Delete scoring_specificity.py

scoring_specificity.py

@@ -1,118 +0,0 @@
-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)}")