists(conn, "cleaned_rows", "extraction_batch_id"): + op.alter_column( + "cleaned_rows", + "extraction_batch_id", + existing_type=sa.String(36), + nullable=False, + ) + + if _col_exists(conn, "cleaning_jobs", "source"): + op.drop_index("ix_cleaning_jobs_source", table_name="cleaning_jobs") + op.drop_column("cleaning_jobs", "source") + + if _col_exists(conn, "cleaning_jobs", "job_id"): + op.drop_index("ix_cleaning_jobs_job_id", table_name="cleaning_jobs") + op.drop_column("cleaning_jobs", "job_id") + + if _col_exists(conn, "cleaning_jobs", "extraction_batch_id"): + op.alter_column( + "cleaning_jobs", + "extraction_batch_id", + existing_type=sa.String(36), + nullable=False, + ) diff --git a/services/desktop_app/.env.minio.example b/services/desktop_app/.env.minio.example new file mode 100644 index 0000000..66b6d97 --- /dev/null +++ b/services/desktop_app/.env.minio.example @@ -0,0 +1,11 @@ +# Credenciales locales para publicar en MinIO (NO subir al git). +# Copia este archivo a .env.minio y rellena los valores. + +MINIO_ENDPOINT=https://s3.builker.xyz +MINIO_BUCKET=apps-versions +MINIO_PREFIX=IPSExtractor +MINIO_ACCESS_KEY= +MINIO_SECRET_KEY= + +# Opcional (DD/MM/YYYY). Si se omite, usa version.json o la fecha de hoy. +# RELEASE_DATE=06/07/2026 diff --git a/services/desktop_app/README.md b/services/desktop_app/README.md index c03ce34..32b5a8f 100644 --- a/services/desktop_app/README.md +++ b/services/desktop_app/README.md @@ -106,17 +106,59 @@ npm run dev ## Build para producción ```bash -npm run build # compila el renderer (TypeScript + Vite) -npm run dist # genera el instalador Windows NSIS +# 1) Sube la versión en package.json (ej. 1.0.1) y la fecha en src/shared/version.json +# 2) Configura credenciales una vez: copia .env.minio.example → .env.minio +# 3) Genera el instalador y súbelo a MinIO automáticamente: +npm run dist ``` -El instalador se crea en `release/IPSExtractor-Desktop-1.0.0-Setup.exe`. +El instalador queda en `release/` y se publica en: + +`s3.builker.xyz/apps-versions/IPSExtractor/{version}/` + +Si solo quieres el `.exe` local **sin** subir a MinIO: + +```bash +npm run dist:local +``` ### Versión de la app | Campo | Archivo | |-------|---------| | Número (`1.0.0`) | `package.json` → `version` | -| Fecha de release | `src/shared/version.json` → `releaseDate` | +| Fecha de release | `src/shared/version.json` → `releaseDate` (ISO) | -La versión se muestra en el menú lateral y en la esquina superior derecha. Al publicar una nueva versión, actualiza ambos archivos y ejecuta `npm run dist`. +La versión se muestra en el menú lateral y en la esquina superior derecha. + +### Auto-update (MinIO) + +Al iniciar la app **instalada**, consulta: + +`https://s3.builker.xyz/apps-versions/IPSExtractor/latest.json` + +Si hay una versión mayor, pregunta al usuario, descarga el Setup.exe, verifica `sha256` y lanza el instalador NSIS. + +Layout en el bucket `apps-versions`: + +```text +IPSExtractor/ + latest.json + 1.0.1/IPSExtractor-Desktop-1.0.1-Setup.exe + 1.0.1/DATA.txt ← fecha DD/MM/YYYY, ej. 06/07/2026 +``` + +**Requisito:** el prefijo `IPSExtractor/` debe permitir `GetObject` anónimo en la API (`s3.builker.xyz`). La política se configura en la consola (`minio.builker.xyz`). + +Credenciales de publicación (solo en tu PC): + +```bash +cp .env.minio.example .env.minio +# Edita MINIO_ACCESS_KEY y MINIO_SECRET_KEY +``` + +`npm run dist` las usa automáticamente al final. También puedes republicar sin rebuild: + +```bash +npm run publish:minio +``` diff --git a/services/desktop_app/electron/autoUpdate.cjs b/services/desktop_app/electron/autoUpdate.cjs new file mode 100644 index 0000000..bfc79f6 --- /dev/null +++ b/services/desktop_app/electron/autoUpdate.cjs @@ -0,0 +1,224 @@ +/** + * Auto-update desde MinIO (apps-versions/IPSExtractor). + * Sin credenciales: lee latest.json y el Setup.exe con GetObject público. + */ +const { app } = require("electron"); +const crypto = require("node:crypto"); +const fs = require("node:fs"); +const fsp = require("node:fs/promises"); +const http = require("node:http"); +const https = require("node:https"); +const os = require("node:os"); +const path = require("node:path"); +const { spawn } = require("node:child_process"); +const { URL } = require("node:url"); + +const { version: CURRENT_VERSION } = require("../package.json"); + +const LATEST_JSON_URL = + process.env.IPS_UPDATE_LATEST_URL || + "https://s3.builker.xyz/apps-versions/IPSExtractor/latest.json"; + +const FETCH_TIMEOUT_MS = 12_000; + +/** + * @typedef {{ + * version: string; + * releaseDate?: string; + * releaseDateDisplay?: string; + * fileName: string; + * url: string; + * sha256: string; + * size?: number; + * }} UpdateManifest + */ + +/** + * @param {string} a + * @param {string} b + * @returns {number} -1 if ab + */ +function compareSemver(a, b) { + const pa = String(a).replace(/^v/i, "").split(".").map((x) => Number.parseInt(x, 10) || 0); + const pb = String(b).replace(/^v/i, "").split(".").map((x) => Number.parseInt(x, 10) || 0); + const len = Math.max(pa.length, pb.length); + for (let i = 0; i < len; i++) { + const x = pa[i] ?? 0; + const y = pb[i] ?? 0; + if (x < y) return -1; + if (x > y) return 1; + } + return 0; +} + +/** + * @param {string} urlStr + * @param {{ onProgress?: (received: number, total: number) => void, destPath?: string }} [opts] + * @returns {Promise<{ buffer?: Buffer, destPath?: string, headers: import('http').IncomingHttpHeaders }>} + */ +function download(urlStr, opts = {}) { + return new Promise((resolve, reject) => { + const url = new URL(urlStr); + const lib = url.protocol === "http:" ? http : https; + const req = lib.get( + url, + { + timeout: FETCH_TIMEOUT_MS, + headers: { "User-Agent": `IPSExtractor-Desktop/${CURRENT_VERSION}` }, + }, + (res) => { + if (res.statusCode && res.statusCode >= 300 && res.statusCode < 400 && res.headers.location) { + res.resume(); + download(res.headers.location, opts).then(resolve, reject); + return; + } + if (res.statusCode !== 200) { + res.resume(); + reject(new Error(`HTTP ${res.statusCode} al descargar ${urlStr}`)); + return; + } + const total = Number(res.headers["content-length"] || 0); + let received = 0; + + if (opts.destPath) { + const out = fs.createWriteStream(opts.destPath); + res.on("data", (chunk) => { + received += chunk.length; + opts.onProgress?.(received, total); + }); + res.pipe(out); + out.on("finish", () => resolve({ destPath: opts.destPath, headers: res.headers })); + out.on("error", reject); + res.on("error", reject); + return; + } + + const chunks = []; + res.on("data", (chunk) => { + chunks.push(chunk); + received += chunk.length; + opts.onProgress?.(received, total); + }); + res.on("end", () => resolve({ buffer: Buffer.concat(chunks), headers: res.headers })); + res.on("error", reject); + }, + ); + req.on("timeout", () => { + req.destroy(); + reject(new Error("Timeout al contactar el servidor de versiones")); + }); + req.on("error", reject); + }); +} + +/** + * @returns {Promise<{ updateAvailable: boolean, currentVersion: string, manifest?: UpdateManifest, error?: string }>} + */ +async function checkForUpdates() { + try { + const { buffer } = await download(LATEST_JSON_URL); + if (!buffer) { + return { updateAvailable: false, currentVersion: CURRENT_VERSION, error: "Respuesta vacía" }; + } + /** @type {UpdateManifest} */ + const manifest = JSON.parse(buffer.toString("utf8")); + if (!manifest?.version || !manifest?.url || !manifest?.fileName) { + return { + updateAvailable: false, + currentVersion: CURRENT_VERSION, + error: "Manifiesto inválido", + }; + } + + // Si falta la fecha de DATA.txt en el manifiesto, intentar leerla junto al exe + if (!manifest.releaseDateDisplay) { + try { + const dataUrl = manifest.url.replace(/[^/]+$/, "DATA.txt"); + const { buffer: dataBuf } = await download(dataUrl); + if (dataBuf) { + const display = dataBuf.toString("utf8").trim().split(/\r?\n/)[0]?.trim(); + if (display) { + manifest.releaseDateDisplay = display; + const iso = display.match(/^(\d{1,2})\/(\d{1,2})\/(\d{4})$/); + if (iso && !manifest.releaseDate) { + manifest.releaseDate = `${iso[3]}-${iso[2].padStart(2, "0")}-${iso[1].padStart(2, "0")}`; + } + } + } + } catch { + /* opcional */ + } + } + + const newer = compareSemver(CURRENT_VERSION, manifest.version) < 0; + return { + updateAvailable: newer, + currentVersion: CURRENT_VERSION, + manifest, + }; + } catch (err) { + return { + updateAvailable: false, + currentVersion: CURRENT_VERSION, + error: err instanceof Error ? err.message : String(err), + }; + } +} + +/** + * @param {UpdateManifest} manifest + * @param {(payload: { phase: string, received?: number, total?: number, percent?: number }) => void} [onProgress] + */ +async function downloadAndInstall(manifest, onProgress) { + if (!manifest?.url || !manifest?.fileName) { + throw new Error("Manifiesto incompleto"); + } + const tmpDir = await fsp.mkdtemp(path.join(os.tmpdir(), "ipsextractor-update-")); + const destPath = path.join(tmpDir, path.basename(manifest.fileName)); + + onProgress?.({ phase: "downloading", received: 0, total: manifest.size || 0, percent: 0 }); + + await download(manifest.url, { + destPath, + onProgress: (received, total) => { + const t = total || manifest.size || 0; + const percent = t > 0 ? Math.min(100, Math.round((received / t) * 100)) : 0; + onProgress?.({ phase: "downloading", received, total: t, percent }); + }, + }); + + onProgress?.({ phase: "verifying" }); + if (manifest.sha256) { + const hash = crypto.createHash("sha256"); + const fileBuf = await fsp.readFile(destPath); + hash.update(fileBuf); + const digest = hash.digest("hex").toLowerCase(); + if (digest !== String(manifest.sha256).toLowerCase()) { + await fsp.rm(tmpDir, { recursive: true, force: true }).catch(() => undefined); + throw new Error("El instalador descargado no coincide con el hash esperado (sha256)."); + } + } + + onProgress?.({ phase: "installing" }); + // NSIS silent install; la app debe cerrarse para que el instalador reemplace archivos. + const child = spawn(destPath, ["/S"], { + detached: true, + stdio: "ignore", + windowsHide: false, + }); + child.unref(); + + setTimeout(() => { + app.quit(); + }, 400); + + return { ok: true, destPath }; +} + +module.exports = { + CURRENT_VERSION, + 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a/services/desktop_app/electron/main.cjs +++ b/services/desktop_app/electron/main.cjs @@ -2,10 +2,15 @@ const { app, BrowserWindow, dialog, ipcMain } = require("electron"); const fs = require("node:fs/promises"); const path = require("node:path"); const { indexFolder } = require("./folderIndex.cjs"); +const autoUpdate = require("./autoUpdate.cjs"); const { version: appVersion } = require("../package.json"); const DOCUMENT_EXTENSIONS = new Set([".pdf", ".tif", ".tiff", ".png", ".jpg", ".jpeg"]); +const APP_ICON = path.join(__dirname, "icon.png"); + +/** @type {BrowserWindow | null} */ +let mainWindow = null; function createWindow() { const win = new BrowserWindow({ @@ -14,6 +19,7 @@ function createWindow() { minWidth: 1080, minHeight: 720, title: `IPSExtractor Desktop v${appVersion}`, + icon: APP_ICON, backgroundColor: "#f6f7f9", webPreferences: { preload: path.join(__dirname, "preload.cjs"), @@ -21,6 +27,10 @@ function createWindow() { nodeIntegration: false, }, }); + mainWindow = win; + win.on("closed", () => { + if (mainWindow === win) mainWindow = null; + }); if (!app.isPackaged) { win.loadURL("http://127.0.0.1:5173"); @@ -30,7 +40,34 @@ function createWindow() { } } +function sendUpdateProgress(payload) { + if (mainWindow && !mainWindow.isDestroyed()) { + mainWindow.webContents.send("updater:progress", payload); + } +} + +ipcMain.handle("updater:check", async () => { + if (!app.isPackaged) { + return { + updateAvailable: false, + currentVersion: appVersion, + error: "Auto-update desactivado en desarrollo", + }; + } + return autoUpdate.checkForUpdates(); +}); + +ipcMain.handle("updater:install", async (_event, manifest) => { + if (!app.isPackaged) { + throw new Error("Auto-update solo disponible en la app instalada"); + } + return autoUpdate.downloadAndInstall(manifest, sendUpdateProgress); +}); + app.whenReady().then(() => { + if (process.platform === "win32") { + app.setAppUserModelId("com.ipsextractor.desktop"); + } createWindow(); app.on("activate", () => { if (BrowserWindow.getAllWindows().length === 0) createWindow(); diff --git a/services/desktop_app/electron/preload.cjs b/services/desktop_app/electron/preload.cjs index 747e7a4..8f36881 100644 --- a/services/desktop_app/electron/preload.cjs +++ b/services/desktop_app/electron/preload.cjs @@ -9,4 +9,11 @@ contextBridge.exposeInMainWorld("desktopApi", { indexFolder: (rootPath, structureMode, docTypes, keywords) => ipcRenderer.invoke("fs:index-folder", rootPath, structureMode, docTypes, keywords), readFile: (filePath) => ipcRenderer.invoke("fs:read-file", filePath), + checkForUpdates: () => ipcRenderer.invoke("updater:check"), + installUpdate: (manifest) => ipcRenderer.invoke("updater:install", manifest), + onUpdateProgress: (callback) => { + const handler = (_event, payload) => callback(payload); + ipcRenderer.on("updater:progress", handler); + return () => ipcRenderer.removeListener("updater:progress", handler); + }, }); diff --git a/services/desktop_app/index.html b/services/desktop_app/index.html index 16c574b..c8bb5c3 100644 --- a/services/desktop_app/index.html +++ b/services/desktop_app/index.html @@ -3,6 +3,7 @@ + IPSExtractor Desktop diff --git a/services/desktop_app/package-lock.json b/services/desktop_app/package-lock.json index 9aed697..b992530 100644 --- a/services/desktop_app/package-lock.json +++ b/services/desktop_app/package-lock.json @@ -1,13 +1,14 @@ { "name": "ipsextractor-desktop", - "version": "0.1.0", + "version": "1.0.0", "lockfileVersion": 3, "requires": true, "packages": { "": { "name": "ipsextractor-desktop", - "version": "0.1.0", + "version": "1.0.0", "dependencies": { + "@aws-sdk/client-s3": "^3.1085.0", "lucide-react": "^0.468.0", "react": "^18.3.1", "react-dom": "^18.3.1" @@ -58,6 +59,314 @@ "dev": true, "license": "ISC" }, + "node_modules/@aws-sdk/checksums": { + "version": "3.1000.16", + "resolved": "https://registry.npmjs.org/@aws-sdk/checksums/-/checksums-3.1000.16.tgz", + "integrity": 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"1.0.0", + "version": "1.0.1", "private": true, "description": "Desktop extraction client for IPSExtractor microservices.", "main": "electron/main.cjs", @@ -10,22 +10,25 @@ "preview": "vite preview", "test": "vitest run", "test:watch": "vitest", - "dist": "npm run build && electron-builder --win", - "dist:win": "npm run dist" + "dist": "npm run build && electron-builder --win && npm run publish:minio", + "dist:win": "npm run dist", + "dist:local": "npm run build && electron-builder --win", + "publish:minio": "node scripts/publish-version.mjs" }, "dependencies": { + "@aws-sdk/client-s3": "^3.1085.0", "lucide-react": "^0.468.0", "react": "^18.3.1", "react-dom": "^18.3.1" }, "devDependencies": { - "@vitejs/plugin-react": "^4.3.4", "@testing-library/jest-dom": "^6.6.3", "@testing-library/react": "^16.1.0", "@testing-library/user-event": "^14.5.2", "@types/node": "^22.10.2", "@types/react": "^18.3.17", "@types/react-dom": "^18.3.5", + "@vitejs/plugin-react": "^4.3.4", "concurrently": 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+ * Publica el instalador NSIS en MinIO y actualiza latest.json + DATA.txt. + * + * Uso: + * set MINIO_ACCESS_KEY=... + * set MINIO_SECRET_KEY=... + * npm run publish:minio + * + * Opcional: + * MINIO_ENDPOINT=https://s3.builker.xyz + * MINIO_BUCKET=apps-versions + * MINIO_PREFIX=IPSExtractor + * RELEASE_DATE=06/07/2026 (formato DATA.txt DD/MM/YYYY; default: hoy o version.json) + */ +import { createHash } from "node:crypto"; +import { createReadStream, existsSync, readFileSync, statSync, writeFileSync } from "node:fs"; +import { join, dirname } from "node:path"; +import { fileURLToPath } from "node:url"; +import { S3Client, PutObjectCommand } from "@aws-sdk/client-s3"; + +const __dirname = dirname(fileURLToPath(import.meta.url)); +const root = join(__dirname, ".."); + +/** Carga KEY=VALUE desde un archivo local (sin dependencia dotenv). */ +function loadEnvFile(filePath) { + if (!existsSync(filePath)) return; + const text = readFileSync(filePath, "utf8"); + for (const line of text.split(/\r?\n/)) { + const trimmed = line.trim(); + if (!trimmed || trimmed.startsWith("#")) continue; + const eq = trimmed.indexOf("="); + if (eq <= 0) continue; + const key = trimmed.slice(0, eq).trim(); + let value = trimmed.slice(eq + 1).trim(); + if ( + (value.startsWith('"') && value.endsWith('"')) || + (value.startsWith("'") && value.endsWith("'")) + ) { + value = value.slice(1, -1); + } + if (!(key in process.env) || process.env[key] === "") { + process.env[key] = value; + } + } +} + +loadEnvFile(join(root, ".env.minio")); +loadEnvFile(join(root, ".env")); + +const pkg = JSON.parse(readFileSync(join(root, "package.json"), "utf8")); +const version = pkg.version; +const endpoint = (process.env.MINIO_ENDPOINT || "https://s3.builker.xyz").replace(/\/$/, ""); +const bucket = process.env.MINIO_BUCKET || "apps-versions"; +const prefix = (process.env.MINIO_PREFIX || "IPSExtractor").replace(/^\/|\/$/g, ""); +const accessKey = process.env.MINIO_ACCESS_KEY || process.env.AWS_ACCESS_KEY_ID; +const secretKey = process.env.MINIO_SECRET_KEY || process.env.AWS_SECRET_ACCESS_KEY; + +if (!accessKey || !secretKey) { + console.error( + "Faltan credenciales. Define MINIO_ACCESS_KEY y MINIO_SECRET_KEY (o AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY).", + ); + process.exit(1); +} + +const setupName = `IPSExtractor-Desktop-${version}-Setup.exe`; +const setupPath = join(root, "release", setupName); +if (!existsSync(setupPath)) { + console.error(`No se encontró ${setupPath}. Ejecuta antes: npm run dist`); + process.exit(1); +} + +function todayDisplay() { + const d = new Date(); + const dd = String(d.getDate()).padStart(2, "0"); + const mm = String(d.getMonth() + 1).padStart(2, "0"); + const yyyy = d.getFullYear(); + return `${dd}/${mm}/${yyyy}`; +} + +function isoFromDisplay(display) { + // DD/MM/YYYY → YYYY-MM-DD + const m = String(display).trim().match(/^(\d{1,2})\/(\d{1,2})\/(\d{4})$/); + if (!m) return null; + const [, dd, mm, yyyy] = m; + return `${yyyy}-${mm.padStart(2, "0")}-${dd.padStart(2, "0")}`; +} + +function displayFromIso(iso) { + const m = String(iso).trim().match(/^(\d{4})-(\d{2})-(\d{2})$/); + if (!m) return null; + const [, yyyy, mm, dd] = m; + return `${dd}/${mm}/${yyyy}`; +} + +function resolveReleaseDateDisplay() { + if (process.env.RELEASE_DATE) return String(process.env.RELEASE_DATE).trim(); + try { + const meta = JSON.parse(readFileSync(join(root, "src/shared/version.json"), "utf8")); + if (meta.releaseDate) { + return displayFromIso(meta.releaseDate) || todayDisplay(); + } + } catch { + /* ignore */ + } + return todayDisplay(); +} + +async function sha256File(filePath) { + return new Promise((resolve, reject) => { + const hash = createHash("sha256"); + const stream = createReadStream(filePath); + stream.on("data", (chunk) => hash.update(chunk)); + stream.on("error", reject); + stream.on("end", () => resolve(hash.digest("hex"))); + }); +} + +async function upload(client, key, body, contentType) { + await client.send( + new PutObjectCommand({ + Bucket: bucket, + Key: key, + Body: body, + ContentType: contentType, + }), + ); + console.log(` ↑ s3://${bucket}/${key}`); +} + +async function main() { + const releaseDateDisplay = resolveReleaseDateDisplay(); + const releaseDateIso = isoFromDisplay(releaseDateDisplay) || new Date().toISOString().slice(0, 10); + const sha256 = await sha256File(setupPath); + const { size } = statSync(setupPath); + + const publicBase = `${endpoint}/${bucket}/${prefix}`; + const objectKeyExe = `${prefix}/${version}/${setupName}`; + const objectKeyData = `${prefix}/${version}/DATA.txt`; + const objectKeyLatest = `${prefix}/latest.json`; + const publicUrl = `${publicBase}/${version}/${setupName}`; + + const latest = { + version, + releaseDate: releaseDateIso, + releaseDateDisplay, + fileName: setupName, + url: publicUrl, + sha256, + size, + }; + + // Actualiza version.json local para alinear la UI con la fecha publicada + writeFileSync( + join(root, "src/shared/version.json"), + `${JSON.stringify({ releaseDate: releaseDateIso }, null, 2)}\n`, + "utf8", + ); + + const client = new S3Client({ + region: process.env.MINIO_REGION || "us-east-1", + endpoint, + forcePathStyle: true, + credentials: { + accessKeyId: accessKey, + secretAccessKey: secretKey, + }, + }); + + console.log(`Publicando v${version} → ${endpoint}/${bucket}/${prefix}/`); + await upload(client, objectKeyExe, createReadStream(setupPath), "application/octet-stream"); + await upload(client, objectKeyData, `${releaseDateDisplay}\n`, "text/plain; charset=utf-8"); + await upload( + client, + objectKeyLatest, + Buffer.from(`${JSON.stringify(latest, null, 2)}\n`, "utf8"), + "application/json", + ); + + console.log("\nListo."); + console.log(` latest.json: ${publicBase}/latest.json`); + console.log(` DATA.txt: ${publicBase}/${version}/DATA.txt → ${releaseDateDisplay}`); + console.log(` Setup: ${publicUrl}`); +} + +main().catch((err) => { + console.error(err); + process.exit(1); +}); diff --git a/services/desktop_app/src/renderer/App.test.tsx b/services/desktop_app/src/renderer/App.test.tsx index cecd6f7..ec19b21 100644 --- a/services/desktop_app/src/renderer/App.test.tsx +++ b/services/desktop_app/src/renderer/App.test.tsx @@ -3,164 +3,49 @@ import userEvent from "@testing-library/user-event"; import { describe, expect, it, vi } from "vitest"; import App from "./App"; -async function advanceToDiscover(user: ReturnType) { - await user.click(screen.getByRole("button", { name: /explorar/i })); - await user.click(screen.getByRole("button", { name: /siguiente/i })); -} - -async function discoverFields(user: ReturnType) { - await advanceToDiscover(user); - await user.click(screen.getByRole("button", { name: /descubrir campos/i })); -} - describe("App", () => { - it("renders the extraction wizard", () => { + it("renders home shell with jobs and configuration entry", () => { + vi.spyOn(globalThis, "fetch").mockResolvedValue({ + ok: true, + text: async () => "[]", + } as Response); render(); expect(screen.getByText("IPSExtractor")).toBeInTheDocument(); - expect(screen.getByRole("button", { name: /^extraccion$/i })).toBeInTheDocument(); - expect(screen.getByRole("button", { name: /tareas/i })).toBeInTheDocument(); - expect(screen.getByRole("button", { name: /configuracion/i })).toBeInTheDocument(); - expect(screen.getByText("Origen de los documentos")).toBeInTheDocument(); - expect(screen.queryByText("IPS_DOCUMENT_ROOT_LOCAL")).not.toBeInTheDocument(); - expect(screen.queryByText("IPS_DOCUMENT_ROOT_CONTAINER")).not.toBeInTheDocument(); - expect(screen.queryByText("Ruta enviada a Docker/API")).not.toBeInTheDocument(); - expect(screen.getByRole("button", { name: /siguiente/i })).toBeDisabled(); + expect(screen.getByRole("button", { name: /^trabajos$/i })).toBeInTheDocument(); + expect(screen.getByRole("button", { name: /configuraci[oó]n/i })).toBeInTheDocument(); }); - it("shows API URL settings in the configuration page", async () => { + it("opens configuration as a submenu with connection settings", async () => { const user = userEvent.setup(); + vi.spyOn(globalThis, "fetch").mockResolvedValue({ + ok: true, + text: async () => "[]", + } as Response); render(); - await user.click(screen.getByRole("button", { name: /configuracion/i })); - expect(screen.getByText("Conexion API")).toBeInTheDocument(); + await user.click(screen.getByRole("button", { name: /configuraci[oó]n/i })); + expect( + screen.getByRole("navigation", { name: /secciones de configuraci[oó]n/i }), + ).toBeInTheDocument(); + expect(screen.getByRole("button", { name: /^conexi[oó]n$/i })).toBeInTheDocument(); + expect(screen.getByRole("button", { name: /^limpieza$/i })).toBeInTheDocument(); + expect(screen.getByRole("button", { name: /^backend$/i })).toBeInTheDocument(); + expect(screen.getByRole("heading", { name: /^conexi[oó]n$/i })).toBeInTheDocument(); const input = screen.getByLabelText("URL del API Gateway"); await user.clear(input); await user.type(input, "http://localhost:9000"); expect(input).toHaveValue("http://localhost:9000"); }); - it("adds and removes document types", async () => { + it("shows cleaning COP/MOD settings section", async () => { const user = userEvent.setup(); - render(); - await user.click(screen.getByRole("button", { name: /agregar tipo/i })); - expect(screen.getByLabelText("Nombre tipo 2")).toBeInTheDocument(); - const removeButtons = screen.getAllByLabelText("Eliminar tipo"); - await user.click(removeButtons[1]); - expect(screen.queryByLabelText("Nombre tipo 2")).not.toBeInTheDocument(); - }); - - it("does not show keywords in general folder mode", async () => { - const user = userEvent.setup(); - render(); - await user.click(screen.getByRole("button", { name: /carpeta general/i })); - expect(screen.queryByLabelText(/keywords/i)).not.toBeInTheDocument(); - expect(screen.queryByText(/^Keywords$/)).not.toBeInTheDocument(); - expect(screen.getByText(/se procesaran todos los pdf y tiff/i)).toBeInTheDocument(); - }); - - it("shows schema fields from a mocked API response", async () => { - const user = userEvent.setup(); - vi.spyOn(window.desktopApi!, "selectDirectory").mockResolvedValue("C:\\docs\\lote"); vi.spyOn(globalThis, "fetch").mockResolvedValue({ ok: true, - text: async () => JSON.stringify({ schema_by_type: { Factura: { numero: "str" } }, sample_by_type: { Factura: { numero: "A1" } } }), + text: async () => "[]", } as Response); render(); - await discoverFields(user); - await user.click(screen.getByRole("button", { name: /siguiente/i })); - expect(await screen.findByRole("checkbox", { name: "Seleccionar numero" })).toBeInTheDocument(); - }); - - it("blocks advancing while no fields are selected", async () => { - const user = userEvent.setup(); - vi.spyOn(window.desktopApi!, "selectDirectory").mockResolvedValue("C:\\docs\\lote"); - vi.spyOn(globalThis, "fetch").mockResolvedValue({ - ok: true, - text: async () => JSON.stringify({ schema_by_type: { Factura: { numero: "str" } }, sample_by_type: { Factura: { numero: "A1" } } }), - } as Response); - render(); - await discoverFields(user); - await user.click(screen.getByRole("button", { name: /siguiente/i })); - expect(screen.getByRole("button", { name: /siguiente/i })).toBeDisabled(); - }); - - it("creates an extraction task with mocked API responses", async () => { - const user = userEvent.setup(); - vi.spyOn(window.desktopApi!, "selectDirectory").mockResolvedValue("C:\\docs\\lote"); - vi.spyOn(globalThis, "fetch").mockImplementation(async (input) => { - const url = String(input); - if (url.includes("/extraction/schemas/upload")) { - return { - ok: true, - text: async () => JSON.stringify({ schema_by_type: { Factura: { numero: "str" } }, sample_by_type: { Factura: { numero: "A1" } } }), - } as Response; - } - if (url.includes("/tasks/extraction")) { - return { - ok: true, - text: async () => JSON.stringify({ - id: "task-1", - type: "extraction", - status: "running", - title: "Extraccion desktop", - request: { source: "desktop" }, - result: { extraction_batch_id: "batch-1", processed: 0, total: 1 }, - error: null, - owner_id: null, - created_at: "now", - updated_at: "now", - started_at: "now", - finished_at: null, - }), - } as Response; - } - if (url.includes("/tasks/task-1/ingest")) { - return { - ok: true, - text: async () => JSON.stringify({ processed: 1, total: 1, task: { id: "task-1", status: "completed", result: { extraction_batch_id: "batch-1" } } }), - } as Response; - } - if (url.includes("/tasks/task-1") && !url.includes("/rows") && !url.includes("/ingest")) { - return { - ok: true, - text: async () => JSON.stringify({ - id: "task-1", - type: "extraction", - status: "completed", - title: "Extraccion desktop", - request: { source: "desktop" }, - result: { extraction_batch_id: "batch-1", processed: 1, total: 1 }, - error: null, - owner_id: null, - created_at: "now", - updated_at: "now", - started_at: "now", - finished_at: "now", - }), - } as Response; - } - if (url.includes("/tasks?")) { - return { - ok: true, - text: async () => JSON.stringify([{ id: "task-1", type: "extraction", status: "completed", title: "Extraccion desktop", request: {}, result: { extraction_batch_id: "batch-1" }, error: null, owner_id: null, created_at: "now", updated_at: "now", started_at: "now", finished_at: "now" }]), - } as Response; - } - if (url.includes("/tasks/task-1/rows")) { - return { - ok: true, - text: async () => JSON.stringify({ task_id: "task-1", batch_id: "batch-1", rows: [{ radicado: "R1", numero: "A1" }], limit: 500, offset: 0 }), - } as Response; - } - return { ok: true, text: async () => "{}" } as Response; - }); - - render(); - await discoverFields(user); - await user.click(screen.getByRole("button", { name: /siguiente/i })); - await user.click(await screen.findByRole("checkbox", { name: "Seleccionar numero" })); - await user.click(screen.getByRole("button", { name: /siguiente/i })); - await user.click(screen.getByRole("button", { name: /ejecutar extraccion/i })); - expect(await screen.findByText("Tareas y resultados")).toBeInTheDocument(); - expect((await screen.findAllByText("completed")).length).toBeGreaterThan(0); - expect(await screen.findByText("A1")).toBeInTheDocument(); + await user.click(screen.getByRole("button", { name: /configuraci[oó]n/i })); + await user.click(screen.getByRole("button", { name: /^limpieza$/i })); + expect(screen.getByRole("heading", { name: /limpieza.*cop/i })).toBeInTheDocument(); + expect(screen.getByLabelText("2024")).toBeInTheDocument(); }); }); diff --git a/services/desktop_app/src/renderer/App.tsx b/services/desktop_app/src/renderer/App.tsx index 14fd387..5bf3fd4 100644 --- a/services/desktop_app/src/renderer/App.tsx +++ b/services/desktop_app/src/renderer/App.tsx @@ -1,17 +1,14 @@ import { BarChart2, Briefcase, - CheckCircle2, Cpu, - Database, Hash, - Loader2, - Server, + Link2, Settings, - XCircle, + Sparkles, } from "lucide-react"; import { useEffect, useMemo, useState } from "react"; -import type { ExtractionFormState, Job } from "../shared/types"; +import type { Job } from "../shared/types"; import { APP_RELEASE_DATE, APP_VERSION, @@ -19,14 +16,18 @@ import { formatVersionLabel, } from "../shared/appVersion"; import { ApiClient } from "./lib/apiClient"; -import { DEFAULT_API_BASE_URL, loadApiBaseUrl, migrateApiBaseUrlInStorage } from "./lib/apiDefaults"; +import { loadApiBaseUrl, migrateApiBaseUrlInStorage } from "./lib/apiDefaults"; import { HomeView } from "./views/HomeView"; import { JobView } from "./views/JobView"; +import { SettingsView, type SettingsSection } from "./views/SettingsView"; import type { JobSection } from "./types/jobSection"; +import appIcon from "./assets/app-icon.png"; +import { UpdatePrompt, useAutoUpdateCheck } from "./components/UpdatePrompt"; // Registrar módulos al cargar la app import "./modules/counting/CountingModule"; import "./modules/extraction/ExtractionModule"; +import "./modules/cleaning/CleaningModule"; type Status = "idle" | "ok" | "error" | "busy"; type View = "home" | "job" | "settings"; @@ -34,15 +35,19 @@ export type { JobSection } from "./types/jobSection"; function App() { const [view, setView] = useState("home"); + const [settingsReturnView, setSettingsReturnView] = useState<"home" | "job">("home"); + const [settingsSection, setSettingsSection] = useState("connection"); const [activeJob, setActiveJob] = useState(null); const [jobSection, setJobSection] = useState("counting"); const [jobSectionContext, setJobSectionContext] = useState>({}); const [apiBaseUrl, setApiBaseUrl] = useState(loadApiBaseUrl); const [connectionStatus, setConnectionStatus] = useState("idle"); - const [connectionMessage, setConnectionMessage] = useState("Sin verificar"); + const [connectionMessage, setConnectionMessage] = useState("Pulsa «Verificar conexión» para comprobar el gateway."); + const [healthJson, setHealthJson] = useState(null); const [configJson, setConfigJson] = useState(null); const [modelsJson, setModelsJson] = useState(null); const [busy, setBusy] = useState(false); + const update = useAutoUpdateCheck(); const client = useMemo(() => new ApiClient(apiBaseUrl), [apiBaseUrl]); @@ -54,21 +59,27 @@ function App() { } }, []); // eslint-disable-line react-hooks/exhaustive-deps - useEffect(() => { localStorage.setItem("apiBaseUrl", apiBaseUrl); }, [apiBaseUrl]); + useEffect(() => { + localStorage.setItem("apiBaseUrl", apiBaseUrl); + }, [apiBaseUrl]); async function verifyConnection() { setConnectionStatus("busy"); - setConnectionMessage("Verificando gateway..."); + setConnectionMessage("Comprobando gateway y servicios…"); setBusy(true); try { const [health, config, models] = await Promise.all([ - client.health(), client.config(), client.models(), + client.health(), + client.config(), + client.models(), ]); + setHealthJson(health); setConfigJson(config); setModelsJson(models); setConnectionStatus("ok"); - setConnectionMessage(`Conectado: ${JSON.stringify(health)}`); + setConnectionMessage("Gateway respondió correctamente."); } catch (err) { + setHealthJson(null); setConnectionStatus("error"); setConnectionMessage(err instanceof Error ? err.message : "No se pudo conectar"); } finally { @@ -88,27 +99,73 @@ function App() { setJobSectionContext(context); } + function openSettings(section: SettingsSection = "connection") { + setSettingsReturnView(view === "job" ? "job" : "home"); + setSettingsSection(section); + setView("settings"); + } + + function leaveSettings() { + if (settingsReturnView === "job" && activeJob) { + setView("job"); + } else { + setView("home"); + } + } + const isJobOpen = view === "job" && activeJob; + const isSettingsOpen = view === "settings"; return (

-
+
{formatVersionLabel()}
- {view === "home" && ( - - )} + {view === "home" && } {view === "job" && activeJob && ( )}
+ + {update.manifest && ( + void update.confirmInstall()} + installing={update.installing} + progress={update.progress} + error={update.error} + /> + )}
); } -type SettingsTab = "config" | "models"; - -function SettingsView({ - apiBaseUrl, - onApiBaseUrlChange, - connectionStatus, - connectionMessage, - verifyConnection, - busy, - configJson, - modelsJson, -}: { - apiBaseUrl: string; - onApiBaseUrlChange: (url: string) => void; - connectionStatus: Status; - connectionMessage: string; - verifyConnection: () => Promise; - busy: boolean; - configJson: unknown; - modelsJson: unknown; -}) { - const [backendTab, setBackendTab] = useState("config"); - - const connectionLabel = - connectionStatus === "ok" - ? "Conectado" - : connectionStatus === "busy" - ? "Verificando..." - : connectionStatus === "error" - ? "Sin conexión" - : "Sin verificar"; - - const backendData = backendTab === "config" ? configJson : modelsJson; - - return ( -
- - -
-
-
- -

Configuración del backend

-
-
- - -
-
-
- {backendData != null ? ( - 
{JSON.stringify(backendData, null, 2)}
- ) : ( -
- -

Sin datos del backend

- Verifica la conexión para cargar la {backendTab === "config" ? "configuración" : "lista de modelos"}. -
- )} -
-
-
- ); -} - export default App; diff --git a/services/desktop_app/src/renderer/assets/app-icon.png b/services/desktop_app/src/renderer/assets/app-icon.png new file mode 100644 index 0000000000000000000000000000000000000000..54863e5c6833e4bfba6f8dab2527a1ee4e4bd230 GIT binary patch literal 56966 zcmV*YKv%zsP)yYf}--Awlw%wQ&fQ4mCsVh9RGDC9yRMKBbi><|5rC@QE3 z8B!7ga)1<+2vQ^{02~AcGZ>6P_w-EL>8k3gSI@iaP5L~3@2yyS?~{4*-uqscdENEu zy}PRN-Mo1-Pae0{UglfhA^;%aR8HkoUPz&z_70rNshrBEC#MGBR8HjumQw?8DyQ-S z%c%i4l~Z|v<(kzWQ+cYQVcn$o2Y)5{*JE!gevwE2d@3)H6nDv~d6~Ve@x*i04D%r$97UeNyZ#orZT6t=P}#1*)-UbAmb?Jj0;W$k;CzL z2>@)|t~@mYFPxkjfX{+xRi9IM3rOg@ZjV*H38^|B)O}x8ZB9Hll`c!D=L$|Ti@BcV zxQ;aq3#61u66KJIjHV#7d%Lc0+e8$`j`nyi$g^*JN@EH={arP_q>R!GOIJ8r3 zLCt~GVKMQTlT)Yzqh2bwiu$<_KrIF`&p`+QK+pxy5d*Vqkm}M4a0#0Pz4Xw&aggla z**MDqylL{_RGxb|H2}|!oTzl3>vk)P^$q)6$BP@zYGzZ;ZJ@wtl><;Iv{Ivb6CjdO ziotp8_V0h$9k-Lg`Ai3S0Gg5)46zGQJeZ0*F`;~zcnN2Bq%PvQ7a)Z>swtjN!S}ujJckwho@Vv^Y0r-@J5F#Y5 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b/services/desktop_app/src/renderer/components/BackgroundProgressBanner.tsx index 75ff816..e91df2e 100644 --- a/services/desktop_app/src/renderer/components/BackgroundProgressBanner.tsx +++ b/services/desktop_app/src/renderer/components/BackgroundProgressBanner.tsx @@ -13,7 +13,10 @@ function pct(processed: number, total: number): number { export function BackgroundProgressBanner({ progress, compact = false }: Props) { const percent = pct(progress.processed, progress.total); - const moduleLabel = progress.module === "counting" ? "Conteo" : "Extracción"; + const moduleLabel = + progress.module === "counting" ? "Conteo" + : progress.module === "cleaning" ? "Limpieza" + : "Extracción"; return (
diff --git a/services/desktop_app/src/renderer/components/CleaningStepper.tsx b/services/desktop_app/src/renderer/components/CleaningStepper.tsx new file mode 100644 index 0000000..48d23aa --- /dev/null +++ b/services/desktop_app/src/renderer/components/CleaningStepper.tsx @@ -0,0 +1,44 @@ +import { Check } from "lucide-react"; + +export type CleaningStep = 1 | 2 | 3; + +const STEPS: Array<{ id: CleaningStep; label: string }> = [ + { id: 1, label: "Fuente" }, + { id: 2, label: "Reglas" }, + { id: 3, label: "Resultados" }, +]; + +interface CleaningStepperProps { + current: CleaningStep; + maxReached: CleaningStep; + onStepClick?: (step: CleaningStep) => void; +} + +export function CleaningStepper({ current, maxReached, onStepClick }: CleaningStepperProps) { + return ( + + ); +} diff --git a/services/desktop_app/src/renderer/components/CountingWizard.tsx b/services/desktop_app/src/renderer/components/CountingWizard.tsx index 894cdca..31169f4 100644 --- a/services/desktop_app/src/renderer/components/CountingWizard.tsx +++ b/services/desktop_app/src/renderer/components/CountingWizard.tsx @@ -24,6 +24,7 @@ import type { Task, } from "../../shared/types"; import { ApiClient } from "../lib/apiClient"; +import { downloadCsv, toCsv, buildExportCsvFilename } from "../lib/csv"; import { CountResultsTable } from "./CountResultsTable"; import { CountingStepper, type CountingStep } from "./CountingStepper"; @@ -377,25 +378,16 @@ export function CountingWizard({ client, localRoot, containerRoot, job, onStartE function handleExportCsv() { if (!savedFiles.length) return; const headers = ["filename", "radicado", "service_count", "auto_ajustado", "status", "is_valid", "error"]; - const rows = savedFiles.map((f) => - [ - `"${f.filename}"`, - `"${f.radicado ?? ""}"`, - f.service_count ?? "", - autoAdjustedFilenames.has(f.filename) ? "si" : "", - f.status, - f.is_valid === null ? "" : String(f.is_valid), - `"${(f.error ?? "").replace(/"/g, '""')}"`, - ].join(","), - ); - const csv = [headers.join(","), ...rows].join("\n"); - const blob = new Blob([csv], { type: "text/csv" }); - const url = URL.createObjectURL(blob); - const a = document.createElement("a"); - a.href = url; - a.download = `conteo-${new Date().toISOString().slice(0, 10)}.csv`; - a.click(); - URL.revokeObjectURL(url); + const rows = savedFiles.map((f) => [ + f.filename, + f.radicado ?? "", + f.service_count ?? "", + autoAdjustedFilenames.has(f.filename) ? "si" : "", + f.status, + f.is_valid === null ? "" : String(f.is_valid), + f.error ?? "", + ]); + downloadCsv(buildExportCsvFilename("conteo", job?.title), toCsv(headers, rows)); } function resetWizard() { diff --git a/services/desktop_app/src/renderer/components/ExtractionResultsTable.tsx b/services/desktop_app/src/renderer/components/ExtractionResultsTable.tsx new file mode 100644 index 0000000..3bbeb20 --- /dev/null +++ b/services/desktop_app/src/renderer/components/ExtractionResultsTable.tsx @@ -0,0 +1,147 @@ +import { Search } from "lucide-react"; +import { useMemo, useState } from "react"; + +type Row = Record; +type FilterKey = "all" | "complete" | "incomplete"; + +interface Props { + rows: Row[]; + columns: string[]; +} + +function cellValue(row: Row, col: string): string { + const v = row[col]; + if (v === null || v === undefined) return ""; + return String(v); +} + +function isEmpty(row: Row, col: string): boolean { + return cellValue(row, col).trim() === ""; +} + +function isComplete(row: Row, columns: string[]): boolean { + return columns.every((c) => !isEmpty(row, c)); +} + +export function ExtractionResultsTable({ rows, columns }: Props) { + const [filter, setFilter] = useState("all"); + const [search, setSearch] = useState(""); + + const summary = useMemo(() => { + const complete = rows.filter((r) => isComplete(r, columns)).length; + return { + total: rows.length, + complete, + incomplete: rows.length - complete, + }; + }, [rows, columns]); + + const filtered = useMemo(() => { + let list = rows; + if (filter === "complete") list = list.filter((r) => isComplete(r, columns)); + else if (filter === "incomplete") list = list.filter((r) => !isComplete(r, columns)); + + const q = search.trim().toLowerCase(); + if (q) { + list = list.filter((r) => + columns.some((c) => cellValue(r, c).toLowerCase().includes(q)), + ); + } + return list; + }, [rows, columns, filter, search]); + + const FILTERS: { key: FilterKey; label: string; count: number }[] = [ + { key: "all", label: "Todos", count: summary.total }, + { key: "complete", label: "Completos", count: summary.complete }, + { key: "incomplete", label: "Incompletos", count: summary.incomplete }, + ]; + + return ( +
+
+
+ {summary.total} + Total +
+
+ {summary.complete} + Completos +
+
+ {summary.incomplete} + Incompletos +
+
+ +
+
+ {FILTERS.map(({ key, label, count }) => ( + + ))} +
+ +
+
+ + setSearch(e.target.value)} + /> +
+
+
+ +
+ + + + {columns.map((c) => ( + + ))} + + + + {filtered.length === 0 ? ( + + + + ) : ( + filtered.map((row, i) => ( + + {columns.map((c) => ( + + ))} + + )) + )} + +
{c}
+ {search + ? "No hay filas que coincidan con la búsqueda." + : "No hay filas en esta categoría."} +
+ {isEmpty(row, c) ? ( + — + ) : ( + cellValue(row, c) + )} +
+
+ + {filtered.length !== rows.length && ( +

+ Mostrando {filtered.length} de {rows.length} filas +

+ )} +
+ ); +} diff --git a/services/desktop_app/src/renderer/components/ExtractionWizard.tsx b/services/desktop_app/src/renderer/components/ExtractionWizard.tsx index 056dee9..c2e2773 100644 --- a/services/desktop_app/src/renderer/components/ExtractionWizard.tsx +++ b/services/desktop_app/src/renderer/components/ExtractionWizard.tsx @@ -15,6 +15,7 @@ import { RefreshCw, Search, Settings2, + Shuffle, Trash2, Zap, } from "lucide-react"; @@ -36,6 +37,9 @@ interface ExtractionWizardProps { statusText: string; selectRoot: () => Promise; discoverSchema: () => Promise; + rediscoverWithOtherSample?: () => Promise; + discoverySampleLabels?: string[]; + canRediscoverWithOtherSample?: boolean; estimate: () => Promise; runExtraction: () => Promise; selection: FieldSelection; @@ -104,6 +108,9 @@ export function ExtractionWizard({ statusText, selectRoot, discoverSchema, + rediscoverWithOtherSample, + discoverySampleLabels = [], + canRediscoverWithOtherSample = false, estimate, runExtraction, selection, @@ -373,7 +380,39 @@ export function ExtractionWizard({ {fieldCount > 0 && (
- {fieldCount} campos detectados. Continua al siguiente paso para elegir cuales exportar. + {fieldCount} campos detectados + {discoverySampleLabels.length > 0 + ? ` con «${discoverySampleLabels.join(", ")}».` + : "."}{" "} + Continúa al siguiente paso para elegir cuáles exportar. +
+ )} + + {schemaResponse && rediscoverWithOtherSample && ( +
+
+ ¿Algo no salió como esperabas? + + Vuelve a descubrir los campos usando otro archivo aleatorio de la carpeta. + {discoverySampleLabels.length > 0 && ( + <> Muestra actual: {discoverySampleLabels.join(", ")}. + )} + +
+
)}
@@ -424,6 +463,34 @@ export function ExtractionWizard({ />
+ {schemaResponse && rediscoverWithOtherSample && ( +
+
+ ¿Algo no salió como esperabas? + + Reintenta el descubrimiento con otro archivo aleatorio + {discoverySampleLabels.length > 0 && ( + <> (ahora: {discoverySampleLabels.join(", ")}) + )} + . + +
+ +
+ )}

Vista previa {selection.fields.length > 0 && ( diff --git a/services/desktop_app/src/renderer/components/UpdatePrompt.tsx b/services/desktop_app/src/renderer/components/UpdatePrompt.tsx new file mode 100644 index 0000000..77e4da7 --- /dev/null +++ b/services/desktop_app/src/renderer/components/UpdatePrompt.tsx @@ -0,0 +1,172 @@ +import { Download, Loader2, X } from "lucide-react"; +import { useEffect, useState } from "react"; +import type { UpdateManifest, UpdateProgress } from "../../shared/types"; + +type Props = { + open: boolean; + currentVersion: string; + manifest: UpdateManifest; + onClose: () => void; + onConfirm: () => void; + installing: boolean; + progress: UpdateProgress | null; + error: string | null; +}; + +function formatBytes(n: number | undefined): string { + if (!n || n <= 0) return ""; + if (n < 1024) return `${n} B`; + if (n < 1024 * 1024) return `${(n / 1024).toFixed(1)} KB`; + return `${(n / (1024 * 1024)).toFixed(1)} MB`; +} + +export function UpdatePrompt({ + open, + currentVersion, + manifest, + onClose, + onConfirm, + installing, + progress, + error, +}: Props) { + if (!open) return null; + + const dateLabel = manifest.releaseDateDisplay || manifest.releaseDate || ""; + const percent = progress?.percent ?? 0; + const phaseLabel = + progress?.phase === "verifying" + ? "Verificando archivo…" + : progress?.phase === "installing" + ? "Iniciando instalador…" + : progress?.phase === "downloading" + ? `Descargando… ${percent}%` + : null; + + return ( +
+
+
+

Nueva versión disponible

+ {!installing && ( + + )} +
+ +

+ Hay una versión nueva: v{manifest.version} + {dateLabel ? <> (lanzamiento {dateLabel}) : null}. +
+ Tienes instalada la v{currentVersion}. +

+ + {manifest.size != null && manifest.size > 0 && ( +

Tamaño aproximado: {formatBytes(manifest.size)}

+ )} + + {installing && ( +
+
+
+
+ + {phaseLabel || "Preparando…"} + {progress?.phase === "downloading" && progress.total + ? ` · ${formatBytes(progress.received)} / ${formatBytes(progress.total)}` + : ""} + +
+ )} + + {error && ( +
+ {error} +
+ )} + +
+ + +
+
+
+ ); +} + +/** Comprueba actualizaciones al montar (solo si existe desktopApi). */ +export function useAutoUpdateCheck() { + const [open, setOpen] = useState(false); + const [currentVersion, setCurrentVersion] = useState(""); + const [manifest, setManifest] = useState(null); + const [installing, setInstalling] = useState(false); + const [progress, setProgress] = useState(null); + const [error, setError] = useState(null); + + useEffect(() => { + const api = window.desktopApi; + if (!api?.checkForUpdates) return; + + let cancelled = false; + const timer = window.setTimeout(() => { + void (async () => { + try { + const result = await api.checkForUpdates(); + if (cancelled || !result.updateAvailable || !result.manifest) return; + setCurrentVersion(result.currentVersion); + setManifest(result.manifest); + setOpen(true); + } catch { + /* silencioso */ + } + })(); + }, 2000); + + return () => { + cancelled = true; + window.clearTimeout(timer); + }; + }, []); + + useEffect(() => { + const api = window.desktopApi; + if (!api?.onUpdateProgress || !installing) return; + return api.onUpdateProgress((p) => setProgress(p)); + }, [installing]); + + async function confirmInstall() { + if (!manifest || !window.desktopApi?.installUpdate) return; + setInstalling(true); + setError(null); + setProgress({ phase: "downloading", percent: 0 }); + try { + await window.desktopApi.installUpdate(manifest); + // La app debería cerrarse desde main; si no, mostramos mensaje. + } catch (e) { + setError(e instanceof Error ? e.message : String(e)); + setInstalling(false); + } + } + + return { + open, + currentVersion, + manifest, + installing, + progress, + error, + close: () => { + if (!installing) setOpen(false); + }, + confirmInstall, + }; +} diff --git a/services/desktop_app/src/renderer/lib/apiClient.ts b/services/desktop_app/src/renderer/lib/apiClient.ts index 8e0a43f..b5184e1 100644 --- a/services/desktop_app/src/renderer/lib/apiClient.ts +++ b/services/desktop_app/src/renderer/lib/apiClient.ts @@ -1,4 +1,10 @@ import type { + CleaningAnalysisResponse, + CleaningJob, + CleaningPreviewPayload, + CleaningPreviewResponse, + CleaningRowsResponse, + CleaningRuleInfo, CountJobFile, CountSyncResponse, CreateCountJobPayload, @@ -7,6 +13,7 @@ import type { ExtractionFromCountPayload, Job, RowsResponse, + RunCleaningPayload, SchemaResponse, Task, UpdateJobPayload, @@ -51,7 +58,9 @@ export class ApiClient { return ( path.startsWith("/count/") || path.includes("/ingest") || - path.includes("/upload") + path.includes("/upload") || + path.startsWith("/cleaning/analyze-csv") || + path.startsWith("/cleaning/jobs/from-csv") ); } @@ -218,6 +227,51 @@ export class ApiClient { return this.postJson("/tasks/extraction/from-count", payload); } + // ── Cleaning ─────────────────────────────────────────────────────────────── + + async cleaningRules(): Promise { + return this.getJson("/cleaning/rules"); + } + + async analyzeCleaningBatch(batchId: string): Promise { + return this.getJson( + `/cleaning/batches/${encodeURIComponent(batchId)}/analysis`, + ); + } + + async analyzeCleaningCsv(file: File | Blob, filename = "upload.csv"): Promise { + const form = new FormData(); + form.append("file", file, filename); + return this.postForm("/cleaning/analyze-csv", form); + } + + async runCleaning(payload: RunCleaningPayload): Promise { + return this.postJson("/cleaning/jobs", payload); + } + + async previewCleaning(payload: CleaningPreviewPayload): Promise { + return this.postJson("/cleaning/preview", payload); + } + + async runCleaningFromCsv(form: FormData): Promise { + return this.postForm("/cleaning/jobs/from-csv", form); + } + + async cleaningJobs(jobId?: string): Promise { + const qs = jobId ? `?job_id=${encodeURIComponent(jobId)}` : ""; + return this.getJson(`/cleaning/jobs${qs}`); + } + + async cleaningJob(cleaningJobId: string): Promise { + return this.getJson(`/cleaning/jobs/${encodeURIComponent(cleaningJobId)}`); + } + + async cleaningJobRows(cleaningJobId: string, limit = 500, offset = 0): Promise { + return this.getJson( + `/cleaning/jobs/${encodeURIComponent(cleaningJobId)}/rows?limit=${limit}&offset=${offset}`, + ); + } + private async handle(response: Response): Promise { const text = await response.text(); let data: unknown = null; diff --git a/services/desktop_app/src/renderer/lib/cleaningSettings.test.ts b/services/desktop_app/src/renderer/lib/cleaningSettings.test.ts new file mode 100644 index 0000000..7c04599 --- /dev/null +++ b/services/desktop_app/src/renderer/lib/cleaningSettings.test.ts @@ -0,0 +1,32 @@ +import { describe, expect, it, beforeEach } from "vitest"; +import { + defaultCopModConfig, + loadCopModConfig, + saveCopModConfig, + CLEANING_COPMOD_STORAGE_KEY, +} from "./cleaningSettings"; + +describe("cleaningSettings", () => { + beforeEach(() => { + localStorage.removeItem(CLEANING_COPMOD_STORAGE_KEY); + }); + + it("returns defaults when empty", () => { + const cfg = loadCopModConfig(); + expect(cfg.moderadora_by_year["2024"]).toEqual([4500, 18200, 47700]); + }); + + it("persists and reloads custom years", () => { + saveCopModConfig({ + moderadora_by_year: { "2030": [1000, 2000] }, + }); + const cfg = loadCopModConfig(); + expect(cfg.moderadora_by_year).toEqual({ "2030": [1000, 2000] }); + }); + + it("defaultCopModConfig is a deep copy", () => { + const a = defaultCopModConfig(); + a.moderadora_by_year["2024"].push(999); + expect(defaultCopModConfig().moderadora_by_year["2024"]).not.toContain(999); + }); +}); diff --git a/services/desktop_app/src/renderer/lib/cleaningSettings.ts b/services/desktop_app/src/renderer/lib/cleaningSettings.ts new file mode 100644 index 0000000..4101d36 --- /dev/null +++ b/services/desktop_app/src/renderer/lib/cleaningSettings.ts @@ -0,0 +1,66 @@ +/** Preferencias locales de limpieza (COP/MOD). */ + +export const CLEANING_COPMOD_STORAGE_KEY = "cleaningCopModConfig"; + +/** Valores por defecto alineados con el backend (`DEFAULT_MODERADORA_BY_YEAR`). */ +export const DEFAULT_MODERADORA_BY_YEAR: Record = { + "2023": [4100, 16400, 43000], + "2024": [4500, 18200, 47700], + "2025": [4700, 19200, 50300], + "2026": [5000, 20100, 52800], +}; + +export type CopModConfig = { + moderadora_by_year: Record; +}; + +export function defaultCopModConfig(): CopModConfig { + return { + moderadora_by_year: Object.fromEntries( + Object.entries(DEFAULT_MODERADORA_BY_YEAR).map(([y, vals]) => [y, [...vals]]), + ), + }; +} + +function normalizeYearMap(raw: unknown): Record { + if (!raw || typeof raw !== "object") return {}; + const out: Record = {}; + for (const [k, v] of Object.entries(raw as Record)) { + const year = String(Number.parseInt(k, 10)); + if (!Number.isFinite(Number(year)) || year === "NaN") continue; + if (!Array.isArray(v)) continue; + const nums = v + .map((x) => Math.round(Number(x))) + .filter((n) => Number.isFinite(n) && n >= 0); + if (nums.length > 0) { + out[year] = [...new Set(nums)].sort((a, b) => a - b); + } + } + return out; +} + +export function loadCopModConfig(): CopModConfig { + try { + const raw = localStorage.getItem(CLEANING_COPMOD_STORAGE_KEY); + if (!raw) return defaultCopModConfig(); + const parsed = JSON.parse(raw) as { moderadora_by_year?: unknown }; + const years = normalizeYearMap(parsed?.moderadora_by_year); + if (Object.keys(years).length === 0) return defaultCopModConfig(); + return { moderadora_by_year: years }; + } catch { + return defaultCopModConfig(); + } +} + +export function saveCopModConfig(config: CopModConfig): void { + const years = normalizeYearMap(config.moderadora_by_year); + localStorage.setItem( + CLEANING_COPMOD_STORAGE_KEY, + JSON.stringify({ moderadora_by_year: years }), + ); +} + +/** Payload listo para enviar al API de cleaning. */ +export function copModConfigForApi(config?: CopModConfig | null): CopModConfig { + return config ?? loadCopModConfig(); +} diff --git a/services/desktop_app/src/renderer/lib/csv.test.ts b/services/desktop_app/src/renderer/lib/csv.test.ts index 8992a45..b3fa69f 100644 --- a/services/desktop_app/src/renderer/lib/csv.test.ts +++ b/services/desktop_app/src/renderer/lib/csv.test.ts @@ -1,18 +1,40 @@ import { describe, expect, it } from "vitest"; -import { rowsToCsv } from "./csv"; +import { + buildExportCsvFilename, + csvEscape, + rowsToCsv, + sanitizeFilenamePart, + toCsv, +} from "./csv"; describe("csv export", () => { - it("escapes commas, quotes and newlines", () => { + it("usa pipe como separador", () => { + const csv = toCsv(["a", "b"], [["1", "2"], ["3", "4"]]); + expect(csv).toBe("a|b\n1|2\n3|4"); + }); + + it("escapa pipes, comillas y saltos de línea", () => { const csv = rowsToCsv( [ { radicado: "R1", - nombre: 'Ana, "IPS"', + nombre: 'Ana| "IPS"', nota: "linea 1\nlinea 2", }, ], { nombre_original: "nombre" }, ); - expect(csv).toContain('R1,"Ana, ""IPS""","linea 1\nlinea 2"'); + expect(csv).toContain('R1|"Ana| ""IPS"""|"linea 1\nlinea 2"'); + }); + + it("no escapa comas (no son el delimitador)", () => { + expect(csvEscape("Ana, IPS")).toBe("Ana, IPS"); + }); + + it("incluye el título del trabajo en el nombre de descarga", () => { + expect(sanitizeFilenamePart("Lote Abril / 2026")).toBe("Lote-Abril-2026"); + expect(buildExportCsvFilename("limpieza", "Lote Abril", "validos")).toMatch( + /^limpieza-Lote-Abril-validos-\d{4}-\d{2}-\d{2}\.csv$/, + ); }); }); diff --git a/services/desktop_app/src/renderer/lib/csv.ts b/services/desktop_app/src/renderer/lib/csv.ts index c6f32b2..44c9bbc 100644 --- a/services/desktop_app/src/renderer/lib/csv.ts +++ b/services/desktop_app/src/renderer/lib/csv.ts @@ -1,4 +1,6 @@ -const NEEDS_QUOTES = /[",\r\n]/; +export const CSV_DELIMITER = "|"; + +const NEEDS_QUOTES = /["|\r\n]/; export function csvEscape(value: unknown): string { if (value === null || value === undefined) return ""; @@ -7,13 +9,21 @@ export function csvEscape(value: unknown): string { return `"${text.replace(/"/g, '""')}"`; } +/** Serializa encabezado + filas con separador pipe (`|`). */ +export function toCsv(headers: string[], rows: Array>): string { + const lines = [ + headers.map(csvEscape).join(CSV_DELIMITER), + ...rows.map((row) => row.map(csvEscape).join(CSV_DELIMITER)), + ]; + return lines.join("\n"); +} + export function rowsToCsv(rows: Array>, aliasMap: Record = {}): string { const header = inferHeader(rows, aliasMap); - const lines = [header.map(csvEscape).join(",")]; - for (const row of rows) { - lines.push(header.map((column) => csvEscape(row[column])).join(",")); - } - return `${lines.join("\r\n")}\r\n`; + return toCsv( + header, + rows.map((row) => header.map((column) => row[column])), + ); } export function inferHeader(rows: Array>, aliasMap: Record = {}): string[] { @@ -38,3 +48,43 @@ export function inferHeader(rows: Array>, aliasMap: Reco } return header; } + +export function downloadCsv(filename: string, csv: string): void { + const blob = new Blob([csv], { type: "text/csv;charset=utf-8" }); + const url = URL.createObjectURL(blob); + const a = document.createElement("a"); + a.href = url; + a.download = filename; + a.click(); + URL.revokeObjectURL(url); +} + +/** Normaliza el título del trabajo para usarlo en nombres de archivo. */ +export function sanitizeFilenamePart(value: string | null | undefined, fallback = "sin-titulo"): string { + const cleaned = String(value ?? "") + .normalize("NFKD") + .replace(/[\u0300-\u036f]/g, "") + .replace(/[^a-zA-Z0-9._-]+/g, "-") + .replace(/-+/g, "-") + .replace(/^-|-$/g, "") + .slice(0, 80); + return cleaned || fallback; +} + +/** + * Nombre de descarga: `{servicio}-{titulo-trabajo}[-{extra}]-{YYYY-MM-DD}.csv` + * Ej.: `limpieza-lote-abril-2026-07-10.csv` + */ +export function buildExportCsvFilename( + service: string, + jobTitle?: string | null, + extra?: string | null, +): string { + const parts = [ + sanitizeFilenamePart(service, "export"), + sanitizeFilenamePart(jobTitle, "sin-titulo"), + ]; + if (extra) parts.push(sanitizeFilenamePart(extra)); + parts.push(new Date().toISOString().slice(0, 10)); + return `${parts.join("-")}.csv`; +} diff --git a/services/desktop_app/src/renderer/modules/cleaning/CleaningModule.ts b/services/desktop_app/src/renderer/modules/cleaning/CleaningModule.ts new file mode 100644 index 0000000..1570534 --- /dev/null +++ b/services/desktop_app/src/renderer/modules/cleaning/CleaningModule.ts @@ -0,0 +1,18 @@ +import { CleaningWizardModule } from "./CleaningWizardModule"; +import { CleaningResults } from "./CleaningResults"; +import type { ModuleDescriptor } from "../registry"; +import { registerModule } from "../registry"; +import type { Task } from "../../../shared/types"; + +const CleaningModule: ModuleDescriptor = { + type: "cleaning", + label: "Limpieza", + iconName: "Sparkles", + hasTask: (tasks: Task[]) => tasks.some((t) => t.type === "cleaning"), + Wizard: CleaningWizardModule, + Results: CleaningResults, +}; + +registerModule(CleaningModule); + +export { CleaningModule }; diff --git a/services/desktop_app/src/renderer/modules/cleaning/CleaningResults.tsx b/services/desktop_app/src/renderer/modules/cleaning/CleaningResults.tsx new file mode 100644 index 0000000..2a356d1 --- /dev/null +++ b/services/desktop_app/src/renderer/modules/cleaning/CleaningResults.tsx @@ -0,0 +1,125 @@ +import { Download, Loader2, RefreshCw, Sparkles } from "lucide-react"; +import { useEffect, useState } from "react"; +import type { CleaningJob } from "../../../shared/types"; +import { ExtractionResultsTable } from "../../components/ExtractionResultsTable"; +import { buildExportCsvFilename, downloadCsv, toCsv } from "../../lib/csv"; +import type { ModuleResultsProps } from "../registry"; + +type Row = Record; + +export function CleaningResults({ client, task, job }: ModuleResultsProps) { + const [cleaningJob, setCleaningJob] = useState(null); + const [rows, setRows] = useState([]); + const [columns, setColumns] = useState([]); + const [loading, setLoading] = useState(true); + const [error, setError] = useState(null); + + async function load() { + try { + const cleaningJobId = String( + (task.result as Record)?.cleaning_job_id ?? "", + ); + if (!cleaningJobId) { + setError("La tarea no tiene cleaning_job_id en result"); + return; + } + const jobData = await client.cleaningJob(cleaningJobId); + setCleaningJob(jobData); + if (jobData.status === "completed") { + const response = await client.cleaningJobRows(cleaningJobId, 500, 0); + const data = response.rows ?? []; + setRows(data); + setColumns(Array.from(new Set(data.flatMap((r) => Object.keys(r))))); + } + } catch (e) { + setError(String(e instanceof Error ? e.message : e)); + } finally { + setLoading(false); + } + } + + useEffect(() => { + void load(); + // eslint-disable-next-line react-hooks/exhaustive-deps + }, [task.id]); + + function exportCsv() { + const csv = toCsv( + columns, + rows.map((r) => columns.map((h) => r[h])), + ); + downloadCsv(buildExportCsvFilename("limpieza", job.title), csv); + } + + if (loading) { + return ( +
+ + Cargando resultados de limpieza… +
+ ); + } + + if (error) { + return ( +
+ {error} + +
+ ); + } + + return ( +
+
+
+

+ + {task.title ?? "Limpieza"} +

+ + {cleaningJob?.status ?? task.status} + + {cleaningJob?.source && ( + + · {cleaningJob.source === "csv" ? "CSV" : "extracción"} + + )} +
+ {rows.length > 0 && ( + + )} +
+ + {cleaningJob?.log && cleaningJob.log.length > 0 && ( +
+ Log ({cleaningJob.log.length}) +
    + {cleaningJob.log.map((line, i) => ( +
  • {line}
    • + ))} +
+
+ )} + + {rows.length === 0 ? ( +
+

No se encontraron filas limpiadas.

+
+ ) : ( + + )} +
+ ); +} diff --git a/services/desktop_app/src/renderer/modules/cleaning/CleaningWizard.tsx b/services/desktop_app/src/renderer/modules/cleaning/CleaningWizard.tsx new file mode 100644 index 0000000..fb20cb0 --- /dev/null +++ b/services/desktop_app/src/renderer/modules/cleaning/CleaningWizard.tsx @@ -0,0 +1,967 @@ +import { + AlertCircle, + ArrowRight, + CheckCircle2, + Cpu, + Download, + FileSpreadsheet, + Loader2, + Play, + Sparkles, + Upload, +} from "lucide-react"; +import { useEffect, useRef, useState } from "react"; +import type { + BackgroundProgress, + CleaningAnalysisResponse, + CleaningColumnAnalysis, + CleaningJob, + CleaningPreviewColumn, + CleaningPreviewResponse, + CleaningRuleInfo, + Job, + Task, +} from "../../../shared/types"; +import { CleaningStepper, type CleaningStep } from "../../components/CleaningStepper"; +import { ExtractionResultsTable } from "../../components/ExtractionResultsTable"; +import type { ApiClient } from "../../lib/apiClient"; +import { loadCopModConfig } from "../../lib/cleaningSettings"; +import { downloadCsv, toCsv, buildExportCsvFilename } from "../../lib/csv"; + +type SourceMode = "extraction" | "csv"; + +interface Props { + client: ApiClient; + job: Job; + extractionBatchId?: string; + extractionTaskId?: string; + onTaskCreated?: (task: Task) => void; + onProgress?: (progress: BackgroundProgress) => void; +} + +function batchIdFromTask(task: Task): string { + return String((task.result as Record)?.extraction_batch_id ?? ""); +} + +function formatTaskDate(iso: string): string { + try { + return new Date(iso).toLocaleString("es-CO", { + day: "2-digit", + month: "short", + year: "numeric", + hour: "2-digit", + minute: "2-digit", + }); + } catch { + return iso; + } +} + +export function CleaningWizard({ + client, + job, + extractionBatchId: initialBatchId, + extractionTaskId: initialTaskId, + onTaskCreated, + onProgress, +}: Props) { + const hasHandoff = Boolean(initialBatchId || initialTaskId); + const [step, setStep] = useState(1); + const [maxReached, setMaxReached] = useState(1); + const [source, setSource] = useState(hasHandoff ? "extraction" : "extraction"); + const [extractionTasks, setExtractionTasks] = useState([]); + const [loadingTasks, setLoadingTasks] = useState(true); + const [selectedTaskId, setSelectedTaskId] = useState(initialTaskId ?? null); + const [batchId, setBatchId] = useState(initialBatchId ?? ""); + const [csvFile, setCsvFile] = useState(null); + const [rulesCatalog, setRulesCatalog] = useState([]); + const [analysis, setAnalysis] = useState(null); + const [rulesPerColumn, setRulesPerColumn] = useState>({}); + const [copModFechaColumn, setCopModFechaColumn] = useState>({}); + const [loading, setLoading] = useState(false); + const [running, setRunning] = useState(false); + const [error, setError] = useState(null); + const [resultJob, setResultJob] = useState(null); + const [resultRows, setResultRows] = useState[]>([]); + const [handoffAnalyzed, setHandoffAnalyzed] = useState(false); + const [preview, setPreview] = useState(null); + const [previewLoading, setPreviewLoading] = useState(false); + const [previewError, setPreviewError] = useState(null); + const [activeColumn, setActiveColumn] = useState(null); + const [sampleMode, setSampleMode] = useState<"before" | "after">("before"); + const fileInputRef = useRef(null); + const previewTimerRef = useRef | null>(null); + + function goToStep(next: CleaningStep) { + setStep(next); + setMaxReached((m) => (next > m ? next : m)); + } + + useEffect(() => { + client.cleaningRules().then(setRulesCatalog).catch(() => setRulesCatalog([])); + }, [client]); + + useEffect(() => { + let cancelled = false; + async function loadExtractions() { + setLoadingTasks(true); + try { + const tasks = await client.jobTasks(job.id); + if (cancelled) return; + const extractions = tasks + .filter((t) => t.type === "extraction" && t.status === "completed") + .sort((a, b) => new Date(b.created_at).getTime() - new Date(a.created_at).getTime()); + setExtractionTasks(extractions); + + // Preselección por handoff o la más reciente + if (initialTaskId && extractions.some((t) => t.id === initialTaskId)) { + const t = extractions.find((x) => x.id === initialTaskId)!; + setSelectedTaskId(t.id); + setBatchId(batchIdFromTask(t) || initialBatchId || ""); + setSource("extraction"); + } else if (initialBatchId) { + const match = extractions.find((t) => batchIdFromTask(t) === initialBatchId); + if (match) { + setSelectedTaskId(match.id); + setBatchId(initialBatchId); + } else { + setBatchId(initialBatchId); + } + setSource("extraction"); + } else if (extractions.length > 0 && !selectedTaskId) { + const newest = extractions[0]; + setSelectedTaskId(newest.id); + setBatchId(batchIdFromTask(newest)); + } + } catch { + if (!cancelled) setExtractionTasks([]); + } finally { + if (!cancelled) setLoadingTasks(false); + } + } + void loadExtractions(); + return () => { cancelled = true; }; + // eslint-disable-next-line react-hooks/exhaustive-deps + }, [client, job.id, initialBatchId, initialTaskId]); + + // Handoff: analizar automáticamente al llegar desde Resultados + useEffect(() => { + if (!hasHandoff || handoffAnalyzed || loadingTasks) return; + const id = batchId || initialBatchId; + if (!id) return; + setHandoffAnalyzed(true); + void loadAnalysis("extraction", id, null); + // eslint-disable-next-line react-hooks/exhaustive-deps + }, [hasHandoff, handoffAnalyzed, loadingTasks, batchId, initialBatchId]); + + function selectExtraction(task: Task) { + setSelectedTaskId(task.id); + setBatchId(batchIdFromTask(task)); + setSource("extraction"); + setError(null); + } + + function ruleName(id: string): string { + return rulesCatalog.find((r) => r.id === id)?.name ?? id; + } + + function guessFechaColumn(columns: CleaningColumnAnalysis[], amountCol: string): string { + const candidates = columns.filter( + (c) => + c.name !== amountCol && + (c.inferred_type === "date" || + /fecha|date|servicio|fec[_ ]/i.test(c.name)), + ); + const preferred = + candidates.find((c) => /fecha\s*servicio|fec[_ ]?serv/i.test(c.name)) ?? + candidates.find((c) => c.inferred_type === "date") ?? + candidates[0]; + return preferred?.name ?? ""; + } + + function toggleRule(column: string, ruleId: string) { + setRulesPerColumn((prev) => { + const current = prev[column] ?? []; + const turningOn = !current.includes(ruleId); + const next = turningOn + ? [...current, ruleId] + : current.filter((r) => r !== ruleId); + return { ...prev, [column]: next }; + }); + if (ruleId === "cop_mod_split") { + setCopModFechaColumn((prev) => { + const current = rulesPerColumn[column] ?? []; + const turningOn = !current.includes(ruleId); + if (!turningOn) { + const next = { ...prev }; + delete next[column]; + return next; + } + if (prev[column]) return prev; + const guess = analysis ? guessFechaColumn(analysis.columns, column) : ""; + return guess ? { ...prev, [column]: guess } : prev; + }); + } + setSampleMode("after"); + } + + function suggestRules(columns: CleaningColumnAnalysis[]) { + const suggested: Record = {}; + for (const col of columns) { + const applicable = col.applicable_rules ?? []; + suggested[col.name] = applicable.includes("strip_spaces") ? ["strip_spaces"] : []; + } + setRulesPerColumn(suggested); + setCopModFechaColumn({}); + } + + function cleaningExtraPayload(): { + cop_mod_fecha_column?: Record; + cop_mod_config: ReturnType; + } { + const fechaEntries = Object.entries(copModFechaColumn).filter( + ([col, fecha]) => + Boolean(fecha) && (rulesPerColumn[col] ?? []).includes("cop_mod_split"), + ); + return { + ...(fechaEntries.length > 0 + ? { cop_mod_fecha_column: Object.fromEntries(fechaEntries) } + : {}), + cop_mod_config: loadCopModConfig(), + }; + } + + function previewColumn(name: string): CleaningPreviewColumn | undefined { + return preview?.columns.find((c) => c.name === name); + } + + function displaySamples(col: CleaningColumnAnalysis): string[] { + if (col.samples && col.samples.length > 0) return col.samples; + if (col.example) return [String(col.example)]; + return []; + } + + /** Columnas nuevas respecto al análisis (p. ej. *_doc_tipo / *_doc_numeros). */ + function derivedColumnNames(sourceCol: string): string[] { + if (!preview) return []; + const original = new Set(analysis?.columns.map((c) => c.name) ?? []); + return preview.columns + .map((c) => c.name) + .filter( + (name) => + !original.has(name) && + name.toLowerCase().startsWith(`${sourceCol.toLowerCase()}_`), + ); + } + + /** Orden: columna activa → columnas derivadas → resto (sin cortar). */ + function orderedSampleColumnNames(mode: "before" | "after"): string[] { + const analysisNames = analysis?.columns.map((c) => c.name) ?? []; + const names = + mode === "after" && preview + ? preview.columns.map((c) => c.name) + : analysisNames; + const focus = activeColumn; + if (!focus || !names.includes(focus)) return names; + const derived = names.filter( + (n) => n !== focus && n.toLowerCase().startsWith(`${focus.toLowerCase()}_`), + ); + const rest = names.filter((n) => n !== focus && !derived.includes(n)); + return [focus, ...derived, ...rest]; + } + + // Preview en vivo al cambiar reglas (debounce 300ms) + useEffect(() => { + if (step !== 2 || !analysis) return; + const sampleRows = analysis.sample_rows ?? []; + if (previewTimerRef.current) clearTimeout(previewTimerRef.current); + + previewTimerRef.current = setTimeout(() => { + void (async () => { + setPreviewLoading(true); + setPreviewError(null); + try { + const extra = cleaningExtraPayload(); + const payload = + sampleRows.length > 0 + ? { rows: sampleRows, rules_per_column: rulesPerColumn, ...extra } + : batchId + ? { + extraction_batch_id: batchId, + rules_per_column: rulesPerColumn, + ...extra, + } + : null; + if (!payload) { + setPreview(null); + return; + } + const data = await client.previewCleaning(payload); + setPreview(data); + } catch (e) { + setPreviewError(String(e instanceof Error ? e.message : e)); + } finally { + setPreviewLoading(false); + } + })(); + }, 300); + + return () => { + if (previewTimerRef.current) clearTimeout(previewTimerRef.current); + }; + }, [step, analysis, rulesPerColumn, copModFechaColumn, batchId, client]); + + async function loadAnalysis(mode: SourceMode, batch?: string, file?: File | null) { + setLoading(true); + setError(null); + setAnalysis(null); + onProgress?.({ + module: "cleaning", + total: 1, + processed: 0, + failed: 0, + currentFile: mode === "csv" ? (file?.name ?? "CSV") : "extracción", + running: true, + label: "Analizando columnas…", + }); + try { + let data: CleaningAnalysisResponse; + if (mode === "extraction") { + if (!batch) throw new Error("Selecciona una extracción de la lista"); + data = await client.analyzeCleaningBatch(batch); + } else { + if (!file) throw new Error("Selecciona un archivo CSV"); + data = await client.analyzeCleaningCsv(file, file.name); + } + setAnalysis(data); + suggestRules(data.columns); + setActiveColumn(data.columns[0]?.name ?? null); + setSampleMode("before"); + setPreview(null); + goToStep(2); + } catch (e) { + setError(String(e instanceof Error ? e.message : e)); + } finally { + setLoading(false); + onProgress?.({ + module: "cleaning", + total: 1, + processed: 1, + failed: 0, + currentFile: "", + running: false, + label: "", + }); + } + } + + async function handleAnalyze() { + await loadAnalysis(source, batchId, csvFile); + } + + async function handleRun() { + setRunning(true); + setError(null); + setResultJob(null); + setResultRows([]); + onProgress?.({ + module: "cleaning", + total: 1, + processed: 0, + failed: 0, + currentFile: source === "csv" ? (csvFile?.name ?? "CSV") : "extracción", + running: true, + label: "Aplicando reglas de limpieza…", + }); + try { + let cleaningJob: CleaningJob; + if (source === "extraction") { + if (!batchId) throw new Error("Selecciona una extracción"); + cleaningJob = await client.runCleaning({ + extraction_batch_id: batchId, + job_id: job.id, + rules_per_column: rulesPerColumn, + ...cleaningExtraPayload(), + }); + } else { + if (!csvFile) throw new Error("Selecciona un archivo CSV"); + const form = new FormData(); + const extra = cleaningExtraPayload(); + form.append("file", csvFile, csvFile.name); + form.append("job_id", job.id); + form.append("rules_per_column", JSON.stringify(rulesPerColumn)); + if (extra.cop_mod_fecha_column) { + form.append("cop_mod_fecha_column", JSON.stringify(extra.cop_mod_fecha_column)); + } + form.append("cop_mod_config", JSON.stringify(extra.cop_mod_config)); + cleaningJob = await client.runCleaningFromCsv(form); + } + setResultJob(cleaningJob); + if (cleaningJob.status === "completed") { + const rowsResp = await client.cleaningJobRows(cleaningJob.id, 500, 0); + setResultRows(rowsResp.rows ?? []); + } + try { + const tasks = await client.jobTasks(job.id); + const latest = tasks + .filter((t) => t.type === "cleaning") + .sort((a, b) => new Date(b.created_at).getTime() - new Date(a.created_at).getTime())[0]; + if (latest) onTaskCreated?.(latest); + } catch { /* silent */ } + goToStep(3); + } catch (e) { + setError(String(e instanceof Error ? e.message : e)); + } finally { + setRunning(false); + onProgress?.({ + module: "cleaning", + total: 1, + processed: 1, + failed: 0, + currentFile: "", + running: false, + label: "", + }); + } + } + + function exportCsv() { + if (resultRows.length === 0) return; + const columns = Array.from(new Set(resultRows.flatMap((r) => Object.keys(r)))); + const csv = toCsv( + columns, + resultRows.map((r) => columns.map((h) => r[h])), + ); + downloadCsv(buildExportCsvFilename("limpieza", job.title), csv); + } + + function resetWizard() { + setStep(1); + setMaxReached(1); + setResultJob(null); + setResultRows([]); + setAnalysis(null); + setHandoffAnalyzed(true); // no re-auto-analizar + setError(null); + } + + const resultColumns = + resultRows.length > 0 + ? Array.from(new Set(resultRows.flatMap((r) => Object.keys(r)))) + : []; + + const selectedExtraction = extractionTasks.find((t) => t.id === selectedTaskId) ?? null; + const canAnalyze = + source === "extraction" ? Boolean(batchId) : Boolean(csvFile); + + return ( +
+ + +
+ {error && ( +
+ {error} +
+ )} + + {/* ── PASO 1: Fuente ── */} + {step === 1 && ( +
+
+

Origen de los datos

+

+ Elige una extracción completada de este trabajo o sube un CSV para limpiar. +

+
+ +
+
+ + +
+
+ + {source === "extraction" ? ( +
+ + {loadingTasks ? ( +
+ Cargando extracciones… +
+ ) : extractionTasks.length === 0 ? ( +
+ No hay extracciones completadas en este trabajo. + +
+ ) : ( +
+ {extractionTasks.map((task, index) => { + const selected = task.id === selectedTaskId; + return ( + + ); + })} +
+ )} + {selectedExtraction && ( +

+ Seleccionada: {selectedExtraction.title ?? "Extracción"} +

+ )} +
+ ) : ( +
+ + setCsvFile(e.target.files?.[0] ?? null)} + /> +
+
+ {csvFile ? ( + <> + + {csvFile.name} + + ) : ( + Ningún archivo seleccionado + )} +
+ +
+
+ )} + +
+ +
+
+ )} + + {/* ── PASO 2: Reglas ── */} + {step === 2 && ( +
+
+
+

Reglas por columna

+

+ Elige una columna, aplica reglas y mira el cambio al instante. + {analysis?.row_count != null ? ` · ${analysis.row_count} filas` : ""} + {previewLoading ? " · actualizando…" : ""} +

+
+ {previewLoading && } +
+ + {loading && !analysis ? ( +
+ Analizando… +
+ ) : analysis ? ( +
+ {/* Lista compacta de columnas */} + + + {/* Detalle de la columna activa */} +
+ {previewError && ( +
+ {previewError} +
+ )} + + {(() => { + const col = analysis.columns.find((c) => c.name === activeColumn) ?? analysis.columns[0]; + if (!col) return

Sin columnas.

; + const applicable = col.applicable_rules ?? []; + const selected = rulesPerColumn[col.name] ?? []; + const beforeFromSamples = displaySamples(col); + const pv = previewColumn(col.name); + const beforeVals = pv?.before?.length ? pv.before.slice(0, 4) : beforeFromSamples.slice(0, 4); + const derived = derivedColumnNames(col.name); + const afterVals = pv?.after?.length + ? pv.after.slice(0, 4) + : selected.length === 0 + ? beforeVals + : []; + const sourceUnchanged = + beforeVals.length > 0 && + afterVals.length > 0 && + beforeVals.every((v, i) => String(v) === String(afterVals[i] ?? "")); + const changed = Boolean(pv?.changed) || derived.length > 0; + + return ( + <> +
+
+

{col.name}

+
+ {col.inferred_type} + {col.null_count != null && ( + nulos {col.null_count}{col.null_pct != null ? ` (${Math.round(col.null_pct)}%)` : ""} + )} + {col.unique_count != null && únicos {col.unique_count}} +
+
+
+ +
+
+
Antes
+
+ {beforeVals.length === 0 ? ( + Sin valores + ) : ( + beforeVals.map((v, i) => ( + {v || "—"} + )) + )} +
+
+ +
+
+ Después +
+
+ {derived.length > 0 ? ( + derived.map((dName) => { + const dPv = previewColumn(dName); + const sample = (dPv?.after ?? []).find((v) => v) ?? (dPv?.after?.[0] ?? ""); + return ( + + {dName.replace(/^.*_doc_/, "_doc_")} + {sample || "—"} + + ); + }) + ) : afterVals.length === 0 ? ( + + {previewLoading ? "…" : "Activa una regla"} + + ) : sourceUnchanged ? ( + Sin cambio en origen + ) : ( + afterVals.map((v, i) => { + const before = beforeVals[i] ?? ""; + const isDiff = String(v) !== String(before); + return ( + + {v || "—"} + + ); + }) + )} +
+
+
+ +
+
Reglas
+ {applicable.length === 0 ? ( + Sin reglas para este tipo + ) : ( +
+ {applicable.map((ruleId) => { + const on = selected.includes(ruleId); + const info = rulesCatalog.find((r) => r.id === ruleId); + return ( + + ); + })} +
+ )} + {selected.length > 0 && ( +

+ {selected + .map((id) => rulesCatalog.find((r) => r.id === id)?.description || id) + .join(" · ")} +

+ )} + {selected.includes("cop_mod_split") && ( +
+ + +

+ Las cuotas por año se editan en Configuración → Limpieza. +

+
+ )} +
+ + ); + })()} + + {/* Una sola tabla de muestra: Antes | Después */} + {((analysis.sample_rows?.length ?? 0) > 0 || (preview?.rows_after.length ?? 0) > 0) && ( +
+
+ Muestra +
+ + +
+
+
+ + + + {orderedSampleColumnNames(sampleMode).map((name) => { + const isNew = !(analysis.columns.some((c) => c.name === name)); + const changed = preview?.columns.find((x) => x.name === name)?.changed; + return ( + + ); + })} + + + + {(sampleMode === "after" && preview + ? preview.rows_after + : analysis.sample_rows ?? preview?.rows_before ?? [] + ).slice(0, 4).map((row, i) => ( + + {orderedSampleColumnNames(sampleMode).map((name) => { + const val = row[name]; + const before = preview?.rows_before[i]?.[name]; + const isNew = !(analysis.columns.some((c) => c.name === name)); + const diff = + sampleMode === "after" && + (isNew || String(before ?? "") !== String(val ?? "")); + return ( + + ); + })} + + ))} + +
+ {name} +
+ {val == null || val === "" + ? — + : String(val).slice(0, 36)} +
+
+
+ )} +
+
+ ) : ( +

No hay análisis. Vuelve al paso Fuente.

+ )} + +
+ + +
+
+ )} + + {/* ── PASO 3: Resultados ── */} + {step === 3 && ( +
+
+

Resultado de la limpieza

+
+ + {resultJob?.status === "completed" ? ( + <> +
+ + + Limpieza completada + {resultJob.log?.length ? ` · ${resultJob.log.length} operaciones` : null} + {resultRows.length > 0 ? ` · ${resultRows.length} filas` : null} + +
+ + {resultJob.log && resultJob.log.length > 0 && ( +
+ Ver log de operaciones +
+
    + {resultJob.log.map((line, i) => ( +
  • {line}
    • + ))} +
+
+
+ )} + +
+ + Vista previa{resultRows.length > 100 ? " (primeras 100)" : ""} + + {resultRows.length > 0 && ( + + )} +
+ + {resultRows.length > 0 ? ( + + ) : ( +

Sin filas en el resultado.

+ )} + + ) : ( +
+ + {resultJob?.error ?? "La limpieza no se completó"} +
+ )} + +
+ + +
+
+ )} +
+
+ ); +} diff --git a/services/desktop_app/src/renderer/modules/cleaning/CleaningWizardModule.tsx b/services/desktop_app/src/renderer/modules/cleaning/CleaningWizardModule.tsx new file mode 100644 index 0000000..41cb367 --- /dev/null +++ b/services/desktop_app/src/renderer/modules/cleaning/CleaningWizardModule.tsx @@ -0,0 +1,26 @@ +import type { BackgroundProgress } from "../../../shared/types"; +import type { ModuleWizardProps } from "../registry"; +import { CleaningWizard } from "./CleaningWizard"; + +interface Props extends ModuleWizardProps { + onProgress?: (p: BackgroundProgress) => void; +} + +export function CleaningWizardModule({ + client, + job, + onTaskCreated, + onProgress, + context, +}: Props) { + return ( + + ); +} diff --git a/services/desktop_app/src/renderer/modules/counting/CountingResults.tsx b/services/desktop_app/src/renderer/modules/counting/CountingResults.tsx index 1e5d848..b0becd7 100644 --- a/services/desktop_app/src/renderer/modules/counting/CountingResults.tsx +++ b/services/desktop_app/src/renderer/modules/counting/CountingResults.tsx @@ -2,6 +2,7 @@ import { ArrowRight, Download, Loader2, RefreshCw } from "lucide-react"; import { useEffect, useState } from "react"; import type { CountJobFile } from "../../../shared/types"; import { CountResultsTable } from "../../components/CountResultsTable"; +import { downloadCsv, toCsv, buildExportCsvFilename } from "../../lib/csv"; import type { ModuleResultsProps } from "../registry"; export function CountingResults({ @@ -47,25 +48,16 @@ export function CountingResults({ : files; const headers = ["filename", "radicado", "service_count", "status", "is_valid", "error"]; - const rows = subset_files.map((f) => - [ - `"${f.filename}"`, - `"${f.radicado ?? ""}"`, - f.service_count ?? "", - f.status, - f.is_valid === null ? "" : String(f.is_valid), - `"${(f.error ?? "").replace(/"/g, '""')}"`, - ].join(","), - ); - const csv = [headers.join(","), ...rows].join("\n"); - const blob = new Blob([csv], { type: "text/csv" }); - const url = URL.createObjectURL(blob); - const a = document.createElement("a"); - a.href = url; + const rows = subset_files.map((f) => [ + f.filename, + f.radicado ?? "", + f.service_count ?? "", + f.status, + f.is_valid === null ? "" : String(f.is_valid), + f.error ?? "", + ]); const label = subset === "valid" ? "validos" : subset === "invalid" ? "invalidos" : "todos"; - a.download = `conteo-${label}-${new Date().toISOString().slice(0, 10)}.csv`; - a.click(); - URL.revokeObjectURL(url); + downloadCsv(buildExportCsvFilename("conteo", job.title, label), toCsv(headers, rows)); } const validCount = files.filter(isFileValid).length; diff --git a/services/desktop_app/src/renderer/modules/extraction/ExtractionResults.tsx b/services/desktop_app/src/renderer/modules/extraction/ExtractionResults.tsx index 25e6e26..de07aea 100644 --- a/services/desktop_app/src/renderer/modules/extraction/ExtractionResults.tsx +++ b/services/desktop_app/src/renderer/modules/extraction/ExtractionResults.tsx @@ -1,11 +1,13 @@ -import { Download, Loader2, RefreshCw } from "lucide-react"; +import { ArrowRight, Download, Loader2, RefreshCw, Sparkles } from "lucide-react"; import { useEffect, useState } from "react"; import type { ExtractionBatch } from "../../../shared/types"; +import { ExtractionResultsTable } from "../../components/ExtractionResultsTable"; +import { downloadCsv, toCsv, buildExportCsvFilename } from "../../lib/csv"; import type { ModuleResultsProps } from "../registry"; type Row = Record; -export function ExtractionResults({ client, task }: ModuleResultsProps) { +export function ExtractionResults({ client, task, job, onActivateModule }: ModuleResultsProps) { const [batch, setBatch] = useState(null); const [rows, setRows] = useState([]); const [columns, setColumns] = useState([]); @@ -54,25 +56,24 @@ export function ExtractionResults({ client, task }: ModuleResultsProps) { }, [task.id]); // eslint-disable-line react-hooks/exhaustive-deps function exportCsv() { - const headers = columns; - const csvRows = rows.map((r) => - headers - .map((h) => { - const v = r[h]; - if (v === null || v === undefined) return ""; - const s = String(v); - return `"${s.replace(/"/g, '""')}"`; - }) - .join(","), + const csv = toCsv( + columns, + rows.map((r) => columns.map((h) => r[h])), ); - const csv = [headers.join(","), ...csvRows].join("\n"); - const blob = new Blob([csv], { type: "text/csv" }); - const url = URL.createObjectURL(blob); - const a = document.createElement("a"); - a.href = url; - a.download = `extraccion-${new Date().toISOString().slice(0, 10)}.csv`; - a.click(); - URL.revokeObjectURL(url); + downloadCsv(buildExportCsvFilename("extraccion", job.title), csv); + } + + function startCleaning() { + const batchId = String( + (task.result as Record)?.extraction_batch_id + ?? batch?.id + ?? "", + ); + onActivateModule?.("cleaning", { + extractionBatchId: batchId || undefined, + extractionTaskId: task.id, + jobId: job.id, + }); } if (loading && !batch) { @@ -109,15 +110,22 @@ export function ExtractionResults({ client, task }: ModuleResultsProps) { {batch?.status ?? "pendiente"}
- {rows.length > 0 && ( - - )} +
+ {rows.length > 0 && ( + + )} + {rows.length > 0 && !isRunning && onActivateModule && ( + + )} +

{isRunning && ( -
+
Extracción en progreso… @@ -132,32 +140,9 @@ export function ExtractionResults({ client, task }: ModuleResultsProps) {

No se encontraron filas extraídas.

- ) : ( -
- - - - {columns.map((c) => ( - - ))} - - - - {rows.map((row, i) => ( - - {columns.map((c) => ( - - ))} - - ))} - -
{c}
- {row[c] === null || row[c] === undefined - ? — - : String(row[c])} -
-
- )} + ) : rows.length > 0 ? ( + + ) : null}
); } diff --git a/services/desktop_app/src/renderer/modules/extraction/JobExtractionWizard.tsx b/services/desktop_app/src/renderer/modules/extraction/JobExtractionWizard.tsx index 779948b..3a58981 100644 --- a/services/desktop_app/src/renderer/modules/extraction/JobExtractionWizard.tsx +++ b/services/desktop_app/src/renderer/modules/extraction/JobExtractionWizard.tsx @@ -30,6 +30,7 @@ import { defaultAlias } from "../../lib/schemaDisplay"; import { loadApiBaseUrl } from "../../lib/apiDefaults"; import { splitRadicados } from "../../lib/pathMapping"; import type { ModuleWizardProps } from "../registry"; +import { ExtractionResults } from "./ExtractionResults"; declare const desktopApi: import("../../../shared/types").DesktopBridge; @@ -71,6 +72,7 @@ export function JobExtractionWizard({ client, job, onTaskCreated, + onActivateModule, context, countingTaskId: countingTaskIdProp, validCounts: validCountsProp, @@ -95,6 +97,9 @@ export function JobExtractionWizard({ const [extractionStep, setExtractionStep] = useState(() => countingTaskId ? 2 : 1); const [maxReachedStep, setMaxReachedStep] = useState(() => countingTaskId ? 2 : 1); const [taskCreated, setTaskCreated] = useState(null); + /** Rutas de muestras ya usadas en descubrimiento (para no repetir al reintentar). */ + const [usedDiscoveryPaths, setUsedDiscoveryPaths] = useState([]); + const [discoverySampleLabels, setDiscoverySampleLabels] = useState([]); const flattened = useMemo( () => (schemaResponse ? flattenSchema(schemaResponse) : { schema: {}, sample: null }), @@ -151,6 +156,8 @@ export function JobExtractionWizard({ setSchemaResponse(null); setSelection({ fields: [], aliasMap: {} }); setEstimateJson(null); + setUsedDiscoveryPaths([]); + setDiscoverySampleLabels([]); setMaxReachedStep(1); if (extractionStep > 1) setExtractionStep(1); } @@ -167,7 +174,6 @@ export function JobExtractionWizard({ resetSchemaState(); setMaxReachedStep(1); setStatusText(`${indexed.length} documento(s) listos`); - // Actualizar carpeta del job try { await client.updateJob(job.id, { folder_path: selected }); } catch { /* ignore */ } } catch (err) { setManifest([]); @@ -190,41 +196,91 @@ export function JobExtractionWizard({ } } - async function discoverSchema() { - await runTask("Descubriendo campos...", async () => { - if (!window.desktopApi) throw new Error("Requiere Electron."); - if (manifest.length === 0) throw new Error("Selecciona una carpeta con documentos."); + function pickSampleEntry( + candidates: ManifestEntry[], + exclude: Set, + preferRandom: boolean, + ): ManifestEntry { + if (candidates.length === 0) { + throw new Error("No hay documentos candidatos para la muestra."); + } + const unused = candidates.filter((e) => !exclude.has(e.localPath)); + const pool = unused.length > 0 ? unused : candidates; + if (!preferRandom && unused.length === candidates.length) { + return candidates[0]; + } + return pool[Math.floor(Math.random() * pool.length)]!; + } - const responses: SchemaResponse[] = []; - if (form.mode === "general") { - const entry = manifest[0]; - const fileData = await window.desktopApi.readFile(entry.localPath); - const blob = new Blob([fileData.data]); - responses.push(await client.discoverSchemaUpload(buildSchemaUploadForm(form, blob, fileData.filename))); - } else { - for (const docType of validDocTypes(form.docTypes)) { - const entry = manifest.find((item) => item.docType === docType.name); - if (!entry) throw new Error(`No se encontró muestra para ${docType.name}.`); + async function discoverSchema(options?: { randomOther?: boolean }) { + const randomOther = Boolean(options?.randomOther); + await runTask( + randomOther ? "Probando con otro archivo…" : "Descubriendo campos...", + async () => { + if (!window.desktopApi) throw new Error("Requiere Electron."); + if (manifest.length === 0) throw new Error("Selecciona una carpeta con documentos."); + + const exclude = new Set(randomOther ? usedDiscoveryPaths : []); + const responses: SchemaResponse[] = []; + const picked: ManifestEntry[] = []; + + if (form.mode === "general") { + const entry = pickSampleEntry(manifest, exclude, randomOther); + picked.push(entry); const fileData = await window.desktopApi.readFile(entry.localPath); const blob = new Blob([fileData.data]); responses.push( - await client.discoverSchemaUpload(buildSchemaUploadForm(form, blob, fileData.filename, docType.name)), + await client.discoverSchemaUpload( + buildSchemaUploadForm(form, blob, fileData.filename), + ), ); + } else { + for (const docType of validDocTypes(form.docTypes)) { + const typed = manifest.filter((item) => item.docType === docType.name); + if (typed.length === 0) { + throw new Error(`No se encontró muestra para ${docType.name}.`); + } + const entry = pickSampleEntry(typed, exclude, randomOther); + picked.push(entry); + const fileData = await window.desktopApi.readFile(entry.localPath); + const blob = new Blob([fileData.data]); + responses.push( + await client.discoverSchemaUpload( + buildSchemaUploadForm(form, blob, fileData.filename, docType.name), + ), + ); + } } - } - const schema = mergeSchemaResponses(responses); - setSchemaResponse(schema); - const flat = flattenSchema(schema); - setSelection({ - fields: [], - aliasMap: Object.fromEntries(Object.keys(flat.schema).map((p) => [p, defaultAlias(p)])), - }); - setEstimateJson(null); - setStatusText(`${Object.keys(flat.schema).length} campo(s) descubiertos`); - setMaxReachedStep((cur) => (cur < 3 ? 3 : cur)); - setExtractionStep(3); - }); + const schema = mergeSchemaResponses(responses); + setSchemaResponse(schema); + const flat = flattenSchema(schema); + setSelection({ + fields: [], + aliasMap: Object.fromEntries(Object.keys(flat.schema).map((p) => [p, defaultAlias(p)])), + }); + setEstimateJson(null); + const labels = picked.map((e) => e.filename); + setDiscoverySampleLabels(labels); + setUsedDiscoveryPaths((prev) => { + const next = new Set(randomOther ? prev : []); + for (const e of picked) next.add(e.localPath); + return [...next]; + }); + setStatusText( + `${Object.keys(flat.schema).length} campo(s) descubiertos` + + (labels.length ? ` · muestra: ${labels.join(", ")}` : ""), + ); + setMaxReachedStep((cur) => (cur < 3 ? 3 : cur)); + if (!randomOther) { + setExtractionStep(3); + } + }, + ); + } + + async function rediscoverWithOtherSample() { + await discoverSchema({ randomOther: true }); } async function estimate() { @@ -419,14 +475,26 @@ export function JobExtractionWizard({ }); } - // Si ya se creó tarea, muestra mensaje y el módulo Results tomará el relevo + // Tras crear la tarea: mostrar resultados (con polling) en esta misma pestaña if (taskCreated) { return ( -
-

✓ Extracción iniciada

-

- Los resultados aparecerán automáticamente en esta pestaña. -

+
+ {(ingestProgress || busy) && ( +
+ + {statusText || "Extracción en curso…"} +
+ )} + { + setTaskCreated(t); + onTaskCreated(t); + }} + onActivateModule={onActivateModule} + />
); } @@ -461,7 +529,16 @@ export function JobExtractionWizard({ error={error} statusText={statusText} selectRoot={selectRoot} - discoverSchema={discoverSchema} + discoverSchema={() => void discoverSchema()} + rediscoverWithOtherSample={() => void rediscoverWithOtherSample()} + discoverySampleLabels={discoverySampleLabels} + canRediscoverWithOtherSample={ + form.mode === "general" + ? manifest.length > 1 + : validDocTypes(form.docTypes).some( + (dt) => manifest.filter((m) => m.docType === dt.name).length > 1, + ) + } estimate={estimate} runExtraction={runExtraction} selection={selection} diff --git a/services/desktop_app/src/renderer/styles.css b/services/desktop_app/src/renderer/styles.css index ab82bae..c727d36 100644 --- a/services/desktop_app/src/renderer/styles.css +++ b/services/desktop_app/src/renderer/styles.css @@ -73,15 +73,12 @@ button:disabled { } .brand-mark { - align-items: center; - background: linear-gradient(135deg, #2e7db2, #3b9ad9); + background: #0b0b0b; border-radius: 10px; - color: #ffffff; - display: flex; - font-size: 13px; - font-weight: 800; + display: block; + flex-shrink: 0; height: 34px; - justify-content: center; + object-fit: contain; width: 34px; } @@ -289,6 +286,212 @@ button:disabled { flex: 1; } +.settings-page { + display: flex; + flex: 1; + flex-direction: column; + min-height: 0; + overflow: hidden; +} + +.settings-page-header { + border-bottom: 1px solid #e8edf4; + flex-shrink: 0; + padding: 20px 28px 16px; +} + +.settings-page-header h2 { + font-size: 18px; + font-weight: 700; + margin: 0 0 6px; +} + +.settings-page-header p { + color: #64748b; + font-size: 13px; + line-height: 1.45; + margin: 0; + max-width: 640px; +} + +.settings-page-header-row { + align-items: flex-start; + display: flex; + gap: 16px; + justify-content: space-between; +} + +.settings-page-actions { + align-items: center; + display: flex; + flex-shrink: 0; + gap: 8px; +} + +.settings-saved-badge { + background: #dcfce7; + border-radius: 999px; + color: #15803d; + font-size: 11px; + font-weight: 600; + padding: 4px 10px; +} + +.settings-page-body { + display: flex; + flex: 1; + flex-direction: column; + gap: 16px; + min-height: 0; + overflow-y: auto; + padding: 20px 28px 28px; +} + +.settings-page-body--fill { + overflow: hidden; +} + +.settings-panel { + background: #ffffff; + border: 1px solid #e3e8ef; + border-radius: 12px; + display: flex; + flex-direction: column; + gap: 14px; + max-width: 720px; + padding: 18px 20px; +} + +.settings-panel-title { + font-size: 14px; + font-weight: 700; + margin: 0; +} + +.copmod-year-list { + display: flex; + flex-direction: column; + gap: 10px; +} + +.copmod-year-row { + align-items: center; + display: grid; + gap: 10px; + grid-template-columns: 64px minmax(0, 1fr) auto; +} + +.copmod-year-label { + color: #334155; + font-size: 13px; + font-weight: 700; +} + +.copmod-add-row { + align-items: center; + display: flex; + gap: 10px; + margin-top: 4px; +} + +.copmod-add-year { + max-width: 120px; +} + +.cleaning-copmod-fecha { + display: flex; + flex-direction: column; + gap: 6px; + margin-top: 12px; + max-width: 420px; +} + +.settings-status-summary { + color: #15803d; + font-size: 12px; + font-weight: 600; +} + +.settings-status-card--error .settings-status-summary { + color: #b91c1c; +} + +.settings-service-grid { + display: grid; + gap: 10px; + grid-template-columns: repeat(auto-fill, minmax(160px, 1fr)); + max-width: 720px; +} + +.settings-service-chip { + align-items: center; + background: #ffffff; + border: 1px solid #e3e8ef; + border-radius: 10px; + display: flex; + gap: 10px; + padding: 12px 14px; +} + +.settings-service-chip--ok { + background: #f0fdf6; + border-color: #bbf7d0; +} + +.settings-service-chip--err { + background: #fff5f5; + border-color: #fecaca; +} + +.settings-service-chip-dot { + background: #94a3b8; + border-radius: 50%; + flex-shrink: 0; + height: 8px; + width: 8px; +} + +.settings-service-chip--ok .settings-service-chip-dot { + background: #16a34a; +} + +.settings-service-chip--err .settings-service-chip-dot { + background: #dc2626; +} + +.settings-service-chip-text { + display: flex; + flex-direction: column; + gap: 2px; + min-width: 0; +} + +.settings-service-chip-text strong { + color: #18212f; + font-size: 13px; + font-weight: 700; +} + +.settings-service-chip-text span { + color: #64748b; + font-size: 11.5px; +} + +.settings-url-row { + align-items: stretch; + display: flex; + gap: 10px; +} + +.settings-url-row .form-input { + flex: 1; + min-width: 0; +} + +.settings-url-row .btn { + flex-shrink: 0; +} + .topbar-inline { align-items: center; display: flex; @@ -1520,7 +1723,7 @@ th { align-items: center; display: flex; flex-shrink: 0; - padding: 0 24px 24px; + padding: 20px 24px 24px; } .stepper-item { @@ -1620,6 +1823,15 @@ th { padding: 0 2px 8px; } +.job-extraction-results-pane { + display: flex; + flex: 1; + flex-direction: column; + min-height: 0; + overflow: auto; + padding: 8px 4px 16px; +} + .wiz-step { display: flex; flex-direction: column; @@ -1995,6 +2207,52 @@ th { padding: 10px 16px; } +.wiz-rediscover-card { + align-items: flex-start; + background: #f8fafc; + border: 1px solid #e3e8ef; + border-radius: 10px; + display: flex; + flex-wrap: wrap; + gap: 12px; + justify-content: space-between; + margin-top: 14px; + max-width: 560px; + padding: 14px 16px; +} + +.wiz-rediscover-card--compact { + margin-bottom: 12px; + margin-top: 0; + max-width: none; +} + +.wiz-rediscover-text { + display: flex; + flex: 1; + flex-direction: column; + gap: 4px; + min-width: 180px; +} + +.wiz-rediscover-text strong { + color: #18212f; + font-size: 13px; + font-weight: 700; +} + +.wiz-rediscover-text span { + color: #64748b; + font-size: 12px; + line-height: 1.45; +} + +.wiz-rediscover-text em { + color: #334155; + font-style: normal; + font-weight: 600; +} + /* ═══════════════════════════════════════════ Review grid (step 4) ═══════════════════════════════════════════ */ @@ -2496,9 +2754,13 @@ th { .count-results { display: flex; + flex: 1 1 auto; flex-direction: column; gap: 16px; margin-top: 12px; + min-height: 0; + overflow: hidden; + padding: 0 16px 16px; } /* Summary stat overrides */ @@ -2626,7 +2888,9 @@ th { .count-table-wrap { border: 1px solid #e8edf4; border-radius: 10px; - overflow: hidden; + flex: 1 1 auto; + min-height: 0; + overflow: auto; } .count-table { @@ -3332,7 +3596,7 @@ th { background: none; border: none; border-radius: 6px; - color: var(--text-muted); + color: #8a9ab4; cursor: pointer; display: flex; font-size: 12.5px; @@ -3344,27 +3608,33 @@ th { } .side-job-back:hover { - background: var(--border); - color: var(--text); + background: rgba(255, 255, 255, 0.05); + color: #c8d5e4; } .side-job-name { - color: var(--text); - font-size: 13px; - font-weight: 700; - margin: 4px 12px 6px; + color: #c8d5e4; + font-size: 12.5px; + font-weight: 600; + line-height: 1.35; + margin: 2px 12px 10px; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; } .side-section-divider { - background: var(--border); + background: rgba(255, 255, 255, 0.06); + flex-shrink: 0; height: 1px; - margin: 4px 10px; + margin: 8px 10px; } .side-nav--bottom { + display: flex; + flex-direction: column; + flex-shrink: 0; + gap: 2px; margin-top: auto; } @@ -3700,8 +3970,49 @@ th { .task-badge--failed { background: #fee2e2; color: #b91c1c; } /* ── Results table (extracción) ── */ +.extraction-results { + display: flex; + flex: 1 1 auto; + flex-direction: column; + gap: 16px; + min-height: 0; + overflow: hidden; + padding: 12px 16px 16px; +} + +.extraction-table-wrap { + border: 1px solid #e8edf4; + border-radius: 10px; + flex: 1 1 auto; + min-height: 0; + overflow: auto; + padding: 0; +} + +.extraction-empty { + color: var(--text-muted); + padding: 28px 12px !important; + text-align: center; +} + +.extraction-empty-cell { + color: var(--text-muted); +} + +.extraction-row--incomplete td { + background: #fefce8; +} + +.extraction-filter-hint { + color: var(--text-muted); + flex-shrink: 0; + font-size: 12px; + margin: 0; +} + .results-table-wrapper { flex: 1; + min-height: 0; overflow: auto; padding: 0 16px 16px; } @@ -3953,3 +4264,445 @@ th { margin-left: auto; padding: 3px 10px; } + +/* ── Cleaning wizard: lista de extracciones / reglas ── */ +.cleaning-source-list { + display: flex; + flex-direction: column; + gap: 8px; + max-height: min(280px, 36vh); + overflow-y: auto; +} + +.cleaning-source-item { + align-items: center; + background: #fff; + border: 1px solid #e2e8f0; + border-radius: 10px; + cursor: pointer; + display: flex; + gap: 12px; + justify-content: space-between; + padding: 12px 14px; + text-align: left; + transition: border-color 0.15s, background 0.15s; + width: 100%; +} + +.cleaning-source-item:hover { + border-color: #94a3b8; + background: #f8fafc; +} + +.cleaning-source-item--selected { + border-color: #2563eb; + background: #eff6ff; + box-shadow: 0 0 0 1px #2563eb; +} + +.cleaning-source-item-main { + align-items: flex-start; + display: flex; + gap: 10px; + min-width: 0; +} + +.cleaning-source-item-title { + color: #0f172a; + font-size: 13px; + font-weight: 600; +} + +.cleaning-source-item-meta { + color: #64748b; + font-size: 12px; + margin-top: 2px; +} + +/* ── Paso Reglas: lista fija + detalle con scroll ── */ +.wiz-step--cleaning-rules { + display: flex; + flex: 1; + flex-direction: column; + gap: 12px; + min-height: 0; +} + +.cleaning-rules-toolbar { + align-items: flex-start; + display: flex; + flex-shrink: 0; + gap: 12px; + justify-content: space-between; +} + +.cleaning-rules-workspace { + display: grid; + flex: 1; + gap: 12px; + grid-template-columns: minmax(160px, 220px) minmax(0, 1fr); + min-height: 0; + overflow: hidden; +} + +.cleaning-col-nav { + background: #f8fafc; + border: 1px solid #e2e8f0; + border-radius: 10px; + display: flex; + flex-direction: column; + gap: 2px; + min-height: 0; + overflow-y: auto; + padding: 6px; +} + +.cleaning-col-nav-item { + align-items: center; + background: transparent; + border: none; + border-radius: 8px; + color: #334155; + cursor: pointer; + display: flex; + gap: 8px; + justify-content: space-between; + padding: 8px 10px; + text-align: left; + width: 100%; +} + +.cleaning-col-nav-item:hover { + background: #eef2f7; +} + +.cleaning-col-nav-item--active { + background: #fff; + box-shadow: 0 0 0 1px #2563eb; + color: #0f172a; +} + +.cleaning-col-nav-item--changed:not(.cleaning-col-nav-item--active) { + background: #eff6ff; +} + +.cleaning-col-nav-name { + font-size: 12px; + font-weight: 600; + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; +} + +.cleaning-col-nav-meta { + align-items: center; + display: flex; + flex-shrink: 0; + gap: 4px; +} + +.cleaning-col-nav-count { + background: #2563eb; + border-radius: 999px; + color: #fff; + font-size: 10px; + font-weight: 700; + min-width: 18px; + padding: 1px 6px; + text-align: center; +} + +.cleaning-col-detail { + background: #fff; + border: 1px solid #e2e8f0; + border-radius: 10px; + display: flex; + flex-direction: column; + gap: 12px; + min-height: 0; + min-width: 0; + overflow-x: hidden; + overflow-y: auto; + padding: 12px 14px; +} + +.cleaning-col-detail-header, +.cleaning-preview-compare--compact, +.cleaning-rules-block, +.cleaning-copmod-fecha { + flex-shrink: 0; +} + +.cleaning-col-detail-title { + font-size: 15px; + font-weight: 700; + margin: 0; +} + +.cleaning-rules-block { + display: flex; + flex-direction: column; + gap: 6px; +} + +.cleaning-rule-desc { + margin: 0; +} + +.cleaning-preview-compare--compact { + background: #f8fafc; + border: 1px solid #e8edf4; + border-radius: 8px; + margin: 0; + padding: 10px; +} + +.cleaning-preview-compare--changed { + background: #f0f7ff; + border-color: #bfdbfe; +} + +.cleaning-sample-dock { + border-top: 1px solid #e8edf4; + display: flex; + flex: 0 0 auto; + flex-direction: column; + gap: 8px; + min-height: 140px; + padding-top: 10px; +} + +.cleaning-sample-dock-header { + align-items: center; + display: flex; + flex-shrink: 0; + gap: 12px; + justify-content: space-between; +} + +.cleaning-sample-tabs { + background: #eef2f7; + border-radius: 8px; + display: inline-flex; + padding: 2px; +} + +.cleaning-sample-tab { + background: transparent; + border: none; + border-radius: 6px; + color: #64748b; + cursor: pointer; + font-size: 12px; + font-weight: 600; + padding: 4px 10px; +} + +.cleaning-sample-tab:disabled { + cursor: not-allowed; + opacity: 0.45; +} + +.cleaning-sample-tab--active { + background: #fff; + box-shadow: 0 1px 2px rgba(15, 23, 42, 0.08); + color: #0f172a; +} + +.cleaning-sample-table-scroll { + max-height: 220px; + min-height: 0; + overflow: auto; +} + +.cleaning-rules-nav { + flex-shrink: 0; + gap: 8px; + margin-top: 0; +} + +.cleaning-type-badge { + background: #e2e8f0; + border-radius: 999px; + color: #334155; + font-size: 10px; + font-weight: 600; + padding: 2px 7px; +} + +.cleaning-rule-card-meta { + align-items: center; + color: #64748b; + display: flex; + flex-wrap: wrap; + font-size: 12px; + gap: 8px; + margin-top: 4px; +} + +.cleaning-rule-chips { + display: flex; + flex-wrap: wrap; + gap: 6px; +} + +.cleaning-preview-compare { + align-items: center; + display: grid; + gap: 8px; + grid-template-columns: 1fr auto 1fr; +} + +.cleaning-preview-side { + min-width: 0; +} + +.cleaning-preview-label { + color: #64748b; + font-size: 11px; + font-weight: 700; + letter-spacing: 0.02em; + margin-bottom: 6px; + text-transform: uppercase; +} + +.cleaning-preview-label--after { + color: #1d4ed8; +} + +.cleaning-preview-arrow { + color: #94a3b8; + flex-shrink: 0; +} + +.cleaning-preview-values { + display: flex; + flex-wrap: wrap; + gap: 4px; +} + +.cleaning-preview-chip { + background: #f1f5f9; + border: 1px solid #e2e8f0; + border-radius: 6px; + color: #0f172a; + font-family: ui-monospace, SFMono-Regular, Menlo, Consolas, monospace; + font-size: 11px; + max-width: 100%; + overflow: hidden; + padding: 3px 7px; + text-overflow: ellipsis; + white-space: nowrap; +} + +.cleaning-preview-chip--changed { + background: #dbeafe; + border-color: #93c5fd; + color: #1e3a8a; + font-weight: 600; +} + +.cleaning-preview-chip-col { + color: #64748b; + font-weight: 500; + margin-right: 6px; +} + +.cleaning-preview-empty { + color: #94a3b8; + font-size: 12px; +} + +.cleaning-preview-table { + font-size: 12px; +} + +.cleaning-th-changed, +.cleaning-td-changed { + background: #eff6ff; +} + +@media (max-width: 900px) { + .cleaning-rules-workspace { + grid-template-columns: 1fr; + overflow: auto; + } + + .cleaning-col-nav { + display: grid; + grid-auto-flow: column; + grid-auto-columns: max-content; + max-height: none; + overflow-x: auto; + overflow-y: hidden; + } +} + +/* ── Auto-update modal ── */ +.update-modal-backdrop { + align-items: center; + background: rgba(15, 23, 42, 0.45); + display: flex; + inset: 0; + justify-content: center; + position: fixed; + z-index: 1000; +} + +.update-modal { + background: #ffffff; + border-radius: 12px; + box-shadow: 0 20px 50px rgba(15, 23, 42, 0.25); + display: flex; + flex-direction: column; + gap: 12px; + max-width: 440px; + padding: 20px 22px; + width: calc(100% - 32px); +} + +.update-modal-header { + align-items: center; + display: flex; + justify-content: space-between; + gap: 12px; +} + +.update-modal-header h2 { + font-size: 16px; + font-weight: 700; + margin: 0; +} + +.update-modal-body { + color: #334155; + font-size: 13.5px; + line-height: 1.5; + margin: 0; +} + +.update-modal-actions { + display: flex; + gap: 8px; + justify-content: flex-end; + margin-top: 8px; +} + +.update-progress { + display: flex; + flex-direction: column; + gap: 6px; +} + +.update-progress-bar { + background: #e2e8f0; + border-radius: 999px; + height: 8px; + overflow: hidden; +} + +.update-progress-fill { + background: #2563eb; + height: 100%; + transition: width 0.2s ease; +} diff --git a/services/desktop_app/src/renderer/types/jobSection.ts b/services/desktop_app/src/renderer/types/jobSection.ts index fb2469b..c37a28a 100644 --- a/services/desktop_app/src/renderer/types/jobSection.ts +++ b/services/desktop_app/src/renderer/types/jobSection.ts @@ -1 +1 @@ -export type JobSection = "counting" | "extraction" | "results"; +export type JobSection = "counting" | "extraction" | "cleaning" | "results"; diff --git a/services/desktop_app/src/renderer/views/JobResultsView.tsx b/services/desktop_app/src/renderer/views/JobResultsView.tsx index e4e3eed..7a2dd16 100644 --- a/services/desktop_app/src/renderer/views/JobResultsView.tsx +++ b/services/desktop_app/src/renderer/views/JobResultsView.tsx @@ -8,14 +8,17 @@ import { Loader2, Plus, RefreshCw, + Sparkles, } from "lucide-react"; import { useEffect, useState } from "react"; -import type { BackgroundProgress, CountJobFile, Job, Task } from "../../shared/types"; +import type { BackgroundProgress, CleaningJob, CountJobFile, Job, Task } from "../../shared/types"; import { CountResultsTable } from "../components/CountResultsTable"; +import { ExtractionResultsTable } from "../components/ExtractionResultsTable"; import { BackgroundProgressBanner } from "../components/BackgroundProgressBanner"; import type { ApiClient } from "../lib/apiClient"; +import { downloadCsv, toCsv, buildExportCsvFilename } from "../lib/csv"; -type ResultTab = "counting" | "extraction"; +type ResultTab = "counting" | "extraction" | "cleaning"; interface Props { client: ApiClient; @@ -23,9 +26,11 @@ interface Props { tasks: Task[]; countingProgress?: BackgroundProgress | null; extractionProgress?: BackgroundProgress | null; + cleaningProgress?: BackgroundProgress | null; onRefresh: () => Promise; onStartCounting: () => void; onStartExtraction: (ctx?: Record) => void; + onStartCleaning: (ctx?: Record) => void; } export function JobResultsView({ @@ -34,9 +39,11 @@ export function JobResultsView({ tasks, countingProgress, extractionProgress, + cleaningProgress, onRefresh, onStartCounting, onStartExtraction, + onStartCleaning, }: Props) { const [activeTab, setActiveTab] = useState("counting"); const [refreshing, setRefreshing] = useState(false); @@ -45,6 +52,8 @@ export function JobResultsView({ .sort((a, b) => new Date(b.created_at).getTime() - new Date(a.created_at).getTime()); const extractionTasks = tasks.filter((t) => t.type === "extraction") .sort((a, b) => new Date(b.created_at).getTime() - new Date(a.created_at).getTime()); + const cleaningTasks = tasks.filter((t) => t.type === "cleaning") + .sort((a, b) => new Date(b.created_at).getTime() - new Date(a.created_at).getTime()); async function refresh() { setRefreshing(true); @@ -54,7 +63,7 @@ export function JobResultsView({ return (
- {/* ── Sub-tabs: Conteo | Extracción ── */} + {/* ── Sub-tabs: Conteo | Extracción | Limpieza ── */}
+
@@ -129,12 +165,14 @@ export function JobResultsView({ ══════════════════════════════════════════ */ function CountingResultsSection({ client, + job, tasks, liveProgress, onStartCounting, onExtractFromCount, }: { client: ApiClient; + job: Job; tasks: Task[]; liveProgress?: BackgroundProgress | null; onStartCounting: () => void; @@ -175,17 +213,15 @@ function CountingResultsSection({ function exportCsv(subset: "valid" | "invalid") { const subset_files = subset === "valid" ? files.filter(isFileValid) : files.filter((f) => !isFileValid(f)); const headers = ["filename", "radicado", "service_count", "status", "is_valid", "error"]; - const rows = subset_files.map((f) => - [`"${f.filename}"`, `"${f.radicado ?? ""}"`, f.service_count ?? "", f.status, f.is_valid === null ? "" : String(f.is_valid), `"${(f.error ?? "").replace(/"/g, '""')}"`].join(","), - ); - const csv = [headers.join(","), ...rows].join("\n"); - const blob = new Blob([csv], { type: "text/csv" }); - const url = URL.createObjectURL(blob); - const a = document.createElement("a"); - a.href = url; - a.download = `conteo-${subset}-${new Date().toISOString().slice(0, 10)}.csv`; - a.click(); - URL.revokeObjectURL(url); + const rows = subset_files.map((f) => [ + f.filename, + f.radicado ?? "", + f.service_count ?? "", + f.status, + f.is_valid === null ? "" : String(f.is_valid), + f.error ?? "", + ]); + downloadCsv(buildExportCsvFilename("conteo", job.title, subset), toCsv(headers, rows)); } if (tasks.length === 0 && !liveProgress?.running) { @@ -291,14 +327,18 @@ type Row = Record; function ExtractionResultsSection({ client, + job, tasks, liveProgress, onStartExtraction, + onStartCleaning, }: { client: ApiClient; + job: Job; tasks: Task[]; liveProgress?: BackgroundProgress | null; onStartExtraction: () => void; + onStartCleaning: (ctx?: Record) => void; }) { const [selectedTask, setSelectedTask] = useState(tasks[0] ?? null); const [rows, setRows] = useState([]); @@ -344,20 +384,11 @@ function ExtractionResultsSection({ }, [selectedTask?.id]); // eslint-disable-line react-hooks/exhaustive-deps function exportCsv() { - const csv = [columns.join(","), ...rows.map((r) => - columns.map((c) => { - const v = r[c]; - if (v === null || v === undefined) return ""; - return `"${String(v).replace(/"/g, '""')}"`; - }).join(","), - )].join("\n"); - const blob = new Blob([csv], { type: "text/csv" }); - const url = URL.createObjectURL(blob); - const a = document.createElement("a"); - a.href = url; - a.download = `extraccion-${new Date().toISOString().slice(0, 10)}.csv`; - a.click(); - URL.revokeObjectURL(url); + const csv = toCsv( + columns, + rows.map((r) => columns.map((c) => r[c])), + ); + downloadCsv(buildExportCsvFilename("extraccion", job.title), csv); } if (tasks.length === 0 && !liveProgress?.running) { @@ -422,6 +453,24 @@ function ExtractionResultsSection({ CSV ({rows.length} filas) )} + {rows.length > 0 && selectedTask?.status === "completed" && ( + + )} @@ -468,26 +517,162 @@ function ExtractionResultsSection({

No hay filas extraídas.

) : ( -
- - - {columns.map((c) => )} - - - {rows.map((row, i) => ( - - {columns.map((c) => ( - - ))} - + + )} + + ); +} + +/* ══════════════════════════════════════════ + Sección de resultados de limpieza +══════════════════════════════════════════ */ +function CleaningResultsSection({ + client, + job, + tasks, + liveProgress, + onStartCleaning, +}: { + client: ApiClient; + job: Job; + tasks: Task[]; + liveProgress?: BackgroundProgress | null; + onStartCleaning: () => void; +}) { + const [selectedTask, setSelectedTask] = useState(tasks[0] ?? null); + const [cleaningJob, setCleaningJob] = useState(null); + const [rows, setRows] = useState([]); + const [columns, setColumns] = useState([]); + const [loading, setLoading] = useState(false); + const [error, setError] = useState(null); + + useEffect(() => { + setSelectedTask(tasks[0] ?? null); + }, [tasks.map((t) => t.id).join(",")]); // eslint-disable-line react-hooks/exhaustive-deps + + useEffect(() => { + if (!selectedTask) { + setRows([]); + setColumns([]); + setCleaningJob(null); + return; + } + let cancelled = false; + async function load() { + setLoading(true); + setError(null); + try { + const cleaningJobId = String( + (selectedTask!.result as Record)?.cleaning_job_id ?? "", + ); + if (!cleaningJobId) { + setError("La tarea no tiene cleaning_job_id"); + return; + } + const jobData = await client.cleaningJob(cleaningJobId); + if (cancelled) return; + setCleaningJob(jobData); + if (jobData.status === "completed") { + const resp = await client.cleaningJobRows(cleaningJobId, 500, 0); + if (cancelled) return; + const data = resp.rows ?? []; + setRows(data); + setColumns(Array.from(new Set(data.flatMap((r) => Object.keys(r))))); + } + } catch (e) { + if (!cancelled) setError(String(e instanceof Error ? e.message : e)); + } finally { + if (!cancelled) setLoading(false); + } + } + void load(); + return () => { cancelled = true; }; + }, [selectedTask?.id, client]); // eslint-disable-line react-hooks/exhaustive-deps + + function exportCsv() { + const csv = toCsv( + columns, + rows.map((r) => columns.map((h) => r[h])), + ); + downloadCsv(buildExportCsvFilename("limpieza", job.title), csv); + } + + if (tasks.length === 0 && !liveProgress?.running) { + return ( +
+ +

No hay resultados de limpieza todavía.

+ +
+ ); + } + + if (tasks.length === 0 && liveProgress?.running) { + return ( +
+ +
+ ); + } + + return ( +
+ {liveProgress?.running && ( +
+ +
+ )} +
+
+ {tasks.length > 1 && ( +
-
{c}
- {row[c] === null || row[c] === undefined - ? — - : String(row[c])} -
+ + )} + {selectedTask && ( + + {cleaningJob?.status ?? selectedTask.status} + + )} +
+ +
+ {rows.length > 0 && ( + + )} + +
+
+ + {error && ( +
+ {error} +
+ )} + + {loading && rows.length === 0 ? ( +
+ ) : rows.length === 0 ? ( +
+

No hay filas limpiadas.

+ ) : ( + )}
); diff --git a/services/desktop_app/src/renderer/views/JobView.tsx b/services/desktop_app/src/renderer/views/JobView.tsx index a1072fc..75b4f26 100644 --- a/services/desktop_app/src/renderer/views/JobView.tsx +++ b/services/desktop_app/src/renderer/views/JobView.tsx @@ -5,6 +5,7 @@ import type { BackgroundProgress, Job, Task } from "../../shared/types"; import type { JobSection } from "../types/jobSection"; import { BackgroundProgressBanner } from "../components/BackgroundProgressBanner"; import { CountingWizardModule } from "../modules/counting/CountingWizardModule"; +import { CleaningWizardModule } from "../modules/cleaning/CleaningWizardModule"; import { JobExtractionWizard } from "../modules/extraction/JobExtractionWizard"; import { JobResultsView } from "./JobResultsView"; @@ -24,10 +25,13 @@ export function JobView({ client, job, section, sectionContext, onActivateSectio const [savingTitle, setSavingTitle] = useState(false); const [countingProgress, setCountingProgress] = useState(null); const [extractionProgress, setExtractionProgress] = useState(null); + const [cleaningProgress, setCleaningProgress] = useState(null); const pollingRef = useRef | null>(null); const backgroundRunning = - countingProgress?.running === true || extractionProgress?.running === true; + countingProgress?.running === true || + extractionProgress?.running === true || + cleaningProgress?.running === true; const loadTasks = useCallback(async () => { try { @@ -74,6 +78,7 @@ export function JobView({ client, job, section, sectionContext, onActivateSectio const activeBackground = countingProgress?.running ? countingProgress : extractionProgress?.running ? extractionProgress + : cleaningProgress?.running ? cleaningProgress : null; return ( @@ -114,6 +119,7 @@ export function JobView({ client, job, section, sectionContext, onActivateSectio
{section === "counting" && "Conteo de servicios"} {section === "extraction" && "Extracción de datos"} + {section === "cleaning" && "Limpieza de datos"} {section === "results" && "Resultados del trabajo"}
@@ -161,6 +167,18 @@ export function JobView({ client, job, section, sectionContext, onActivateSectio />
+
+ setCleaningProgress(p.running ? p : null)} + onActivateModule={(type, ctx) => onActivateSection(type as JobSection, ctx)} + context={sectionContext} + /> +
+
onActivateSection("counting")} onStartExtraction={(ctx?: Record) => onActivateSection("extraction", ctx)} + onStartCleaning={(ctx?: Record) => onActivateSection("cleaning", ctx)} />
diff --git a/services/desktop_app/src/renderer/views/SettingsView.tsx b/services/desktop_app/src/renderer/views/SettingsView.tsx new file mode 100644 index 0000000..251c3c4 --- /dev/null +++ b/services/desktop_app/src/renderer/views/SettingsView.tsx @@ -0,0 +1,471 @@ +import { + CheckCircle2, + Database, + Loader2, + Plus, + RotateCcw, + Server, + Trash2, + XCircle, +} from "lucide-react"; +import { useEffect, useState } from "react"; +import { DEFAULT_API_BASE_URL } from "../lib/apiDefaults"; +import { + type CopModConfig, + defaultCopModConfig, + loadCopModConfig, + saveCopModConfig, +} from "../lib/cleaningSettings"; + +export type SettingsSection = "connection" | "cleaning" | "backend"; +type Status = "idle" | "ok" | "error" | "busy"; +type BackendTab = "config" | "models"; + +type SettingsViewProps = { + section: SettingsSection; + apiBaseUrl: string; + onApiBaseUrlChange: (url: string) => void; + connectionStatus: Status; + connectionMessage: string; + healthJson?: unknown; + verifyConnection: () => Promise; + busy: boolean; + configJson: unknown; + modelsJson: unknown; +}; + +const SERVICE_LABELS: Record = { + extraction: "Extracción", + cleaning: "Limpieza", + counting: "Conteo", +}; + +type ServiceRow = { id: string; label: string; ok: boolean; detail: string }; + +function parseHealthServices(health: unknown): ServiceRow[] { + if (!health || typeof health !== "object") return []; + const services = (health as { services?: unknown }).services; + if (!services || typeof services !== "object") return []; + return Object.entries(services as Record).map(([id, raw]) => { + const info = raw && typeof raw === "object" ? (raw as Record) : {}; + const ok = Boolean(info.ok); + const code = info.status_code != null ? String(info.status_code) : ""; + const err = typeof info.error === "string" ? info.error : ""; + return { + id, + label: SERVICE_LABELS[id] ?? id, + ok, + detail: ok ? (code ? `HTTP ${code}` : "OK") : err || (code ? `HTTP ${code}` : "Error"), + }; + }); +} + +export function SettingsView({ + section, + apiBaseUrl, + onApiBaseUrlChange, + connectionStatus, + connectionMessage, + healthJson, + verifyConnection, + busy, + configJson, + modelsJson, +}: SettingsViewProps) { + if (section === "connection") { + return ( + + ); + } + if (section === "cleaning") { + return ; + } + return ( + + ); +} + +function ConnectionSettings({ + apiBaseUrl, + onApiBaseUrlChange, + connectionStatus, + connectionMessage, + healthJson, + verifyConnection, + busy, +}: { + apiBaseUrl: string; + onApiBaseUrlChange: (url: string) => void; + connectionStatus: Status; + connectionMessage: string; + healthJson?: unknown; + verifyConnection: () => Promise; + busy: boolean; +}) { + const connectionLabel = + connectionStatus === "ok" + ? "Conectado" + : connectionStatus === "busy" + ? "Verificando…" + : connectionStatus === "error" + ? "Sin conexión" + : "Sin verificar"; + + const services = parseHealthServices(healthJson); + const allOk = services.length > 0 && services.every((s) => s.ok); + + return ( +
+
+

Conexión

+

URL del API Gateway y estado de los servicios del backend.

+
+ +
+
+
+ {connectionStatus === "ok" ? ( + + ) : connectionStatus === "error" ? ( + + ) : connectionStatus === "busy" ? ( + + ) : ( + + )} +
+
+ {connectionLabel} + {connectionMessage} + {connectionStatus === "ok" && services.length > 0 && ( + + {allOk + ? `${services.length} servicios operativos` + : `${services.filter((s) => s.ok).length} de ${services.length} servicios OK`} + + )} +
+
+ + {services.length > 0 && connectionStatus === "ok" && ( +
+ {services.map((svc) => ( +
+ +
+ {svc.label} + {svc.detail} +
+
+ ))} +
+ )} + +
+
+ +
+ onApiBaseUrlChange(e.target.value)} + placeholder={DEFAULT_API_BASE_URL} + spellCheck={false} + /> + +
+

Los cambios se guardan automáticamente en este equipo.

+
+
+
+
+ ); +} + +function parseAmountList(text: string): number[] { + return [ + ...new Set( + text + .split(/[,;\s]+/) + .map((s) => s.trim()) + .filter(Boolean) + .map((s) => Math.round(Number(s.replace(/\./g, "").replace(",", ".")))) + .filter((n) => Number.isFinite(n) && n >= 0), + ), + ].sort((a, b) => a - b); +} + +function CleaningSettings() { + const [config, setConfig] = useState(() => loadCopModConfig()); + const [drafts, setDrafts] = useState>(() => { + const initial = loadCopModConfig(); + return Object.fromEntries( + Object.entries(initial.moderadora_by_year).map(([y, vals]) => [y, vals.join(", ")]), + ); + }); + const [savedFlash, setSavedFlash] = useState(false); + const [newYear, setNewYear] = useState(""); + const [hydrated, setHydrated] = useState(false); + + useEffect(() => { + setHydrated(true); + }, []); + + useEffect(() => { + if (!hydrated) return; + saveCopModConfig(config); + setSavedFlash(true); + const t = window.setTimeout(() => setSavedFlash(false), 1200); + return () => window.clearTimeout(t); + }, [config, hydrated]); + + const years = Object.keys(config.moderadora_by_year).sort( + (a, b) => Number(a) - Number(b), + ); + + function commitYear(year: string, text: string) { + const nums = parseAmountList(text); + setDrafts((prev) => ({ ...prev, [year]: nums.join(", ") })); + setConfig((prev) => ({ + moderadora_by_year: { + ...prev.moderadora_by_year, + [year]: nums, + }, + })); + } + + function removeYear(year: string) { + setConfig((prev) => { + const next = { ...prev.moderadora_by_year }; + delete next[year]; + return { moderadora_by_year: next }; + }); + setDrafts((prev) => { + const next = { ...prev }; + delete next[year]; + return next; + }); + } + + function addYear() { + const y = String(Number.parseInt(newYear, 10)); + if (!Number.isFinite(Number(y)) || y === "NaN") return; + if (config.moderadora_by_year[y]) { + setNewYear(""); + return; + } + setConfig((prev) => ({ + moderadora_by_year: { ...prev.moderadora_by_year, [y]: [] }, + })); + setDrafts((prev) => ({ ...prev, [y]: "" })); + setNewYear(""); + } + + function resetDefaults() { + const defaults = defaultCopModConfig(); + setConfig(defaults); + setDrafts( + Object.fromEntries( + Object.entries(defaults.moderadora_by_year).map(([y, vals]) => [y, vals.join(", ")]), + ), + ); + } + + return ( +
+
+
+
+

Limpieza · COP / MOD

+

+ Cuotas moderadora por año. La regla «Separa COP/MOD» clasifica el importe + según el año de la fecha de servicio que elijas en el wizard. +

+
+
+ {savedFlash && Guardado} + +
+
+
+ +
+
+

Cuotas moderadora por año

+

+ Separa los valores con coma o espacio. Si el importe coincide con uno de + estos montos en el año de la fecha, se marca como moderadora; si no, como + copago. +

+ +
+ {years.length === 0 && ( +

No hay años configurados. Añade uno o restaura los defaults.

+ )} + {years.map((year) => ( +
+ + + setDrafts((prev) => ({ ...prev, [year]: e.target.value })) + } + onBlur={(e) => commitYear(year, e.target.value)} + placeholder="ej. 5000, 20100, 52800" + /> + +
+ ))} +
+ +
+ setNewYear(e.target.value)} + placeholder="Año" + inputMode="numeric" + aria-label="Nuevo año" + onKeyDown={(e) => { + if (e.key === "Enter") { + e.preventDefault(); + addYear(); + } + }} + /> + +
+
+
+
+ ); +} + +function BackendSettings({ + connectionStatus, + configJson, + modelsJson, + verifyConnection, + busy, +}: { + connectionStatus: Status; + configJson: unknown; + modelsJson: unknown; + verifyConnection: () => Promise; + busy: boolean; +}) { + const [backendTab, setBackendTab] = useState("config"); + const backendData = backendTab === "config" ? configJson : modelsJson; + + return ( +
+
+
+
+

Backend

+

Respuesta en vivo de /config y /models del gateway.

+
+ +
+
+ +
+
+
+ +

Datos del servidor

+
+
+ + +
+
+ +
+ {backendData != null ? ( + 
{JSON.stringify(backendData, null, 2)}
+ ) : ( +
+ +

Sin datos del backend

+ + {connectionStatus === "ok" + ? "Pulsa Actualizar para cargar los datos." + : "Verifica la conexión en la sección Conexión."} + +
+ )} +
+
+
+ ); +} diff --git a/services/desktop_app/src/shared/types.ts b/services/desktop_app/src/shared/types.ts index bbc3d67..5873b12 100644 --- a/services/desktop_app/src/shared/types.ts +++ b/services/desktop_app/src/shared/types.ts @@ -164,6 +164,83 @@ export interface CountSyncResponse { failed: number; } +export interface CleaningRuleInfo { + id: string; + name: string; + description: string; +} + +export interface CleaningColumnAnalysis { + name: string; + inferred_type: string; + example?: string | null; + samples?: string[]; + null_count?: number; + null_pct?: number; + unique_count?: number; + applicable_rules?: string[]; + [key: string]: unknown; +} + +export interface CleaningAnalysisResponse { + batch_id?: string; + filename?: string; + delimiter?: string; + row_count?: number; + columns: CleaningColumnAnalysis[]; + sample_rows?: Array>; +} + +export interface CleaningPreviewColumn { + name: string; + before: string[]; + after: string[]; + changed?: boolean; +} + +export interface CleaningPreviewResponse { + rows_before: Array>; + rows_after: Array>; + columns: CleaningPreviewColumn[]; +} + +export interface CleaningPreviewPayload { + rows?: Array>; + extraction_batch_id?: string; + rules_per_column: Record; + cop_mod_fecha_column?: Record; + cop_mod_config?: Record | null; + sample_size?: number; +} + +export interface CleaningJob { + id: string; + extraction_batch_id: string | null; + job_id: string | null; + source: "extraction" | "csv" | string; + status: "pending" | "running" | "completed" | "failed" | string; + request: Record; + log: string[]; + error: string | null; + created_at: string; + updated_at: string; +} + +export interface RunCleaningPayload { + extraction_batch_id: string; + job_id?: string; + rules_per_column: Record; + cop_mod_fecha_column?: Record; + cop_mod_config?: Record | null; +} + +export interface CleaningRowsResponse { + job_id: string; + rows: Array>; + limit: number; + offset: number; +} + export interface CountingFormState { localRoot: string; containerRoot: string; @@ -174,7 +251,7 @@ export interface CountingFormState { /** Estado de progreso de un módulo ejecutándose en segundo plano. */ export interface BackgroundProgress { - module: "counting" | "extraction"; + module: "counting" | "extraction" | "cleaning"; total: number; processed: number; failed: number; @@ -189,6 +266,31 @@ export interface RuntimeConfig { documentRootContainer: string; } +export interface UpdateManifest { + version: string; + releaseDate?: string; + /** Fecha de lanzamiento como en DATA.txt, ej. "06/07/2026" */ + releaseDateDisplay?: string; + fileName: string; + url: string; + sha256: string; + size?: number; +} + +export interface UpdateCheckResult { + updateAvailable: boolean; + currentVersion: string; + manifest?: UpdateManifest; + error?: string; +} + +export interface UpdateProgress { + phase: "downloading" | "verifying" | "installing" | string; + received?: number; + total?: number; + percent?: number; +} + export interface DesktopBridge { getRuntimeConfig(): Promise; selectDirectory(): Promise; @@ -202,4 +304,7 @@ export interface DesktopBridge { keywords: string[], ): Promise; readFile(filePath: string): Promise; + checkForUpdates(): Promise; + installUpdate(manifest: UpdateManifest): Promise<{ ok: boolean; destPath?: string }>; + onUpdateProgress(callback: (progress: UpdateProgress) => void): () => void; } diff --git a/services/extraction/api/main.py b/services/extraction/api/main.py index 7395b7f..5d3eb7c 100644 --- a/services/extraction/api/main.py +++ b/services/extraction/api/main.py @@ -1126,8 +1126,7 @@ def create_count_job(request: CreateCountJobRequest) -> dict[str, Any]: job = session.get(Job, request.job_id) if job is None: raise HTTPException(status_code=404, detail="Job no encontrado") - if request.title: - job.title = request.title + # No sobrescribir el nombre que el usuario dio al trabajo. job.status = "running" job.updated_at = utcnow() else: diff --git a/services/extraction/infrastructure/database.py b/services/extraction/infrastructure/database.py index f521832..dfbc08a 100644 --- a/services/extraction/infrastructure/database.py +++ b/services/extraction/infrastructure/database.py @@ -136,7 +136,13 @@ class CleaningJob(Base): __tablename__ = "cleaning_jobs" id: Mapped[str] = mapped_column(String(36), primary_key=True, default=new_id) - extraction_batch_id: Mapped[str] = mapped_column(ForeignKey("extraction_batches.id"), index=True) + extraction_batch_id: Mapped[str | None] = mapped_column( + ForeignKey("extraction_batches.id"), nullable=True, index=True + ) + job_id: Mapped[str | None] = mapped_column( + String(36), ForeignKey("jobs.id"), nullable=True, index=True + ) + source: Mapped[str] = mapped_column(String(32), default="extraction", index=True) status: Mapped[str] = mapped_column(String(32), default="pending", index=True) request: Mapped[dict[str, Any]] = mapped_column(JSON, default=dict) log: Mapped[list[str]] = mapped_column(JSON, default=list) @@ -150,7 +156,9 @@ class CleanedRow(Base): id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True) cleaning_job_id: Mapped[str] = mapped_column(ForeignKey("cleaning_jobs.id"), index=True) - extraction_batch_id: Mapped[str] = mapped_column(ForeignKey("extraction_batches.id"), index=True) + extraction_batch_id: Mapped[str | None] = mapped_column( + ForeignKey("extraction_batches.id"), nullable=True, index=True + ) row_index: Mapped[int] = mapped_column(Integer, default=0) row_json: Mapped[dict[str, Any]] = mapped_column(JSON, default=dict) created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow)