Update app.py

app.py

@@ -3,7 +3,6 @@ from transformers import pipeline
 import soundfile as sf
 import os
 
-# --- Model Loading ---
 try:
     classifier = pipeline("audio-classification", model="superb/wav2vec2-base-superb-er")
 except Exception as e:
@@ -11,36 +10,23 @@ except Exception as e:
         return {"error": f"Failed to load the model. Please check the logs. Error: {str(e)}"}
     classifier = None
 
-# --- Prediction Function ---
 def predict_emotion(audio_file):
-    if classifier is None:
-        return {"error": "The AI model could not be loaded."}
-    if audio_file is None:
-        return {"error": "No audio input provided."}
-
-    if isinstance(audio_file, str):
-        audio_path = audio_file
+    if classifier is None: return {"error": "The AI model could not be loaded."}
+    if audio_file is None: return {"error": "No audio input provided."}
+    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_from_mic.wav"
         sf.write(temp_audio_path, audio_array, sample_rate)
         audio_path = temp_audio_path
-    else:
-        return {"error": f"Invalid audio input format: {type(audio_file)}"}
-
+    else: return {"error": f"Invalid audio input format: {type(audio_file)}"}
     try:
         results = classifier(audio_path, top_k=5)
-        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)}"}
+        return {item['label']: round(item['score'], 3) for item in results}
+    except Exception as e: return {"error": f"An error occurred during prediction: {str(e)}"}
     finally:
-        if 'temp_audio_path' in locals() and os.path.exists(temp_audio_path):
-            os.remove(temp_audio_path)
-
+        if 'temp_audio_path' in locals() and os.path.exists(temp_audio_path): os.remove(temp_audio_path)
 
-# --- Gradio Interface ---
-# We have REMOVED the api_name parameter to revert to the default endpoint.
 iface = gr.Interface(
     fn=predict_emotion,
     inputs=gr.Audio(sources=["microphone", "upload"], type="filepath", label="Upload Audio or Record with Microphone"),
@@ -49,6 +35,5 @@ iface = gr.Interface(
     description="Upload an audio file or record your voice to detect emotions.",
 )
 
-# Launch the Gradio app with the API queue enabled
 if __name__ == "__main__":
     iface.queue().launch()