app.py

import streamlit as st
from audio_recorder_streamlit import audio_recorder
import openai  # Corrected import

API_KEY = 'enter-openai-api-key-here'

# Set the API key
openai.api_key = API_KEY

def transcribe_text_to_voice(audio_location):
    # Transcribe audio to text using Whisper API
    with open(audio_location, "rb") as audio_file:
        transcript = openai.Audio.transcriptions.create(
            model="whisper-1", file=audio_file
        )
    return transcript['text']

def chat_completion_call(text):
    # Send the text to GPT-3.5 for chat completion
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo-1106", 
        messages=[{"role": "user", "content": text}]
    )
    return response['choices'][0]['message']['content']

def text_to_speech_ai(speech_file_path, api_response):
    # Convert the text to speech using OpenAI's TTS API
    response = openai.Audio.speech.create(
        model="text-to-speech-1",  # TTS model name may differ
        voice="nova",  # Specify a voice (choose one that is available)
        input=api_response
    )
    # Save the speech response to a file
    with open(speech_file_path, "wb") as f:
        f.write(response['audio'])

st.title("🧑‍💻 Skolo Online 💬 Talking Assistant")

"""
Hi🤖 just click on the voice recorder and let me know how I can help you today?
"""

audio_bytes = audio_recorder()
if audio_bytes:
    # Save the Recorded Audio File
    audio_location = "audio_file.wav"
    with open(audio_location, "wb") as f:
        f.write(audio_bytes)

    # Transcribe the saved file to text
    text = transcribe_text_to_voice(audio_location)
    st.write(text)

    # Get AI response from GPT-3.5
    api_response = chat_completion_call(text)
    st.write(api_response)

    # Convert the response to speech and save it as a file
    speech_file_path = 'audio_response.mp3'
    text_to_speech_ai(speech_file_path, api_response)

    # Play the audio response in the app
    st.audio(speech_file_path)