app.py

import gradio as gr
import numpy as np
import tensorflow as tf
import librosa
import librosa.util
import pickle
from sklearn.preprocessing import LabelEncoder

# Feature extraction function
def extract_features(file_path):
    try:
        # Load the audio file
        y, sr = librosa.load(file_path, sr=8000)  # Resample to 8kHz
        mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
        
        # Pad or truncate to 100 frames along axis 1
        mfcc = librosa.util.fix_length(mfcc, size=100, axis=1)

        # Ensure the shape is (13, 100)
        if mfcc.shape[0] != 13:
            mfcc = librosa.util.fix_length(mfcc, size=13, axis=0)

        return {"mfcc": mfcc}
    except Exception as e:
        raise ValueError(f"Error in feature extraction: {str(e)}")

# Prediction function
def predict_class(file_path, model, label_encoder):
    try:
        features = extract_features(file_path)
        mfcc = features["mfcc"]

        # Add batch and channel dimensions for model compatibility
        mfcc = mfcc[np.newaxis, ..., np.newaxis]  # Shape: (1, 13, 100, 1)

        # Make prediction
        prediction = model.predict(mfcc)
        predicted_class = label_encoder.inverse_transform([np.argmax(prediction)])
        return f"Predicted Class: {predicted_class[0]}"
    except Exception as e:
        return f"Error in prediction: {str(e)}"

# Load the pre-trained model
model = tf.keras.models.load_model("voice_classification_modelm.h5")

# Load the label encoder
with open("label_encoder.pkl", "rb") as f:
    label_encoder = pickle.load(f)

# Define the Gradio function
def classify_audio(audio_file):
    return predict_class(audio_file, model, label_encoder)

# Create the Gradio interface
interface = gr.Interface(
    fn=classify_audio,
    inputs=gr.Audio(source="upload", type="filepath", label="Upload an Audio File"),
    outputs=gr.Textbox(label="Predicted Class"),
    title="Voice Disorder Classification",
    description="Upload an audio file to classify its voice type (e.g., healthy or various disorder types).",
    examples=["example_audio.wav"],  # Replace with paths to example audio files
)

# Launch the Gradio app
interface.launch()