Update app.py

app.py

@@ -17,72 +17,60 @@ except Exception as e:
 else:
     model_load_error = None
 
-# --- Gradio Prediction Function ---
-def predict_emotion(audio_file):
-    if classifier is None:
-        return {"error": f"Model load failed: {model_load_error}"}
-    if audio_file is None:
-        return {"error": "No audio input provided."}
-
-    try:
-        if isinstance(audio_file, str):
-            audio_path = audio_file
-        elif isinstance(audio_file, tuple):
-            sample_rate, audio_array = audio_file
-            temp_audio_path = "temp_audio.wav"
-            sf.write(temp_audio_path, audio_array, sample_rate)
-            audio_path = temp_audio_path
-        else:
-            return {"error": f"Unsupported input type: {type(audio_file)}"}
-
-        results = classifier(audio_path, top_k=5)
-        return {item['label']: round(item['score'], 3) for item in results}
-    except Exception as e:
-        return {"error": f"Prediction error: {str(e)}"}
-    finally:
-        if 'temp_audio_path' in locals() and os.path.exists(temp_audio_path):
-            os.remove(temp_audio_path)
-
-# --- FastAPI App for Base64 API ---
+# --- FastAPI App for a dedicated, robust API ---
 app = FastAPI()
 
 @app.post("/api/predict/")
 async def predict_emotion_api(request: Request):
     if classifier is None:
-        return JSONResponse(content={"error": f"Model load failed: {model_load_error}"}, status_code=500)
+        return JSONResponse(content={"error": f"Model is not loaded: {model_load_error}"}, status_code=503)
     
     try:
         body = await request.json()
-        base64_audio = body.get("data")
-        if not base64_audio:
-            return JSONResponse(content={"error": "Missing 'data' field with base64 audio."}, status_code=400)
+        # The JS FileReader sends a string like "data:audio/wav;base64,AABBCC..."
+        base64_with_prefix = body.get("data")
 
-        audio_data = base64.b64decode(base64_audio)
+        if not base64_with_prefix:
+            return JSONResponse(content={"error": "Missing 'data' field in request body."}, status_code=400)
+
+        # 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):
+             return JSONResponse(content={"error": "Invalid base64 data format."}, status_code=400)
+
+        # Write to a temporary file for the pipeline
         with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_file:
             temp_file.write(audio_data)
             temp_audio_path = temp_file.name
 
         results = classifier(temp_audio_path, top_k=5)
-        os.unlink(temp_audio_path)
+        os.unlink(temp_audio_path) # Clean up the temp file
+
+        # Return a successful response
+        return JSONResponse(content={"data": results})
 
-        return {item['label']: round(item['score'], 3) for item in results}
     except Exception as e:
-        return JSONResponse(content={"error": f"API prediction failed: {str(e)}"}, status_code=500)
+        return JSONResponse(content={"error": f"Internal server error during prediction: {str(e)}"}, status_code=500)
+
+# --- Gradio UI function (optional, for the direct Space page) ---
+def gradio_predict_wrapper(audio_file):
+    # This is just for the UI on the Hugging Face page itself
+    if audio_file is None: return {"error": "Please provide an audio file."}
+    results = classifier(audio_file, top_k=5)
+    return {item['label']: round(item['score'], 3) for item in results}
 
-# --- Gradio UI ---
 gradio_interface = gr.Interface(
-    fn=predict_emotion,
+    fn=gradio_predict_wrapper,
     inputs=gr.Audio(sources=["microphone", "upload"], type="filepath", label="Upload Audio or Record"),
     outputs=gr.Label(num_top_classes=5, label="Emotion Predictions"),
     title="Audio Emotion Detector",
-    description="Upload or record your voice to detect emotions.",
+    description="This UI is for direct demonstration. The primary API is at /api/predict/",
     allow_flagging="never"
 )
 
-# --- Mount Gradio inside FastAPI ---
-app = gr.mount_gradio_app(app, gradio_interface, path="/")
-
-# --- Launch for local/dev use only ---
-if __name__ == "__main__":
-    gradio_interface.queue()
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+# --- Mount the Gradio UI onto the FastAPI app ---
+# The API at /api/predict/ will work even if the UI is at a different path.
+app = gr.mount_gradio_app(app, gradio_interface, path="/ui")