Updated try and catch code

app.py

@@ -31,8 +31,12 @@ def load_model():
 @asynccontextmanager
 async def lifespan(app: FastAPI):
     global model, tokenizer
-    model, tokenizer = load_model()
-    yield
+    try:
+        model, tokenizer = load_model()
+        yield
+    except Exception as e:
+        print(f"Startup error: {str(e)}")
+        raise RuntimeError(f"Failed to start application: {str(e)}")
 
 # Attach startup loader
 app = FastAPI(lifespan=lifespan)
@@ -43,23 +47,26 @@ class TextInput(BaseModel):
 
 # Sync text classification
 def classify_text(sentence: str):
-    inputs = tokenizer(sentence, return_tensors="pt", truncation=True, padding=True)
-    input_ids = inputs["input_ids"]
-    attention_mask = inputs["attention_mask"]
-
-    with torch.no_grad():
-        outputs = model(input_ids, attention_mask=attention_mask, labels=input_ids)
-        loss = outputs.loss
-        perplexity = torch.exp(loss).item()
-
-    if perplexity < 60:
-        result = "AI-generated"
-    elif perplexity < 80:
-        result = "Probably AI-generated"
-    else:
-        result = "Human-written"
-
-    return result, perplexity
+    try:
+        inputs = tokenizer(sentence, return_tensors="pt", truncation=True, padding=True)
+        input_ids = inputs["input_ids"]
+        attention_mask = inputs["attention_mask"]
+
+        with torch.no_grad():
+            outputs = model(input_ids, attention_mask=attention_mask, labels=input_ids)
+            loss = outputs.loss
+            perplexity = torch.exp(loss).item()
+
+        if perplexity < 60:
+            result = "AI-generated"
+        elif perplexity < 80:
+            result = "Probably AI-generated"
+        else:
+            result = "Human-written"
+
+        return result, perplexity
+    except Exception as e:
+        raise RuntimeError(f"Error during text classification: {str(e)}")
 
 # POST route to analyze text
 @app.post("/analyze")
@@ -68,13 +75,14 @@ async def analyze_text(data: TextInput):
     if not user_input:
         raise HTTPException(status_code=400, detail="Text cannot be empty")
 
-    # Run classification asynchronously to prevent blocking
-    result, perplexity = await asyncio.to_thread(classify_text, user_input)
-    
-    return {
-        "result": result,
-        "perplexity": round(perplexity, 2),
-    }
+    try:
+        result, perplexity = await asyncio.to_thread(classify_text, user_input)
+        return {
+            "result": result,
+            "perplexity": round(perplexity, 2),
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}")
 
 # Health check route
 @app.get("/health")