app.py

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()