app.py

import gradio as gr
from supabase import create_client, Client
import os
import requests
import json

# ======================================================================
# --- SETUP AND CONFIGURATION ---
# ======================================================================

# Retrieve Supabase credentials from environment variables (Hugging Face Space secrets)
# This is a secure way to store your API keys and other sensitive information.
try:
    supabase_url = os.environ.get("SUPABASE_URL")
    supabase_key = os.environ.get("SUPABASE_KEY")
    supabase: Client = create_client(supabase_url, supabase_key)
except Exception as e:
    print(f"Error initializing Supabase client: {e}")
    supabase = None

# Set the endpoint for your voice generation engine
# Make sure to replace this with the actual URL from your RunPod instance.
CHATTTERBOX_ENDPOINT = os.environ.get("CHATTERBOX_ENDPOINT")

# ======================================================================
# --- CORE LOGIC ---
# ======================================================================

def process_voice_command(audio):
    """
    This function handles the core logic of the voice assistant.
    It takes an audio file, transcribes it, gets a response from a language model,
    saves the interaction to Supabase, and generates an audio response.
    """
    
    # Placeholder for the actual transcription logic
    # You will replace this with a call to your speech-to-text model.
    transcribed_text = "Placeholder: Your transcribed text will appear here."

    # Placeholder for the response from the language model
    # You will replace this with a call to your Gemma model.
    model_response = "Placeholder: This is Gemma's generated response."
    
    # Save the chat to Supabase
    if supabase:
        try:
            # We are using a dummy table name 'chats', make sure this matches your Supabase table.
            supabase.table("chats").insert({
                "user_input": transcribed_text,
                "model_response": model_response
            }).execute()
            print("Chat successfully saved to Supabase! 💾")
        except Exception as e:
            print(f"Error saving chat to Supabase: {e}")
    else:
        print("Supabase client not initialized. Skipping database save.")

    # Placeholder for the voice generation logic using Chatterbox
    # You will replace this with an actual API call to your Chatterbox endpoint.
    if CHATTTERBOX_ENDPOINT:
        try:
            # Example API call structure
            payload = {"text": model_response}
            headers = {"Content-Type": "application/json"}
            requests.post(CHATTTERBOX_ENDPOINT, data=json.dumps(payload), headers=headers)
            print("Response sent to Chatterbox endpoint! 🎙️")
        except Exception as e:
            print(f"Error sending data to Chatterbox: {e}")
    else:
        print("Chatterbox endpoint not set. Skipping voice generation.")
    
    # Return the transcription and the response to the Gradio interface.
    return transcribed_text, model_response

# ======================================================================
# --- GRADIO INTERFACE ---
# ======================================================================

# Create the Gradio interface
iface = gr.Interface(
    fn=process_voice_command,
    inputs=gr.Audio(sources=["microphone"], label="Speak your command here..."),
    outputs=[
        gr.Textbox(label="Transcription"),
        gr.Textbox(label="AI's Response")
    ],
    title="My Personal Voice Assistant",
    description="Speak into the microphone and get a response from the AI, with chats saved to your Supabase database."
)

# Launch the Gradio app
if __name__ == "__main__":
    iface.launch()