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