app.py

import gradio as gr
import asyncio
import edge_tts
import os
from huggingface_hub import InferenceClient
import requests
import tempfile
import logging

# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

# Get the Hugging Face token from environment variable
hf_token = os.getenv("HF_TOKEN")
if not hf_token:
    raise ValueError("HF_TOKEN environment variable is not set")

# Initialize the Hugging Face Inference Client for chat completion
chat_client = InferenceClient("mistralai/Mistral-Nemo-Instruct-2407", token=hf_token)

# Whisper API settings
WHISPER_API_URL = "https://api-inference.huggingface.co/models/openai/whisper-large-v3-turbo"
headers = {"Authorization": f"Bearer {hf_token}"}

# Initialize an empty chat history
chat_history = []

async def text_to_speech_stream(text):
    """Convert text to speech using edge_tts and return the audio file path."""
    communicate = edge_tts.Communicate(text, "en-US-AvaMultilingualNeural")
    audio_data = b""

    async for chunk in communicate.stream():
        if chunk["type"] == "audio":
            audio_data += chunk["data"]

    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_file:
        temp_file.write(audio_data)
        return temp_file.name

def whisper_speech_to_text(audio_path):
    """Convert speech to text using Hugging Face Whisper API."""
    if audio_path is None:
        logging.error("Error: No audio file provided")
        return ""
    
    if not os.path.exists(audio_path):
        logging.error(f"Error: Audio file not found at {audio_path}")
        return ""
    
    try:
        with open(audio_path, "rb") as audio_file:
            data = audio_file.read()
        response = requests.post(WHISPER_API_URL, headers=headers, data=data)
        response.raise_for_status()  # Raise an exception for bad status codes
        result = response.json()
        transcribed_text = result.get("text", "")
        logging.info(f"Transcribed text: {transcribed_text}")
        return transcribed_text
    except requests.exceptions.RequestException as e:
        logging.error(f"Error during API request: {e}")
        return ""
    except Exception as e:
        logging.error(f"Unexpected error in whisper_speech_to_text: {e}")
        return ""

async def chat_with_ai(message):
    global chat_history
    
    chat_history.append({"role": "user", "content": message})
    
    try:
        response = chat_client.chat_completion(
            messages=[{"role": "system", "content": "You are a helpful voice assistant. Provide concise and clear responses to user queries."}] + chat_history,
            max_tokens=800,
            temperature=0.7
        )
        
        response_text = response.choices[0].message['content']
        chat_history.append({"role": "assistant", "content": response_text})
        
        audio_path = await text_to_speech_stream(response_text)
        
        return response_text, audio_path
    except Exception as e:
        logging.error(f"Error in chat_with_ai: {e}")
        return str(e), None

def transcribe_and_chat(audio):
    if audio is None:
        return "Sorry, no audio was provided. Please try recording again.", None
    
    text = whisper_speech_to_text(audio)
    if not text:
        return "Sorry, I couldn't understand the audio or there was an error in transcription. Please try again.", None
    
    response, audio_path = asyncio.run(chat_with_ai(text))
    return response, audio_path

def create_demo():
    with gr.Blocks() as demo:
        gr.Markdown(
            """
            # 🗣️ AI Voice Assistant
            Welcome to your personal voice assistant! Simply record your voice, and I will respond with both text and speech. Powered by advanced AI models.
            """
        )

        with gr.Row():
            with gr.Column(scale=1):
                audio_input = gr.Audio(type="filepath", label="🎤 Record your voice", elem_id="audio-input")
                clear_button = gr.Button("Clear", variant="secondary", elem_id="clear-button")

            with gr.Column(scale=1):
                chat_output = gr.Textbox(label="💬 AI Response", elem_id="chat-output", lines=5, interactive=False)
                audio_output = gr.Audio(label="🔊 AI Voice Response", autoplay=True, elem_id="audio-output")

        # Add some spacing and a divider
        gr.Markdown("---")

        # Processing the audio input
        def process_audio(audio):
            logging.info(f"Received audio: {audio}")
            if audio is None:
                return "No audio detected. Please try recording again.", None, None
            response, audio_path = transcribe_and_chat(audio)
            logging.info(f"Response: {response}, Audio path: {audio_path}")
            return response, audio_path, None  # Return None to clear the audio input

        audio_input.change(process_audio, inputs=[audio_input], outputs=[chat_output, audio_output, audio_input])
        clear_button.click(lambda: (None, None, None), None, [chat_output, audio_output, audio_input])

        # JavaScript to handle autoplay and automatic submission
        demo.load(None, js="""
            function() {
                document.querySelector("audio").addEventListener("stop", function() {
                    setTimeout(function() {
                        document.querySelector('button[title="Submit"]').click();
                    }, 500);
                });
                
                function playAssistantAudio() {
                    var audioElements = document.querySelectorAll('audio');
                    if (audioElements.length > 1) {
                        var assistantAudio = audioElements[1];
                        if (assistantAudio) {
                            assistantAudio.play();
                        }
                    }
                }

                document.addEventListener('gradioAudioLoaded', function(event) {
                    playAssistantAudio();
                });

                document.addEventListener('gradioUpdated', function(event) {
                    setTimeout(playAssistantAudio, 100);
                });
            }
        """)

    return demo

# Launch the Gradio app
if __name__ == "__main__":
    demo = create_demo()
    demo.launch(server_name="0.0.0.0", server_port=7860)