app.py

import streamlit as st
import whisper  # This will now refer to the correct openai-whisper
import openai
import tempfile
import os
import requests
from gtts import gTTS
from pydub import AudioSegment
from pydub.playback import play

# Set your Groq-compatible OpenAI API key
openai.api_key = os.getenv("GROQ_API_KEY", "your-groq-api-key")

# Load Whisper model
model = whisper.load_model("base")

# Title
st.title("🎙️ Voice-to-Voice Conversational App")

# Upload or record voice
uploaded_file = st.file_uploader("Upload your voice message (MP3/WAV)", type=["mp3", "wav"])

if uploaded_file:
    # Save audio to a temp file
    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
        tmp.write(uploaded_file.read())
        tmp_path = tmp.name

    # Transcribe with Whisper
    st.info("Transcribing...")
    result = model.transcribe(tmp_path)
    user_text = result["text"]
    st.success(f"You said: {user_text}")

    # Ask Groq/OpenAI
    st.info("Thinking...")
    response = openai.ChatCompletion.create(
        model="mixtral-8x7b-32768",  # Groq supports this
        messages=[{"role": "user", "content": user_text}]
    )
    reply_text = response["choices"][0]["message"]["content"]
    st.success(f"AI says: {reply_text}")

    # Convert to voice (TTS)
    tts = gTTS(reply_text)
    tts_path = "response.mp3"
    tts.save(tts_path)

    # Play the voice
    audio = AudioSegment.from_file(tts_path)
    st.audio(tts_path, format="audio/mp3")

    # Clean up temp file
    os.remove(tmp_path)