app.py

import streamlit as st
import streamlit.components.v1 as components
import os
import base64
import glob
import io
import json
import mistune
import pytz
import math
import requests
import sys
import time
import re
import textract
import zipfile  
import random
import httpx # add 11/13/23
import asyncio
from openai import OpenAI
#from openai import AsyncOpenAI
from datetime import datetime
from xml.etree import ElementTree as ET
from bs4 import BeautifulSoup
from collections import deque
from audio_recorder_streamlit import audio_recorder
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from templates import css, bot_template, user_template
from io import BytesIO
from contextlib import redirect_stdout

# set page config once
st.set_page_config(page_title="Python AI Pair Programmer", layout="wide")

# UI for sidebar controls
should_save = st.sidebar.checkbox("💾 Save", value=True)
col1, col2, col3, col4 = st.columns(4)
with col1:
    with st.expander("Settings 🧠💾", expanded=True):
        # File type for output, model choice
        menu = ["txt", "htm", "xlsx", "csv", "md", "py"]
        choice = st.sidebar.selectbox("Output File Type:", menu)
        model_choice = st.sidebar.radio("Select Model:", ('gpt-3.5-turbo', 'gpt-3.5-turbo-0301'))

# Define a context dictionary to maintain the state between exec calls
context = {}

def create_file(filename, prompt, response, should_save=True):
    if not should_save:
        return

    # Extract base filename without extension
    base_filename, ext = os.path.splitext(filename)

    # Initialize the combined content
    combined_content = ""

    # Add Prompt with markdown title and emoji
    combined_content += "# Prompt 📝\n" + prompt + "\n\n"

    # Add Response with markdown title and emoji
    combined_content += "# Response 💬\n" + response + "\n\n"

    # Check for code blocks in the response
    resources = re.findall(r"```([\s\S]*?)```", response)
    for resource in resources:
        # Check if the resource contains Python code
        if "python" in resource.lower():
            # Remove the 'python' keyword from the code block
            cleaned_code = re.sub(r'^\s*python', '', resource, flags=re.IGNORECASE | re.MULTILINE)
            
            # Add Code Results title with markdown and emoji
            combined_content += "# Code Results 🚀\n"

            # Redirect standard output to capture it
            original_stdout = sys.stdout
            sys.stdout = io.StringIO()
            
            # Execute the cleaned Python code within the context
            try:
                exec(cleaned_code, context)
                code_output = sys.stdout.getvalue()
                combined_content += f"```\n{code_output}\n```\n\n"
                realtimeEvalResponse = "# Code Results 🚀\n" + "```" + code_output + "```\n\n"
                st.code(realtimeEvalResponse)
                
            except Exception as e:
                combined_content += f"```python\nError executing Python code: {e}\n```\n\n"
            
            # Restore the original standard output
            sys.stdout = original_stdout
        else:
            # Add non-Python resources with markdown and emoji
            combined_content += "# Resource 🛠️\n" + "```" + resource + "```\n\n"

    # Save the combined content to a Markdown file
    if should_save:
        with open(f"{base_filename}.md", 'w') as file:
            file.write(combined_content)
            st.code(combined_content)

    # Create a Base64 encoded link for the file
    with open(f"{base_filename}.md", 'rb') as file:
        encoded_file = base64.b64encode(file.read()).decode()
        href = f'<a href="data:file/markdown;base64,{encoded_file}" download="{filename}">Download File 📄</a>'
        st.markdown(href, unsafe_allow_html=True)


# Read it aloud        
def readitaloud(result):
    documentHTML5='''
    <!DOCTYPE html>
    <html>
    <head>
        <title>Read It Aloud</title>
        <script type="text/javascript">
            function readAloud() {
                const text = document.getElementById("textArea").value;
                const speech = new SpeechSynthesisUtterance(text);
                window.speechSynthesis.speak(speech);
            }
        </script>
    </head>
    <body>
        <h1>🔊 Read It Aloud</h1>
        <textarea id="textArea" rows="10" cols="80">
    '''
    documentHTML5 = documentHTML5 + result
    documentHTML5 = documentHTML5 + '''
        </textarea>
        <br>
        <button onclick="readAloud()">🔊 Read Aloud</button>
    </body>
    </html>
    '''

    components.html(documentHTML5, width=800, height=300)
    #return result

def generate_filename(prompt, file_type):
    central = pytz.timezone('US/Central')
    safe_date_time = datetime.now(central).strftime("%m%d_%H%M")
    replaced_prompt = prompt.replace(" ", "_").replace("\n", "_")
    safe_prompt = "".join(x for x in replaced_prompt if x.isalnum() or x == "_")[:90]
    return f"{safe_date_time}_{safe_prompt}.{file_type}"

# Chat and Chat with files
def chat_with_model(prompt, document_section, model_choice='gpt-3.5-turbo'):
    model = model_choice
    conversation = [{'role': 'system', 'content': 'You are a python script writer.'}]
    conversation.append({'role': 'user', 'content': prompt})
    if len(document_section)>0:
        conversation.append({'role': 'assistant', 'content': document_section})
    start_time = time.time()
    report = []
    res_box = st.empty()
    collected_chunks = []
    collected_messages = []
    key = os.getenv('OPENAI_API_KEY')

    client = OpenAI(
        api_key= os.getenv('OPENAI_API_KEY')
    )
    stream = client.chat.completions.create(
        model='gpt-3.5-turbo',
        messages=conversation,
        stream=True,
    )
    all_content = ""  # Initialize an empty string to hold all content
    for part in stream:
        chunk_message = (part.choices[0].delta.content or "")
        collected_messages.append(chunk_message)  # save the message
        content=part.choices[0].delta.content
        try:
            if len(content) > 0:
                report.append(content)
                all_content += content  
                result = "".join(report).strip()
                res_box.markdown(f'*{result}*') 
        except:
            st.write(' ')
    full_reply_content = all_content
    st.write("Elapsed time:")
    st.write(time.time() - start_time)
    filename = generate_filename(full_reply_content, choice)
    create_file(filename, prompt, full_reply_content, should_save)
    readitaloud(full_reply_content)
    return full_reply_content

def chat_with_file_contents(prompt, file_content, model_choice='gpt-3.5-turbo'):
    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})
        client = OpenAI(
            api_key= os.getenv('OPENAI_API_KEY')
        )
    response = client.chat.completions.create(model=model_choice, messages=conversation)
    return response['choices'][0]['message']['content']

def link_button_with_emoji(url, title, emoji_summary):
    emojis = ["💉", "🏥", "🌡️", "🩺", "🔬", "💊", "🧪", "👨‍⚕️", "👩‍⚕️"]
    random_emoji = random.choice(emojis)
    st.markdown(f"[{random_emoji} {emoji_summary} - {title}]({url})")

python_parts = {
    "Syntax": {"emoji": "✏️", "details": "Variables, Comments, Printing"},
    "Data Types": {"emoji": "📊", "details": "Numbers, Strings, Lists, Tuples, Sets, Dictionaries"},
    "Control Structures": {"emoji": "🔁", "details": "If, Elif, Else, Loops, Break, Continue"},
    "Functions": {"emoji": "🔧", "details": "Defining, Calling, Parameters, Return Values"},
    "Classes": {"emoji": "🏗️", "details": "Creating, Inheritance, Methods, Properties"},
    "API Interaction": {"emoji": "🌐", "details": "Requests, JSON Parsing, HTTP Methods"},
    "Data Visualization Libraries1": {"emoji": "📈", "details": "matplotlib"},
    "Data Visualization Libraries2": {"emoji": "📈", "details": "seaborn"},
    "Data Visualization Libraries3": {"emoji": "📈", "details": "plotly"},
    "Data Visualization Libraries4": {"emoji": "📈", "details": "altair"},
    "Data Visualization Libraries5": {"emoji": "📈", "details": "bokeh"},
    "Data Visualization Libraries6": {"emoji": "📈", "details": "pydeck"},
    "Data Visualization Libraries7": {"emoji": "📈", "details": "holoviews"},
    "Data Visualization Libraries8": {"emoji": "📈", "details": "plotnine"},
    "Data Visualization Libraries9": {"emoji": "📈", "details": "graphviz"},
    "Error Handling": {"emoji": "⚠️", "details": "Try, Except, Finally, Raising"},
    "Scientific & Data Analysis Libraries": {"emoji": "🧪", "details": "Numpy, Pandas, Scikit-Learn, TensorFlow, SciPy, Pillow"},
    "Advanced Concepts": {"emoji": "🧠", "details": "Decorators, Generators, Context Managers, Metaclasses, Asynchronous Programming"},
    "Web & Network Libraries": {"emoji": "🕸️", "details": "Flask, Django, Requests, BeautifulSoup, HTTPX, Asyncio"},
    "Streamlit & Extensions1": {"emoji": "💡", "details": "Streamlit"},
    "Streamlit & Extensions2": {"emoji": "💡", "details": "Streamlit-AgGrid"},
    "Streamlit & Extensions3": {"emoji": "💡", "details": "Streamlit-Folium"},
    "Streamlit & Extensions4": {"emoji": "💡", "details": "Streamlit-Pandas-Profiling"},
    "Streamlit & Extensions5": {"emoji": "💡", "details": "Streamlit-Vega-Lite, Gradio"},
    "Gradio": {"emoji": "💡", "details": "gradio"},
    "File Handling & Serialization": {"emoji": "📁", "details": "PyPDF2, Pytz, Json, Base64, Zipfile, Random, Glob, IO"},
    "Machine Learning & AI": {"emoji": "🤖", "details": "OpenAI, LangChain, HuggingFace"},
    "Text & Data Extraction": {"emoji": "🔍", "details": "TikToken, Textract, SQLAlchemy, Pillow"},
    "XML & Collections Libraries": {"emoji": "📚", "details": "XML, Collections"},
    "Top PyPI Libraries1": {"emoji": "🏆", "details": "Requests, Pillow, SQLAlchemy, Flask, Django, SciPy, Beautiful Soup, PyTest, PyGame, Twisted"},
    "Top PyPI Libraries2": {"emoji": "🏆", "details": "numpy, pandas, matplotlib, requests, beautifulsoup4"},
    "Top PyPI Libraries3": {"emoji": "🏆", "details": "langchain, openai, PyPDF2, pytz"},
    "Top PyPI Libraries4": {"emoji": "🏆", "details": "streamlit, audio_recorder_streamlit, gradio"},
    "Top PyPI Libraries5": {"emoji": "🏆", "details": "tiktoken, textract, glob, io"},
    "Top PyPI Libraries6": {"emoji": "🏆", "details": "matplotlib, seaborn, plotly, altair, bokeh, pydeck"},
    "Top PyPI Libraries7": {"emoji": "🏆", "details": "streamlit, streamlit-aggrid, streamlit-folium, streamlit-pandas-profiling, streamlit-vega-lite"},
    "Top PyPI Libraries8": {"emoji": "🏆", "details": "holoviews, plotnine, graphviz"},
    "Top PyPI Libraries9": {"emoji": "🏆", "details": "json, base64, zipfile, random"},
    "Top PyPI Libraries10": {"emoji": "🏆", "details": "httpx, asyncio, xml, collections, huggingface "}
}


response_placeholders = {}
example_placeholders = {}

def display_python_parts_old2():
    st.title("Python Interactive Learning Platform")
    
    for part, content in python_parts.items():
        with st.expander(f"{content['emoji']} {part} - {content['details']}", expanded=False):
            if st.button(f"Show Example for {part}", key=f"example_{part}"):
                example = "Write short python script examples with mock data in python list dictionary for inputs for " + part
                example_placeholders[part] = example
                st.code(example_placeholders[part], language="python")
                response = chat_with_model(f'Write python script with short code examples for: {content["details"]}', part)
                response_placeholders[part] = response
                st.write(f"#### {content['emoji']} {part} Example")
                st.code(response_placeholders[part], language="python")

            if st.button(f"Take Quiz on {part}", key=f"quiz_{part}"):
                quiz = "Write Python script quiz examples with mock static data inputs for " + part
                response = chat_with_model(f'Write python code blocks for quiz program: {quiz}', part)
                response_placeholders[part] = response
                st.write(f"#### {content['emoji']} {part} Quiz")
                st.code(response_placeholders[part], language="python")

            prompt = f"Write python script with a few advanced coding examples using mock data input for {content['details']}"
            if st.button(f"Explore {part}", key=part):
                response = chat_with_model(prompt, part)
                response_placeholders[part] = response
                st.write(f"#### {content['emoji']} {part} Details")
                st.code(response_placeholders[part], language="python")


def display_python_parts():
    st.title("Python Interactive Learning Platform")
    for part, content in python_parts.items():
        with st.expander(f"{content['emoji']} {part} - {content['details']}", expanded=False):
            if st.button(f"Show Example for {part}", key=f"example_{part}"):
                example = "Python script example with mock example inputs for " + part
                example_placeholders[part] = example
                st.code(example_placeholders[part], language="python")
                response = chat_with_model('Create detailed advanced python script code examples for:' + example_placeholders[part], part)
            if st.button(f"Take Quiz on {part}", key=f"quiz_{part}"):
                quiz = "Python script quiz example with mock example inputs for " + part
                response = chat_with_model(quiz, part)
            prompt = f"Learn about advanced coding examples using mock example inputs for {content['details']}"
            if st.button(f"Explore {part}", key=part):
                response = chat_with_model(prompt, part)
                response_placeholders[part] = response
            if part in response_placeholders:
                st.markdown(f"**Response:** {response_placeholders[part]}")

def add_paper_buttons_and_links():
    page = st.sidebar.radio("Choose a page:", ["Python Pair Programmer"])
    if page == "Python Pair Programmer":
        display_python_parts()

    col1, col2, col3, col4 = st.columns(4)

    with col1:
        with st.expander("MemGPT 🧠💾", expanded=False):
            link_button_with_emoji("https://arxiv.org/abs/2310.08560", "MemGPT", "🧠💾 Memory OS")
            outline_memgpt = "Memory Hierarchy, Context Paging, Self-directed Memory Updates, Memory Editing, Memory Retrieval, Preprompt Instructions, Semantic Memory, Episodic Memory, Emotional Contextual Understanding"
            if st.button("Discuss MemGPT Features"):
                chat_with_model("Discuss the key features of MemGPT: " + outline_memgpt, "MemGPT")

    with col2:
        with st.expander("AutoGen 🤖🔗", expanded=False):
            link_button_with_emoji("https://arxiv.org/abs/2308.08155", "AutoGen", "🤖🔗 Multi-Agent LLM")
            outline_autogen = "Cooperative Conversations, Combining Capabilities, Complex Task Solving, Divergent Thinking, Factuality, Highly Capable Agents, Generic Abstraction, Effective Implementation"
            if st.button("Explore AutoGen Multi-Agent LLM"):
                chat_with_model("Explore the key features of AutoGen: " + outline_autogen, "AutoGen")

    with col3:
        with st.expander("Whisper 🔊🧑‍🚀", expanded=False):
            link_button_with_emoji("https://arxiv.org/abs/2212.04356", "Whisper", "🔊🧑‍🚀 Robust STT")
            outline_whisper = "Scaling, Deep Learning Approaches, Weak Supervision, Zero-shot Transfer Learning, Accuracy & Robustness, Pre-training Techniques, Broad Range of Environments, Combining Multiple Datasets"
            if st.button("Learn About Whisper STT"):
                chat_with_model("Learn about the key features of Whisper: " + outline_whisper, "Whisper")

    with col4:
        with st.expander("ChatDev 💬💻", expanded=False):
            link_button_with_emoji("https://arxiv.org/pdf/2307.07924.pdf", "ChatDev", "💬💻 Comm. Agents")
            outline_chatdev = "Effective Communication, Comprehensive Software Solutions, Diverse Social Identities, Tailored Codes, Environment Dependencies, User Manuals"
            if st.button("Deep Dive into ChatDev"):
                chat_with_model("Deep dive into the features of ChatDev: " + outline_chatdev, "ChatDev")

add_paper_buttons_and_links()


# Process user input is a post processor algorithm which runs after document embedding vector DB play of GPT on context of documents..
def process_user_input(user_question):
    # Check and initialize 'conversation' in session state if not present
    if 'conversation' not in st.session_state:
        st.session_state.conversation = {}  # Initialize with an empty dictionary or an appropriate default value

    response = st.session_state.conversation({'question': user_question})
    st.session_state.chat_history = response['chat_history']

    for i, message in enumerate(st.session_state.chat_history):
        template = user_template if i % 2 == 0 else bot_template
        st.write(template.replace("{{MSG}}", message.content), unsafe_allow_html=True)

        # Save file output from PDF query results
        filename = generate_filename(user_question, 'txt')
        create_file(filename, user_question, message.content, should_save)

        # New functionality to create expanders and buttons
        create_expanders_and_buttons(message.content)

def create_expanders_and_buttons(content):
    # Split the content into paragraphs
    paragraphs = content.split("\n\n")
    for paragraph in paragraphs:
        # Identify the header and detail in the paragraph
        header, detail = extract_feature_and_detail(paragraph)
        if header and detail:
            with st.expander(header, expanded=False):
                if st.button(f"Explore {header}"):
                    expanded_outline = "Expand on the feature: " + detail
                    chat_with_model(expanded_outline, header)

def extract_feature_and_detail(paragraph):
    # Use regex to find the header and detail in the paragraph
    match = re.match(r"(.*?):(.*)", paragraph)
    if match:
        header = match.group(1).strip()
        detail = match.group(2).strip()
        return header, detail
    return None, None

def transcribe_audio(file_path, model):
    key = os.getenv('OPENAI_API_KEY')
    headers = {
        "Authorization": f"Bearer {key}",
    }
    with open(file_path, 'rb') as f:
        data = {'file': f}
        st.write("Read file {file_path}", file_path)
        OPENAI_API_URL = "https://api.openai.com/v1/audio/transcriptions"
        response = requests.post(OPENAI_API_URL, headers=headers, files=data, data={'model': model})
    if response.status_code == 200:
        st.write(response.json())
        chatResponse = chat_with_model(response.json().get('text'), '') # *************************************
        transcript = response.json().get('text')
        #st.write('Responses:')
        #st.write(chatResponse)
        filename = generate_filename(transcript, 'txt')
        #create_file(filename, transcript, chatResponse)
        response = chatResponse
        user_prompt = transcript
        create_file(filename, user_prompt, response, should_save)
        return transcript
    else:
        st.write(response.json())
        st.error("Error in API call.")
        return None

def save_and_play_audio(audio_recorder):
    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 truncate_document(document, length):
    return document[:length]

def divide_document(document, max_length):
    return [document[i:i+max_length] for i in range(0, len(document), max_length)]

def get_table_download_link(file_path):
    with open(file_path, 'r') as file:
        try:
            data = file.read()
        except:
            st.write('')
            return file_path    
    b64 = base64.b64encode(data.encode()).decode()  
    file_name = os.path.basename(file_path)
    ext = os.path.splitext(file_name)[1]  # get the file extension
    if ext == '.txt':
        mime_type = 'text/plain'
    elif ext == '.py':
        mime_type = 'text/plain'
    elif ext == '.xlsx':
        mime_type = 'text/plain'
    elif ext == '.csv':
        mime_type = 'text/plain'
    elif ext == '.htm':
        mime_type = 'text/html'
    elif ext == '.md':
        mime_type = 'text/markdown'
    else:
        mime_type = 'application/octet-stream'  # general binary data type
    href = f'<a href="data:{mime_type};base64,{b64}" target="_blank" download="{file_name}">{file_name}</a>'
    return href

def CompressXML(xml_text):
    root = ET.fromstring(xml_text)
    for elem in list(root.iter()):
        if isinstance(elem.tag, str) and 'Comment' in elem.tag:
            elem.parent.remove(elem)
    return ET.tostring(root, encoding='unicode', method="xml")
    
def read_file_content(file,max_length):
    if file.type == "application/json":
        content = json.load(file)
        return str(content)
    elif file.type == "text/html" or file.type == "text/htm":
        content = BeautifulSoup(file, "html.parser")
        return content.text
    elif file.type == "application/xml" or file.type == "text/xml":
        tree = ET.parse(file)
        root = tree.getroot()
        xml = CompressXML(ET.tostring(root, encoding='unicode'))
        return xml
    elif file.type == "text/markdown" or file.type == "text/md":
        md = mistune.create_markdown()
        content = md(file.read().decode())
        return content
    elif file.type == "text/plain":
        return file.getvalue().decode()
    else:
        return ""

def extract_mime_type(file):
    # Check if the input is a string
    if isinstance(file, str):
        pattern = r"type='(.*?)'"
        match = re.search(pattern, file)
        if match:
            return match.group(1)
        else:
            raise ValueError(f"Unable to extract MIME type from {file}")
    # If it's not a string, assume it's a streamlit.UploadedFile object
    elif isinstance(file, streamlit.UploadedFile):
        return file.type
    else:
        raise TypeError("Input should be a string or a streamlit.UploadedFile object")



def extract_file_extension(file):
    # get the file name directly from the UploadedFile object
    file_name = file.name
    pattern = r".*?\.(.*?)$"
    match = re.search(pattern, file_name)
    if match:
        return match.group(1)
    else:
        raise ValueError(f"Unable to extract file extension from {file_name}")

def pdf2txt(docs):
    text = ""
    for file in docs:
        file_extension = extract_file_extension(file)
        # print the file extension
        st.write(f"File type extension: {file_extension}")

        # read the file according to its extension
        try:
            if file_extension.lower() in ['py', 'txt', 'html', 'htm', 'xml', 'json']:
                text += file.getvalue().decode('utf-8')
            elif file_extension.lower() == 'pdf':
                from PyPDF2 import PdfReader
                pdf = PdfReader(BytesIO(file.getvalue()))
                for page in range(len(pdf.pages)):
                    text += pdf.pages[page].extract_text() # new PyPDF2 syntax
        except Exception as e:
            st.write(f"Error processing file {file.name}: {e}")
    return text

def txt2chunks(text):
    text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len)
    return text_splitter.split_text(text)

def vector_store(text_chunks):
    key = os.getenv('OPENAI_API_KEY')
    embeddings = OpenAIEmbeddings(openai_api_key=key)
    return FAISS.from_texts(texts=text_chunks, embedding=embeddings)

def get_chain(vectorstore):
    llm = ChatOpenAI()
    memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
    return ConversationalRetrievalChain.from_llm(llm=llm, retriever=vectorstore.as_retriever(), memory=memory)

def divide_prompt(prompt, max_length):
    words = prompt.split()
    chunks = []
    current_chunk = []
    current_length = 0
    for word in words:
        if len(word) + current_length <= max_length:
            current_length += len(word) + 1  # Adding 1 to account for spaces
            current_chunk.append(word)
        else:
            chunks.append(' '.join(current_chunk))
            current_chunk = [word]
            current_length = len(word)
    chunks.append(' '.join(current_chunk))  # Append the final chunk
    return chunks

def create_zip_of_files(files):
    """
    Create a zip file from a list of files.
    """
    zip_name = "all_files.zip"
    with zipfile.ZipFile(zip_name, 'w') as zipf:
        for file in files:
            zipf.write(file)
    return zip_name


def get_zip_download_link(zip_file):
    """
    Generate a link to download the zip file.
    """
    with open(zip_file, 'rb') as f:
        data = f.read()
    b64 = base64.b64encode(data).decode()
    href = f'<a href="data:application/zip;base64,{b64}" download="{zip_file}">Download All</a>'
    return href

    
def main():

    # Audio, transcribe, GPT:
    filename = save_and_play_audio(audio_recorder)

    if filename is not None:
        try:
            transcription = transcribe_audio(filename, "whisper-1")
        except:
            st.write(' ')
        st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
        filename = None

    # prompt interfaces
    user_prompt = st.text_area("Enter prompts, instructions & questions:", '', height=100)

    # file section interface for prompts against large documents as context
    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=["pdf", "xml", "json", "xlsx", "csv", "html", "htm", "md", "txt"])


    # Document section chat
        
    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_prompt, section, model_choice)
                    document_responses[i] = response
                    filename = generate_filename(f"{user_prompt}_section_{i+1}", choice)
                    create_file(filename, user_prompt, response, should_save)
                    st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)

    if st.button('💬 Chat'):
        st.write('Reasoning with your inputs...')
        
        # Divide the user_prompt into smaller sections
        user_prompt_sections = divide_prompt(user_prompt, max_length)
        full_response = ''
        for prompt_section in user_prompt_sections:
            # Process each section with the model
            response = chat_with_model(prompt_section, ''.join(list(document_sections)), model_choice)
            full_response += response + '\n'  # Combine the responses
        response = full_response
        filename = generate_filename(user_prompt, choice)
        create_file(filename, user_prompt, response, should_save)
        st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)

    all_files = glob.glob("*.*")
    all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 20]  # exclude files with short names
    all_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True)  # sort by file type and file name in descending order


    # Sidebar buttons Download All and Delete All
    colDownloadAll, colDeleteAll = st.sidebar.columns([3,3])
    with colDownloadAll:
        if st.button("⬇️ Download All"):
            zip_file = create_zip_of_files(all_files)
            st.markdown(get_zip_download_link(zip_file), unsafe_allow_html=True)
    with colDeleteAll:
        if st.button("🗑 Delete All"):
            for file in all_files:
                os.remove(file)
            st.experimental_rerun()
        
    # Sidebar of Files Saving History and surfacing files as context of prompts and responses
    file_contents=''
    next_action=''
    for file in all_files:
        col1, col2, col3, col4, col5 = st.sidebar.columns([1,6,1,1,1])  # adjust the ratio as needed
        with col1:
            if st.button("🌐", key="md_"+file):  # md emoji button
                with open(file, 'r') as f:
                    file_contents = f.read()
                    next_action='md'
        with col2:
            st.markdown(get_table_download_link(file), unsafe_allow_html=True)
        with col3:
            if st.button("📂", key="open_"+file):  # open emoji button
                with open(file, 'r') as f:
                    file_contents = f.read()
                    next_action='open'
        with col4:
            if st.button("🔍", key="read_"+file):  # search emoji button
                with open(file, 'r') as f:
                    file_contents = f.read()
                    next_action='search'
        with col5:
            if st.button("🗑", key="delete_"+file):
                os.remove(file)
                st.experimental_rerun()
                
    if len(file_contents) > 0:
        if next_action=='open':
            file_content_area = st.text_area("File Contents:", file_contents, height=500)
        if next_action=='md':
            st.markdown(file_contents)
        if next_action=='search':
            file_content_area = st.text_area("File Contents:", file_contents, height=500)
            st.write('Reasoning with your inputs...')
            response = chat_with_model(user_prompt, file_contents, model_choice)
            filename = generate_filename(file_contents, choice)
            create_file(filename, user_prompt, response, should_save)

            st.experimental_rerun()
                
if __name__ == "__main__":
    main()

load_dotenv()
st.write(css, unsafe_allow_html=True)

st.header("Chat with documents :books:")
user_question = st.text_input("Ask a question about your documents:")
if user_question:
    process_user_input(user_question)

with st.sidebar:
    st.subheader("Your documents")
    docs = st.file_uploader("import documents", accept_multiple_files=True)
    with st.spinner("Processing"):
        raw = pdf2txt(docs)
        if len(raw) > 0:
            length = str(len(raw))
            text_chunks = txt2chunks(raw)
            vectorstore = vector_store(text_chunks)
            st.session_state.conversation = get_chain(vectorstore)
            st.markdown('# AI Search Index of Length:' + length + ' Created.')  # add timing
            filename = generate_filename(raw, 'txt')
            create_file(filename, raw, '', should_save)