app.py

import streamlit as st
import os
import requests

# Title for the Streamlit app
st.title("Voice Cloning Application")

# File uploader to upload a voice recording
uploaded_file = st.file_uploader("Upload a voice recording (First Time)", type=["wav", "mp3"])

if uploaded_file is not None:
    # Save the uploaded file locally
    with open("user_voice.wav", "wb") as f:
        f.write(uploaded_file.getbuffer())
    
    st.write("Your voice has been uploaded. Now we will clone your voice.")

    # Simulate storing the voice in a "database" (for simplicity, store it in the file system)
    user_voice_path = "user_voice.wav"

    # You could use Groq or other ML acceleration API here to fine-tune the model
    # Save user voice for future use (in real-life, this would be stored in a database)
    st.write(f"Voice stored at {user_voice_path}")

    # You can call your backend (Google Colab) to fine-tune the voice model
    response = requests.post("https://your-colab-backend-url", files={"file": open(user_voice_path, "rb")})
    
    if response.status_code == 200:
        st.write("Voice fine-tuning completed successfully!")
    else:
        st.write("Error in fine-tuning the voice.")

        # File uploader for subsequent requests
uploaded_file = st.file_uploader("Upload a text to clone your voice", type=["txt"])

if uploaded_file is not None:
    # Simulate user requesting a new cloned voice (no need to upload their voice)
    with open("user_text.txt", "w") as f:
        f.write(uploaded_file.getvalue().decode("utf-8"))
    
    # Call Google Colab model to generate cloned voice based on saved voice data
    cloned_voice = requests.post("https://your-colab-backend-url", files={"file": open("user_text.txt", "rb")}).content
    
    # Output the cloned voice
    st.audio(cloned_voice, format="audio/wav")
    st.write("Voice cloned successfully!")
    import requests

def groq_accelerate(audio_file_path):
    url = "https://groq.api/accelerate"
    headers = {"Authorization": "Bearer YOUR_GROQ_API_KEY"}
    
    # Send the audio file to Groq for processing (this is a placeholder for actual implementation)
    audio_file = open(audio_file_path, "rb")
    response = requests.post(url, headers=headers, files={"file": audio_file})
    
    # Return the accelerated result (cloned voice)
    return response.content