feat: Add .env.example configuration file and integrate Redis service into Docker Compose for improved task management. Update desktop application to support document indexing and extraction workflows, enhancing user experience with a new extraction wizard and stepper components. Revise README and documentation for clarity on setup and usage.

services/services-count-facturas-medicas-serving/internal/model/onnx.go

@@ -0,0 +1,110 @@
+package model
+
+import (
+	"encoding/json"
+	"fmt"
+	"math"
+	"os"
+	"sync"
+
+	ort "github.com/yalue/onnxruntime_go"
+)
+
+type InferenceMeta struct {
+	Threshold      float64  `json:"threshold"`
+	FeatureColumns []string `json:"feature_columns"`
+}
+
+type Predictor struct {
+	session   *ort.DynamicAdvancedSession
+	threshold float64
+	mu        sync.Mutex
+}
+
+func NewPredictor(modelPath, metaPath string) (*Predictor, error) {
+	if lib := os.Getenv("ONNXRUNTIME_SHARED_LIBRARY"); lib != "" {
+		ort.SetSharedLibraryPath(lib)
+	}
+	if err := ort.InitializeEnvironment(); err != nil {
+		return nil, fmt.Errorf("onnx init: %w", err)
+	}
+
+	metaBytes, err := os.ReadFile(metaPath)
+	if err != nil {
+		return nil, fmt.Errorf("read inference meta: %w", err)
+	}
+	var meta InferenceMeta
+	if err := json.Unmarshal(metaBytes, &meta); err != nil {
+		return nil, fmt.Errorf("parse inference meta: %w", err)
+	}
+	if meta.Threshold == 0 {
+		meta.Threshold = 0.5
+	}
+
+	session, err := ort.NewDynamicAdvancedSession(
+		modelPath,
+		[]string{"float_input"},
+		[]string{"probabilities"},
+		nil,
+	)
+	if err != nil {
+		return nil, fmt.Errorf("onnx session: %w", err)
+	}
+
+	return &Predictor{session: session, threshold: meta.Threshold}, nil
+}
+
+func (p *Predictor) Threshold() float64 {
+	return p.threshold
+}
+
+func (p *Predictor) Close() error {
+	if p.session != nil {
+		p.session.Destroy()
+	}
+	return ort.DestroyEnvironment()
+}
+
+func (p *Predictor) PredictProba(features [][8]float32) ([]float64, error) {
+	if len(features) == 0 {
+		return nil, nil
+	}
+
+	n := len(features)
+	flat := make([]float32, 0, n*8)
+	for _, row := range features {
+		flat = append(flat, row[:]...)
+	}
+
+	inputShape := ort.NewShape(int64(n), 8)
+	inputTensor, err := ort.NewTensor(inputShape, flat)
+	if err != nil {
+		return nil, err
+	}
+	defer inputTensor.Destroy()
+
+	outputShape := ort.NewShape(int64(n), 2)
+	outputTensor, err := ort.NewEmptyTensor[float32](outputShape)
+	if err != nil {
+		return nil, err
+	}
+	defer outputTensor.Destroy()
+
+	p.mu.Lock()
+	err = p.session.Run([]ort.Value{inputTensor}, []ort.Value{outputTensor})
+	p.mu.Unlock()
+	if err != nil {
+		return nil, err
+	}
+
+	data := outputTensor.GetData()
+	probs := make([]float64, n)
+	for i := 0; i < n; i++ {
+		probs[i] = float64(data[i*2+1])
+	}
+	return probs, nil
+}
+
+func RoundProb(p float64) float64 {
+	return math.Round(p*10000) / 10000
+}