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awacke1 commited on Nov 12, 2023

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Create backupapp.py

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+import streamlit as st
+import streamlit.components.v1 as components
+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
+import random
+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
+from io import BytesIO
+# 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)
+# 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
+# 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 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 link_button_with_emoji(url, title, emoji_summary):
+    emojis = ["💉", "🏥", "🌡️", "🩺", "🔬", "💊", "🧪", "👨‍⚕️", "👩‍⚕️"]
+    random_emoji = random.choice(emojis)
+    st.markdown(f"[{random_emoji} {emoji_summary} - {title}]({url})")
+# Define function to add paper buttons and links
+def add_paper_buttons_and_links():
+    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 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
+# 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.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 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)
+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():
+    col1, col2, col3, col4 = st.columns(4)
+    with col1:
+        with st.expander("Settings 🧠💾", expanded=False):
+            # 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...')
+        # 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()
+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)