backup.py

import streamlit as st
import openai
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  # New import for zipping files
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
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
import streamlit.components.v1 as components  # Import Streamlit Components for HTML5

# page config and sidebar declares up front allow all other functions to see global class variables
st.set_page_config(page_title="GPT Streamlit Document Reasoner", layout="wide")
should_save = st.sidebar.checkbox("💾 Save", value=True)

# Whisper Paper - how open STT suddenly got so good:
# st link button with emoji anyone?
url="https://arxiv.org/pdf/2212.04356.pdf"
import random
def link_button_with_emoji(url):
    emojis = ["💉", "🏥", "🌡️", "🩺", "🌡️", "🔬", "💊", "🧪", "👨‍⚕️", "👩‍⚕️"]
    random_emoji = random.choice(emojis)
    st.markdown(f"[{random_emoji} Whisper Paper - Robust Speech Recognition via Large-Scale Weak Supervision]({url})")
url = "https://arxiv.org/pdf/2212.04356.pdf"
link_button_with_emoji(url)



def generate_filename_old(prompt, file_type):
    central = pytz.timezone('US/Central')
    safe_date_time = datetime.now(central).strftime("%m%d_%H%M")  # Date and time DD-HHMM
    safe_prompt = "".join(x for x in prompt if x.isalnum())[:90]  # Limit file name size and trim whitespace
    return f"{safe_date_time}_{safe_prompt}.{file_type}"  # Return a safe file name

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}"

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

    # Step 2: Extract base filename without extension
    base_filename, ext = os.path.splitext(filename)

    # Step 3: Check if the response contains Python code
    has_python_code = bool(re.search(r"```python([\s\S]*?)```", response))

    # Step 4: 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 Python code or other resources and add them with markdown title and emoji
    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 word 'python' from the beginning of 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"

            # Capture standard output
            original_stdout = sys.stdout
            sys.stdout = io.StringIO()
            
            # Execute cleaned Python code and capture the output
            try:
                exec(cleaned_code)
                code_output = sys.stdout.getvalue()
                combined_content += f"```\n{code_output}\n```\n\n"
                realtimeEvalResponse = "# Code Results 🚀\n" + "```" + code_output + "```\n\n"
                st.write(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 Resource title with markdown and emoji for non-Python resources
            combined_content += "# Resource 🛠️\n" + "```" + resource + "```\n\n"

    # Write the combined content into one file
    with open(f"{base_filename}-Combined.md", 'w') as file:
        file.write(combined_content)        



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 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 chat_with_model(prompt, document_section, model_choice='gpt-3.5-turbo'):
    model = model_choice
    conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
    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')
    openai.api_key = key
    for chunk in openai.ChatCompletion.create(
        model='gpt-3.5-turbo',
        messages=conversation,
        temperature=0.5,
        stream=True  
    ):
        
        collected_chunks.append(chunk)  # save the event response
        chunk_message = chunk['choices'][0]['delta']  # extract the message
        collected_messages.append(chunk_message)  # save the message
        
        content=chunk["choices"][0].get("delta",{}).get("content")
        
        try:
            report.append(content)
            if len(content) > 0:
                result = "".join(report).strip()
                #result = result.replace("\n", "")        
                res_box.markdown(f'*{result}*') 
        except:
            st.write(' ')
        
    full_reply_content = ''.join([m.get('content', '') for m in collected_messages])
    st.write("Elapsed time:")
    st.write(time.time() - start_time)
    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})
    response = openai.ChatCompletion.create(model=model_choice, messages=conversation)
    return response['choices'][0]['message']['content']

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")

from io import BytesIO
import re

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 pdf2txt_old(pdf_docs):
    st.write(pdf_docs)
    for file in pdf_docs:
        mime_type = extract_mime_type(file)
        st.write(f"MIME type of file: {mime_type}")
    
    text = ""
    for pdf in pdf_docs:
        pdf_reader = PdfReader(pdf)
        for page in pdf_reader.pages:
            text += page.extract_text()
    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 process_user_input(user_question):
    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)
        response = message.content
        user_prompt = user_question
        create_file(filename, user_prompt, response, should_save)       
        #st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)

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():
    #openai.api_key = os.getenv('OPENAI_API_KEY')

    # 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'))

    # 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) # *************************************
                    st.write('Response:')
                    st.write(response)
                    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...')
        
        #response = chat_with_model(user_prompt, ''.join(list(document_sections,)), model_choice) # *************************************

        # 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
        
        #st.write('Response:')
        #st.write(full_response)

        response = full_response
        st.write('Response:')
        st.write(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()
            #st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
                
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)
            #create_file(filename, raw, '')