Update app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+import librosa
+import numpy as np
+import soundfile as sf
+import os
+
+# --- Model Loading ---
+# We'll use the pipeline abstraction from transformers for simplicity.
+# This model is specifically designed for audio classification (emotion detection).
+# It will automatically handle the loading of the model and its preprocessor.
+classifier = pipeline("audio-classification", model="mrm8488/Emotion-detection-from-audio-files")
+
+# --- Emotion Labels Mapping (Optional, for clearer output) ---
+# The model outputs raw labels, we can define a more readable mapping if needed
+# For this specific model, the labels are already pretty clear.
+# Example labels from the model's page: 'anger', 'disgust', 'fear', 'happiness', 'neutral', 'sadness', 'surprise'
+
+
+# --- Prediction Function ---
+def predict_emotion(audio_file):
+    """
+    Predicts emotions from an audio file.
+
+    Args:
+        audio_file (str or np.ndarray): Path to the audio file or a numpy array
+                                        (if using microphone input directly).
+                                        Gradio's Audio component usually provides
+                                        a file path for file uploads or a tuple
+                                        (samplerate, audio_array) for microphone.
+    Returns:
+        dict: A dictionary of emotion labels and their probabilities.
+    """
+    if audio_file is None:
+        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
+        # Save the numpy array to a temporary WAV file as the pipeline expects a file path or direct bytes
+        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": "Invalid audio input format."}
+
+    try:
+        # Perform inference
+        results = classifier(audio_path)
+
+        # Process results into a dictionary for better display
+        emotion_scores = {item['label']: item['score'] 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)
+
+
+# --- Gradio Interface ---
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=predict_emotion,
+    inputs=gr.Audio(type="filepath", label="Upload Audio or Record with Microphone", sources=["microphone", "file"]),
+    outputs=gr.Label(num_top_classes=7, label="Emotion Probabilities"), # Adjust num_top_classes based on model's output labels
+    title="AI Audio Emotion Detector",
+    description="Upload an audio file or record your voice to detect emotions like anger, disgust, fear, happiness, neutral, sadness, and surprise."
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    iface.launch()