Upload scoring_specificity.py

scoring_specificity.py

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+import os
+import uvicorn
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import List, Dict, Union
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+
+# Definition of Pydantic data models
+class ProblematicItem(BaseModel):
+    text: str
+
+class ProblematicList(BaseModel):
+    problematics: List[str]
+
+class PredictionResponse(BaseModel):
+    predicted_class: str
+    score: float
+
+class PredictionsResponse(BaseModel):
+    results: List[Dict[str, Union[str, float]]]
+
+# Model environment variables
+MODEL_NAME = os.getenv("MODEL_NAME", "votre-compte/votre-modele")
+LABEL_0 = os.getenv("LABEL_0", "Classe A")
+LABEL_1 = os.getenv("LABEL_1", "Classe B")
+
+# Loading the model and tokenizer
+tokenizer = None
+model = None
+
+def load_model():
+    global tokenizer, model
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+        model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+        return True
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        return False
+
+
+def health_check():
+    global model, tokenizer
+    if model is None or tokenizer is None:
+        success = load_model()
+        if not success:
+            raise HTTPException(status_code=503, detail="Model not available")
+    return {"status": "ok", "model": MODEL_NAME}
+
+
+def predict_single(item: ProblematicItem):
+    global model, tokenizer
+    
+    if model is None or tokenizer is None:
+        success = load_model()
+        if not success:
+            print('Error loading the model.')
+    
+    try:
+        # Tokenization
+        inputs = tokenizer(item.text, padding=True, truncation=True, return_tensors="pt")
+        
+        # Prediction
+        with torch.no_grad():
+            outputs = model(**inputs)
+            probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+            predicted_class = torch.argmax(probabilities, dim=1).item()
+            confidence_score = probabilities[0][predicted_class].item()
+        
+        # Associate the correct label
+        predicted_label = LABEL_0 if predicted_class == 0 else LABEL_1
+        
+        return PredictionResponse(predicted_class=predicted_label, score=confidence_score)
+    
+    except Exception as e:
+        print(f"Error during prediction: {str(e)}")
+
+def predict_batch(items: ProblematicList):
+    global model, tokenizer
+    
+    if model is None or tokenizer is None:
+        success = load_model()
+        if not success:
+            print("Model not available")
+    
+    try:
+        results = []
+        
+        # Batch processing
+        batch_size = 8
+        for i in range(0, len(items.problematics), batch_size):
+            batch_texts = items.problematics[i:i+batch_size]
+            
+            # Tokenization
+            inputs = tokenizer(batch_texts, padding=True, truncation=True, return_tensors="pt")
+            
+            # Prediction
+            with torch.no_grad():
+                outputs = model(**inputs)
+                probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+                predicted_classes = torch.argmax(probabilities, dim=1).tolist()
+                confidence_scores = [probabilities[j][predicted_classes[j]].item() for j in range(len(predicted_classes))]
+            
+            # Converting numerical predictions into labels
+            for j, (pred_class, score) in enumerate(zip(predicted_classes, confidence_scores)):
+                predicted_label = LABEL_0 if pred_class == 0 else LABEL_1
+                results.append({
+                    "text": batch_texts[j],
+                    "class": predicted_label,
+                    "score": score
+                })
+        
+        return PredictionsResponse(results=results)
+    
+    except Exception as e:
+        print(f"Error during prediction: {str(e)}")