Update app.py

app.py

@@ -4,40 +4,23 @@ import soundfile as sf
 import os
 
 # --- Model Loading ---
-# We switched to 'superb/wav2vec2-base-superb-er' as it's a well-established and public model for emotion recognition.
 try:
     classifier = pipeline("audio-classification", model="superb/wav2vec2-base-superb-er")
 except Exception as e:
-    # If there's an error during model loading, we can display it in the Gradio interface
     def error_fn(audio_file):
         return {"error": f"Failed to load the model. Please check the logs. Error: {str(e)}"}
     classifier = None
 
 # --- Prediction Function ---
 def predict_emotion(audio_file):
-    """
-    Predicts emotions from an audio file.
-
-    Args:
-        audio_file (str or tuple): Path to the audio file (from upload) or a tuple
-                                   (samplerate, audio_array) from microphone input.
-    Returns:
-        dict: A dictionary of emotion labels and their probabilities.
-    """
-    # Handle case where the model failed to load
     if classifier is None:
-        return {"error": "The AI model could not be loaded. The application cannot start."}
-    
+        return {"error": "The AI model could not be loaded."}
     if audio_file is None:
-        return {"error": "No audio input provided. Please upload a file or record."}
+        return {"error": "No audio input provided."}
 
-    # Gradio's Audio component can return a path to a temp file for file uploads,
-    # or a tuple (samplerate, numpy_array) for microphone input.
     if isinstance(audio_file, str):
-        # Handle file path (e.g., from file upload)
         audio_path = audio_file
     elif isinstance(audio_file, tuple):
-        # Handle microphone input (samplerate, numpy_array)
         sample_rate, audio_array = audio_file
         temp_audio_path = "temp_audio_from_mic.wav"
         sf.write(temp_audio_path, audio_array, sample_rate)
@@ -46,17 +29,12 @@ def predict_emotion(audio_file):
         return {"error": f"Invalid audio input format: {type(audio_file)}"}
 
     try:
-        # Perform inference
         results = classifier(audio_path, top_k=5)
-
-        # Process results into a dictionary for better display
         emotion_scores = {item['label']: round(item['score'], 3) for item in results}
-
         return emotion_scores
     except Exception as e:
         return {"error": f"An error occurred during prediction: {str(e)}"}
     finally:
-        # Clean up temporary file if created
         if 'temp_audio_path' in locals() and os.path.exists(temp_audio_path):
             os.remove(temp_audio_path)
 
@@ -67,9 +45,11 @@ iface = gr.Interface(
     inputs=gr.Audio(sources=["microphone", "upload"], type="filepath", label="Upload Audio or Record with Microphone"),
     outputs=gr.Label(num_top_classes=5, label="Emotion Probabilities"),
     title="AI Audio Emotion Detector",
-    description="Upload an audio file or record your voice to detect emotions. This model is trained to recognize 'anger', 'happiness', 'neutral', 'sadness', and 'no-emotion'.",
+    description="Upload an audio file or record your voice to detect emotions.",
+    # THIS IS THE CRITICAL CHANGE TO CREATE A NAMED ENDPOINT
+    api_name="predict" 
 )
 
 # Launch the Gradio app with the API queue enabled
 if __name__ == "__main__":
-    iface.queue().launch() # <-- THE FIX IS HERE!
+    iface.queue().launch()