app.py

import gradio as gr
import torch
from transformers import pipeline
from gtts import gTTS
import tempfile
import os
from groq import Groq

# Load the Whisper model from Hugging Face
device = "cuda" if torch.cuda.is_available() else "cpu"
whisper_model = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device)

# Initialize Groq client
client = Groq(api_key="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")

# Function to handle the voice-to-voice conversation
def voice_to_voice_conversation(audio):
    # Read and transcribe audio using Whisper
    transcription = whisper_model(audio)["text"]

    # Get response from Groq API using Llama 8b
    chat_completion = client.chat.completions.create(
        messages=[{"role": "user", "content": transcription}],
        model="llama3-8b-8192",
    )
    response_text = chat_completion.choices[0].message.content

    # Convert text to speech using GTTS and save to a temporary file
    tts = gTTS(response_text)
    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
        tts.save(tmp_file.name)
        tmp_file_path = tmp_file.name

    # Load the generated speech as an audio file for Gradio
    return transcription, tmp_file_path

# Gradio Interface
interface = gr.Interface(
    fn=voice_to_voice_conversation,
    inputs=gr.Audio(type="filepath"),
    outputs=[gr.Textbox(label="Transcription"), gr.Audio(label="Response Audio")],
    title="Voice-to-Voice Chatbot",
    description="Speak into the microphone, and the chatbot will respond with a generated voice message.",
    live=False
)

# Launch the interface
interface.launch()