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# app.py
import os
import gradio as gr
from groq import Groq
from gtts import gTTS
import tempfile
import whisper
# Initialize Groq client
GROQ_API_KEY = "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"
client = Groq(api_key = GROQ_API_KEY)
# Load the Whisper model
whisper_model = whisper.load_model("base") # You can use "small", "medium", or "large" depending on your preference
# Function to convert audio to text using OpenAI Whisper
def audio_to_text(audio_file):
audio = whisper.load_audio(audio_file)
audio = whisper.pad_or_trim(audio)
mel = whisper.log_mel_spectrogram(audio).to(whisper_model.device)
options = whisper.DecodingOptions(fp16=False)
result = whisper.decode(whisper_model, mel, options)
return result.text
# Function to interact with Groq API and generate a response
def interact_with_groq(user_input):
try:
chat_completion = client.chat.completions.create(
messages=[{"role": "user", "content": user_input}],
model="llama3-8b-8192", # Use the appropriate model
stream=False,
)
return chat_completion.choices[0].message.content
except Exception as e:
return f"Error interacting with Groq API: {e}"
# Function to convert text to speech using gTTS
def text_to_audio(response_text):
tts = gTTS(response_text)
output_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3").name
tts.save(output_path)
return output_path
# Main function for the chatbot
def voice_to_voice(audio_file):
try:
# Step 1: Convert voice input to text
print("Transcribing audio...")
transcribed_text = audio_to_text(audio_file)
print(f"Transcribed Text: {transcribed_text}")
# Step 2: Interact with LLM via Groq API
print("Getting LLM response...")
response_text = interact_with_groq(transcribed_text)
print(f"LLM Response: {response_text}")
# Step 3: Convert LLM response to audio
print("Generating audio response...")
audio_response = text_to_audio(response_text)
return transcribed_text, audio_response
except Exception as e:
return f"Error processing request: {e}", None
# Gradio Interface
interface = gr.Interface(
fn=voice_to_voice,
inputs=gr.Audio(type="filepath"),
outputs=[gr.Textbox(label="Transcribed Text"), gr.Audio(label="Response Audio")],
title="Real-Time Voice-to-Voice Chatbot",
description="A real-time voice-to-voice chatbot using Whisper for transcription, Groq API for LLM, and gTTS for audio response.",
)
# Launch the interface
if __name__ == "__main__":
interface.launch()
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