Update main.py

main.py

@@ -10,81 +10,81 @@ model_name = "ProsusAI/finbert"
 print(f"Loading model {model_name}...")
 
 try:
-    tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
-    model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=3)
-    cryptobert = TextClassificationPipeline(
-        model=model, 
-        tokenizer=tokenizer, 
-        max_length=64, 
-        truncation=True, 
-        padding='max_length'
-    )
-    print("Model loaded successfully!")
+    tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+    model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=3)
+    cryptobert = TextClassificationPipeline(
+        model=model, 
+        tokenizer=tokenizer, 
+        max_length=64, 
+        truncation=True, 
+        padding='max_length'
+    )
+    print("Model loaded successfully!")
 except Exception as e:
-    print(f"Error loading model: {e}")
-    cryptobert = None
+    print(f"Error loading model: {e}")
+    cryptobert = None
 
 # --------- Định nghĩa Schema ---------
 class AnalyzeRequest(BaseModel):
-    texts: list[str]
+    texts: list[str]
 
 class AnalyzeResult(BaseModel):
-    text: str
-    label: str
-    score: float
-    numeric_score: float
+    text: str
+    label: str
+    score: float
+    numeric_score: float
 
 class AnalyzeResponse(BaseModel):
-    results: list[AnalyzeResult]
-    avg_score: float
+    results: list[AnalyzeResult]
+    avg_score: float
 
 # --------- Helper Function ---------
 def calculate_numeric_score(label: str, score: float) -> float:
-    if label == 'positive':
-        return score
-    elif label == 'negative':
-        return -score
-    else: # Neutral
-        return 0.0
+    if label == 'positive':
+        return score
+    elif label == 'negative':
+        return -score
+    else: # Neutral
+        return 0.0
 
 # --------- API Endpoints ---------
 @app.get("/")
 def read_root():
-    return {"status": "ok", "message": "CryptoBERT API is running", "model_loaded": cryptobert is not None}
+    return {"status": "ok", "message": "FinBERT API is running", "model_loaded": cryptobert is not None}
 
 @app.post("/api/sentiment", response_model=AnalyzeResponse)
 def analyze_sentiment(req: AnalyzeRequest):
-    if not cryptobert:
-        raise HTTPException(status_code=500, detail="Model is not loaded properly.")
-    
-    if not req.texts:
-        return {"results": [], "avg_score": 0.0}
+    if not cryptobert:
+        raise HTTPException(status_code=500, detail="Model is not loaded properly.")
+    
+    if not req.texts:
+        return {"results": [], "avg_score": 0.0}
 
-    try:
-        # Run predictions in batch
-        preds = cryptobert(req.texts)
-        
-        results = []
-        total_numeric = 0.0
-        
-        for text, pred in zip(req.texts, preds):
-            label = pred['label']
-            score = float(pred['score'])
-            numeric_score = calculate_numeric_score(label, score)
-            
-            results.append({
-                "text": text,
-                "label": label,
-                "score": score,
-                "numeric_score": numeric_score
-            })
-            total_numeric += numeric_score
-            
-        avg_score = total_numeric / len(results) if len(results) > 0 else 0.0
-        
-        return {
-            "results": results,
-            "avg_score": avg_score
-        }
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+    try:
+        # Run predictions in batch
+        preds = cryptobert(req.texts)
+        
+        results = []
+        total_numeric = 0.0
+        
+        for text, pred in zip(req.texts, preds):
+            label = pred['label']
+            score = float(pred['score'])
+            numeric_score = calculate_numeric_score(label, score)
+            
+            results.append({
+                "text": text,
+                "label": label,
+                "score": score,
+                "numeric_score": numeric_score
+            })
+            total_numeric += numeric_score
+            
+        avg_score = total_numeric / len(results) if len(results) > 0 else 0.0
+        
+        return {
+            "results": results,
+            "avg_score": avg_score
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))