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| import streamlit as st | |
| import pandas as pd | |
| import openai | |
| from text_speech_utils import * # Assuming this module exists for your audio functionality | |
| # Define filenames for audio and conversation output | |
| input_audio_filename = 'input.wav' | |
| output_audio_filename = 'chatgpt_response.wav' | |
| output_conversation_filename = 'ChatGPT_conversation.txt' | |
| # Initialize app | |
| if 'messages' not in st.session_state: | |
| st.session_state['messages'] = [{"role": "system", "content": "You are a helpful assistant."}] | |
| # Allow user to input OpenAI API Key via Streamlit text input | |
| openai.api_key = st.text_input("Enter your OpenAI API Key", type="password") | |
| # Display a warning if API key is not provided | |
| if not openai.api_key: | |
| st.warning("Please enter your OpenAI API key to proceed.") | |
| # UI components | |
| st.title("My awesome personal assistant") | |
| sec = st.slider("Select number of seconds of recording", min_value=2, max_value=8, value=5) | |
| # Record audio + transcribe with Whisper + get GPT-3 response | |
| if st.button('Record audio'): | |
| if openai.api_key: # Proceed only if API key is provided | |
| st.write("Recording...") | |
| record_audio(input_audio_filename, sec) | |
| transcription = transcribe_audio(input_audio_filename) | |
| st.write(f"Me: {transcription['text']}") | |
| st.session_state['messages'].append({"role": "user", "content": transcription['text']}) | |
| bot = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=st.session_state['messages']) | |
| response = bot.choices[0].message.content | |
| st.write(f"GPT: {response}") | |
| save_text_as_audio(response, output_audio_filename) | |
| play_audio(output_audio_filename) | |
| st.session_state['messages'].append({"role": "assistant", "content": response}) | |
| else: | |
| st.error("API key is required to interact with GPT.") | |
| # # Download conversation button | |
| # st.download_button(label="Download conversation", | |
| # data=pd.DataFrame(st.session_state['messages']).to_csv(index=False).encode('utf-8'), | |
| # file_name=output_conversation_filename) | |
| # Function to generate conversation as plain text | |
| def generate_conversation_text(messages): | |
| conversation_text = "" | |
| for message in messages: | |
| if message["role"] == "user": | |
| conversation_text += f"Me: {message['content']}\n" | |
| elif message["role"] == "assistant": | |
| conversation_text += f"GPT: {message['content']}\n" | |
| elif message["role"] == "system": | |
| conversation_text += f"System: {message['content']}\n" | |
| return conversation_text | |
| # Download conversation button | |
| st.download_button( | |
| label="Download conversation", | |
| data=generate_conversation_text(st.session_state['messages']).encode('utf-8'), | |
| file_name=output_conversation_filename, | |
| mime="text/plain" | |
| ) |