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| import streamlit as st | |
| import openai | |
| import os | |
| import base64 | |
| import glob | |
| import json | |
| import mistune | |
| import pytz | |
| import math | |
| import requests | |
| from datetime import datetime | |
| from openai import ChatCompletion | |
| from xml.etree import ElementTree as ET | |
| from bs4 import BeautifulSoup | |
| from collections import deque | |
| from audio_recorder_streamlit import audio_recorder | |
| # Function Definitions (kept unchanged) | |
| # ... | |
| def chat_with_file_contents(prompt, file_content): | |
| conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}] | |
| conversation.append({'role': 'user', 'content': prompt}) | |
| if len(file_content)>0: | |
| conversation.append({'role': 'assistant', 'content': file_content}) | |
| response = openai.ChatCompletion.create(model=model_choice, messages=conversation) | |
| return response['choices'][0]['message']['content'] | |
| # Sidebar and global | |
| openai.api_key = os.getenv('OPENAI_KEY') | |
| st.set_page_config(page_title="GPT Streamlit Document Reasoner",layout="wide") | |
| menu = ["htm", "txt", "xlsx", "csv", "md", "py"] #619 | |
| choice = st.sidebar.selectbox("Output File Type:", menu) | |
| model_choice = st.sidebar.radio("Select Model:", ('gpt-3.5-turbo', 'gpt-3.5-turbo-0301')) | |
| # Audio, transcribe, GPT: | |
| filename = save_and_play_audio(audio_recorder) | |
| if filename is not None: | |
| transcription = transcribe_audio(openai.api_key, filename, "whisper-1") | |
| st.write(transcription) | |
| gptOutput = chat_with_model(transcription, '') | |
| filename = generate_filename(transcription, choice) | |
| create_file(filename, transcription, gptOutput) | |
| st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True) | |
| def main(): | |
| user_prompt = st.text_area("Enter prompts, instructions & questions:", '', height=100) | |
| collength, colupload = st.columns([2,3]) # adjust the ratio as needed | |
| with collength: | |
| max_length = st.slider("File section length for large files", min_value=1000, max_value=128000, value=12000, step=1000) | |
| with colupload: | |
| uploaded_file = st.file_uploader("Add a file for context:", type=["xml", "json", "xlsx","csv","html", "htm", "md", "txt"]) | |
| document_sections = deque() | |
| document_responses = {} | |
| if uploaded_file is not None: | |
| file_content = read_file_content(uploaded_file, max_length) | |
| document_sections.extend(divide_document(file_content, max_length)) | |
| if len(document_sections) > 0: | |
| if st.button("👁️ View Upload"): | |
| st.markdown("**Sections of the uploaded file:**") | |
| for i, section in enumerate(list(document_sections)): | |
| st.markdown(f"**Section {i+1}**\n{section}") | |
| st.markdown("**Chat with the model:**") | |
| for i, section in enumerate(list(document_sections)): | |
| if i in document_responses: | |
| st.markdown(f"**Section {i+1}**\n{document_responses[i]}") | |
| else: | |
| if st.button(f"Chat about Section {i+1}"): | |
| st.write('Reasoning with your inputs...') | |
| response = chat_with_model(user | |