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| import streamlit as st | |
| import openai | |
| import os | |
| import json | |
| import requests | |
| from datetime import datetime | |
| from collections import deque | |
| from openai import ChatCompletion | |
| from audio_recorder_streamlit import audio_recorder | |
| # Initialize configurations | |
| configurations = {} | |
| config_file = "configurations.json" | |
| if os.path.exists(config_file): | |
| with open(config_file, "r") as file: | |
| configurations = json.load(file) | |
| openai.api_key = os.getenv('OPENAI_KEY') | |
| st.set_page_config(page_title="GPT Streamlit Document Reasoner", layout="wide") | |
| model_choice = st.sidebar.radio("Select Model:", ('gpt-3.5-turbo', 'gpt-3.5-turbo-0301')) | |
| user_prompt = st.text_area( | |
| "Enter prompts, instructions & questions:", | |
| configurations.get("user_prompt", ""), | |
| height=100 | |
| ) | |
| system_prompt = configurations.get("system_prompt", "You are a helpful assistant.") | |
| def generate_filename(prompt, file_type): | |
| safe_date_time = datetime.now().strftime("%m%d_%I%M") | |
| safe_prompt = "".join(x for x in prompt if x.isalnum())[:45] | |
| return f"{safe_date_time}_{safe_prompt}.{file_type}" | |
| def chat_with_model(prompt, document_section): | |
| conversation = [{'role': 'system', 'content': system_prompt}] | |
| conversation.append({'role': 'user', 'content': prompt}) | |
| if document_section: | |
| conversation.append({'role': 'assistant', 'content': document_section}) | |
| response = openai.ChatCompletion.create(model=model_choice, messages=conversation) | |
| return response | |
| def save_and_play_audio(): | |
| audio_bytes = audio_recorder() | |
| if audio_bytes: | |
| filename = generate_filename("Recording", "wav") | |
| with open(filename, 'wb') as f: | |
| f.write(audio_bytes) | |
| st.audio(audio_bytes, format="audio/wav") | |
| return filename | |
| return None | |
| def create_file(filename, prompt, response): | |
| with open(filename, 'w') as file: | |
| file.write(f"Prompt:\n{prompt}\nResponse:\n{response}") | |
| def divide_document(document, max_length): | |
| return [document[i:i+max_length] for i in range(0, len(document), max_length)] | |
| def handle_uploaded_file(uploaded_file, max_length): | |
| file_content = uploaded_file.read().decode() | |
| return divide_document(file_content, max_length) | |
| def main(): | |
| max_length = st.slider("File section length for large files", min_value=1000, max_value=128000, value=12000, step=1000) | |
| uploaded_file = st.file_uploader("Add a file for context:", type=["txt"]) | |
| document_sections = deque() | |
| if uploaded_file is not None: | |
| document_sections.extend(handle_uploaded_file(uploaded_file, max_length)) | |
| document_responses = {} | |
| for i, section in enumerate(document_sections): | |
| if st.button(f"Chat about Section {i+1}"): | |
| response = chat_with_model(user_prompt, section) | |
| document_responses[i] = response | |
| filename = generate_filename(f"{user_prompt}_section_{i+1}", "txt") | |
| create_file(filename, user_prompt, response) | |
| if st.button('Chat'): | |
| response = chat_with_model(user_prompt, ''.join(document_sections)) | |
| filename = generate_filename(user_prompt, "txt") | |
| create_file(filename, user_prompt, response) | |
| configurations["user_prompt"] = user_prompt | |
| configurations["system_prompt"] = system_prompt | |
| with open(config_file, "w") as file: | |
| json.dump(configurations, file) | |
| if __name__ == "__main__": | |
| main() | |