app.py

import numpy as np
import torch
import gradio as gr

# Placeholder for model loading and voice cloning logic
class VoiceCloner:
    def __init__(self):
        self.model = None
    
    def load_model(self, npz_file):
        data = np.load(npz_file)
        # Load your model parameters from the npz file
        # Initialize your model here with the loaded parameters
        self.model = data  # Example; replace with your actual model loading code

    def clone_voice(self, audio_file, text=None):
        # Implement the logic to clone voice from the uploaded audio file
        # and possibly from the text if provided
        return audio_file  # Placeholder; return processed audio

# Create the Gradio interface
def create_interface():
    cloner = VoiceCloner()
    
    with gr.Blocks() as demo:
        gr.Markdown("## Voice Cloning Application")

        # User uploads their .npz file
        npz_file = gr.File(label="Upload Your .npz Voice Model")
        audio_input = gr.Audio(source="upload", type="filepath", label="Upload Original Audio")
        text_input = gr.Textbox(label="Text Input for TTS (Optional)")
        output_audio = gr.Audio(label="Cloned Voice Output")

        upload_button = gr.Button("Load Model")
        
        # Button to clone voice
        clone_button = gr.Button("Clone Voice")

        # Load the model when the user uploads the .npz file
        def load_and_initialize(npz):
            cloner.load_model(npz.name)  # Use the file path to load the model
            return "Model Loaded!"

        upload_button.click(fn=load_and_initialize, inputs=npz_file, outputs="text")

        # Clone the voice when the button is pressed
        clone_button.click(fn=cloner.clone_voice, inputs=[audio_input, text_input], outputs=output_audio)

    return demo

if __name__ == "__main__":
    demo = create_interface()
    demo.launch()