app.py

import os
import requests
import google.generativeai as genai
import gradio as gr
from tempfile import NamedTemporaryFile
from pydub import AudioSegment
from pydub.exceptions import CouldntDecodeError

# Configure your Google Generative AI API key
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

# Create the model
generation_config = {
    "temperature": 1,
    "top_p": 0.95,
    "top_k": 64,
    "max_output_tokens": 8192,
    "response_mime_type": "text/plain",
}

model = genai.GenerativeModel(
    model_name="gemini-1.5-pro",
    generation_config=generation_config,
    system_instruction="You are a human that lives on earth, and you also speak friendly and use informal language. Your name is Ath, and you speak like a 100% human.",
)

chat_session = model.start_chat(history=[])

# Eleven Labs API configuration
eleven_labs_api_key = os.getenv("ELEVEN_LABS_API_KEY")
eleven_labs_url = "https://api.elevenlabs.io/v1/text-to-speech/aEO01A4wXwd1O8GPgGlF"

def chat_and_tts_text(user_input, history):
    # Send the user's text input to the chat session
    response = chat_session.send_message(user_input)
    response_text = response.text
    
    # Update the chat history with text input and response
    history.append((user_input, response_text))
    
    return history, response_text

def convert_audio_to_text(audio_file):
    # Function to convert audio to text (you can replace this with your preferred method)
    try:
        sound = AudioSegment.from_file(audio_file)
        return sound.export(format="wav")  # Export as WAV for TTS API (adjust format as needed)
    except CouldntDecodeError:
        return None

def chat_and_tts_audio(audio_file):
    # Convert uploaded audio file to text
    converted_audio = convert_audio_to_text(audio_file)
    if not converted_audio:
        return "Error: Could not decode audio file.", None
    
    # Send the audio text to the chat session
    response = chat_session.send_message(converted_audio)
    response_text = response.text
    
    # Eleven Labs text-to-speech request payload
    payload = {
        "text": response_text,
        "voice_settings": {
            "stability": 0,
            "similarity_boost": 0
        }
    }
    headers = {
        "xi-api-key": eleven_labs_api_key,
        "Content-Type": "application/json"
    }
    
    # Make the request to Eleven Labs API
    tts_response = requests.post(eleven_labs_url, json=payload, headers=headers)
    
    # Check if the response is successful and save the audio content to a temporary file
    if tts_response.status_code == 200:
        with NamedTemporaryFile(delete=False, suffix=".mp3") as temp_audio:
            temp_audio.write(tts_response.content)
            audio_path = temp_audio.name
    else:
        audio_path = None
    
    return response_text, audio_path

# Create the Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("<h1 style='text-align: center;'>Chat with Ath</h1>")
    gr.Markdown("Ask any question by typing or upload an audio file to receive a response from Ath in text and audio format.")

    with gr.Row():
        with gr.Column(scale=2):
            chatbot = gr.Chatbot(label="Chat History")
            user_input_text = gr.Textbox(placeholder="Type your question...", label="Text Input")
            submit_btn_text = gr.Button("Send")

        with gr.Column(scale=2):
            user_input_audio = gr.File(label="Upload Audio", type="audio")
            submit_btn_audio = gr.Button("Send")

        with gr.Column(scale=1):
            audio_output = gr.Audio(label="Response Audio", type="filepath")

    state = gr.State([])

    submit_btn_text.click(chat_and_tts_text, inputs=[user_input_text, state], outputs=[chatbot, state])
    submit_btn_audio.click(chat_and_tts_audio, inputs=[user_input_audio], outputs=[chatbot, audio_output])

demo.launch()