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import streamlit as st
from audio_recorder_streamlit import audio_recorder
import openai
# Prompt the user to input their OpenAI API key
API_KEY = st.text_input("Enter your OpenAI API Key", type="password")
# Check if the API key is provided
if API_KEY:
openai.api_key = API_KEY # Set the 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'])
# Streamlit interface for voice assistant
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("You said:", text)
# Get AI response from GPT-3.5
api_response = chat_completion_call(text)
st.write("AI says:", 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)
else:
st.warning("Please enter your OpenAI API key to continue.")
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