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import os
import whisper
import gradio as gr
import requests
from gtts import gTTS
# Load Whisper model
model = whisper.load_model("base")
# Read Groq API Key from environment variable
GROQ_API_KEY = os.getenv("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")
client= GROQ(API_KEY=GROQ_API_KEY)
# Main function: audio → text → LLM → speech
def transcribe_and_respond(audio_file):
# 1. Transcribe audio
result = model.transcribe(audio_file)
user_text = result["text"]
# 2. Query Groq LLM
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {GROQ_API_KEY}"
}
data = {
"model": "llama-3.3-70b-versatile",
"messages": [{"role": "user", "content": user_text}]
}
response = requests.post("https://api.groq.com/openai/v1/chat/completions", headers=headers, json=data)
if response.status_code == 200:
output_text = response.json()['choices'][0]['message']['content']
else:
output_text = f"Error from Groq API: {response.status_code} - {response.text}"
# 3. Convert to speech
tts = gTTS(text=output_text, lang='en')
tts_path = "response.mp3"
tts.save(tts_path)
return output_text, tts_path
# Gradio UI
iface = gr.Interface(
fn=transcribe_and_respond,
inputs=gr.Audio(type="filepath", label="🎙️ Speak"),
outputs=[gr.Textbox(label="🧠 LLM Reply"), gr.Audio(label="🔊 Spoken Response")],
title="Voice Chatbot with Whisper + Groq + gTTS",
description="Click to record → Get LLM reply → Hear it spoken back"
)
if __name__ == "__main__":
iface.launch()
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