Update app.py

app.py

@@ -36,7 +36,6 @@ async def predict_emotion_api(request: Request):
 
         # Robustly strip the prefix to get the pure base64 data
         try:
-            # Find the comma that separates the prefix from the data
             header, encoded = base64_with_prefix.split(",", 1)
             audio_data = base64.b64decode(encoded)
         except (ValueError, TypeError):
@@ -50,9 +49,7 @@ async def predict_emotion_api(request: Request):
         results = classifier(temp_audio_path)
         os.unlink(temp_audio_path) # Clean up the temp file
 
-        # The transformers pipeline returns a list of dicts
-        # Example: [{'score': 0.99, 'label': 'happy'}, {'score': 0.01, 'label': 'sad'}]
-        # We will return this directly
+        # Return a successful response with the data
         return JSONResponse(content={"data": results})
 
     except Exception as e:
@@ -65,10 +62,8 @@ async def predict_emotion_api(request: Request):
 def gradio_predict_wrapper(audio_file_path):
     if classifier is None: return {"error": f"Model is not loaded: {model_load_error}"}
     if audio_file_path is None: return {"error": "Please provide an audio file."}
-    
     try:
         results = classifier(audio_file_path, top_k=5)
-        # Format for Gradio's Label component
         return {item['label']: item['score'] for item in results}
     except Exception as e:
         return {"error": str(e)}
@@ -86,6 +81,5 @@ gradio_interface = gr.Interface(
 app = gr.mount_gradio_app(app, gradio_interface, path="/gradio")
 
 # The Uvicorn server launch command (used by Hugging Face Spaces)
-# This is the ONLY launch command needed.
 if __name__ == "__main__":
     uvicorn.run(app, host="0.0.0.0", port=7860)