Update main.py

main.py

@@ -286,21 +286,29 @@ labels = [
 @app.post("/fast_classify/", description="Quickly classify text into predefined categories.")
 async def fast_classify_text(statement: str = Form(...)):
     try:
-        # Use asyncio to set a timeout for the classification
-        result = await asyncio.wait_for(
-            nlp_sequence_classification(statement, labels, multi_label=False),
-            timeout=5  # timeout in seconds
-        )
+        # Create a thread pool executor for running the synchronous function asynchronously
+        loop = asyncio.get_running_loop()
+        with ThreadPoolExecutor() as executor:
+            # Run the classification pipeline in a separate thread
+            result = await loop.run_in_executor(
+                executor, 
+                lambda: nlp_sequence_classification(statement, labels, multi_label=False)
+            )
 
-        # Extract the best label and score
+        # Extract the best label and score from the result
         best_label = result["labels"][0]
         best_score = result["scores"][0]
 
         return {"classification": best_label, "confidence": best_score}
     except asyncio.TimeoutError:
+        logging.error("Classification request timed out.")
         return JSONResponse(content="Classification timed out. Try a shorter input or increase timeout.", status_code=504)
+    except HTTPException as http_exc:
+        logging.error(f"HTTP exception: {http_exc.detail}")
+        return JSONResponse(content=f"HTTP error: {http_exc.detail}", status_code=http_exc.status_code)
     except Exception as e:
-        return JSONResponse(content=f"Error in classification: {str(e)}", status_code=500)
+        logging.error(f"Error in classification pipeline: {str(e)}")
+        return JSONResponse(content=f"Error in classification pipeline: {str(e)}", status_code=500)
         
 # Set up CORS middleware
 origins = ["*"]  # or specify your list of allowed origins