app.py

import streamlit as st
from dotenv import load_dotenv
import pickle
from huggingface_hub import Repository
from PyPDF2 import PdfReader
from streamlit_extras.add_vertical_space import add_vertical_space
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import OpenAI
from langchain.chains.question_answering import load_qa_chain
from langchain.callbacks import get_openai_callback
import os

# Step 1: Clone the Dataset Repository
repo = Repository(
    local_dir="Private_Book",  # Local directory to clone the repository
    repo_type="dataset",  # Specify that this is a dataset repository
    
    clone_from="Anne31415/Private_Book",  # Replace with your repository URL
    
    token=os.environ["HUB_TOKEN"]  # Use the secret token to authenticate
)
repo.git_pull()  # Pull the latest changes (if any)

# Step 2: Load the PDF File
pdf_file_path = "Private_Book/KOMBI_all2.pdf"  # Replace with your PDF file path


def cloud_button(label, query, key=None, color=None, overlap=30):
    button_id = f"cloud-button-{key or label}".replace(" ", "-")
    color_class = f"color-{color}" if color else ""
    num_circles = max(3, min(35, len(label) // 4))
    circle_size = 60

    circles_html = ''.join([
        f'<div class="circle {color_class}" style="margin-right: -{overlap}px;"></div>'
        for _ in range(num_circles)
    ])
    circles_html += f'<div class="circle-text">{label}</div>'

    cloud_button_html = f"""
    <div class="cloud" id="{button_id}" style="margin-bottom: 20px; cursor: pointer;">
        <div class="wrapper {color_class}">
            {circles_html}
        </div>
    </div>
    <script>
        document.getElementById("{button_id}").onclick = function() {{
            const query = "{query}";
            const label = "{label}";
            const button_id = "{button_id}";
            window.parent.postMessage({{
                'isStreamlitMessage': true,
                'type': 'streamlit:setComponentValue',
                'value': {{'label': label, 'query': query, 'button_id': button_id}},
                'key': 'button_clicked'
            }}, '*');
        }};
    </script>
    """
    st.markdown(cloud_button_html, unsafe_allow_html=True)


def display_chat_history(chat_history):
    for sender, msg, _ in chat_history:
        background_color = "#FFA07A" if sender == "User" else "#caf"
        st.markdown(f"<div style='background-color: {background_color}; padding: 10px; border-radius: 10px; margin: 10px;'>{sender}: {msg}</div>", unsafe_allow_html=True)


def load_pdf(file_path):
    pdf_reader = PdfReader(file_path)
    text = ""
    for page in pdf_reader.pages:
        text += page.extract_text()

    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1000,
        chunk_overlap=200,
        length_function=len
    )
    chunks = text_splitter.split_text(text=text)

    store_name, _ = os.path.splitext(os.path.basename(file_path))

    if os.path.exists(f"{store_name}.pkl"):
        with open(f"{store_name}.pkl", "rb") as f:
            VectorStore = pickle.load(f)
    else:
        embeddings = OpenAIEmbeddings()
        VectorStore = FAISS.from_texts(chunks, embedding=embeddings)
        with open(f"{store_name}.pkl", "wb") as f:
            pickle.dump(VectorStore, f)

    return VectorStore



def load_chatbot():
    return load_qa_chain(llm=OpenAI(), chain_type="stuff")

    
def main():
    hide_streamlit_style = """
    <style>
    #MainMenu {visibility: hidden;}
    footer {visibility: hidden;}
    </style>
    """
    st.markdown(hide_streamlit_style, unsafe_allow_html=True)

    st.title("Welcome to BinDocs ChatBot! 🤖")

    pdf_path = pdf_file_path
    if not os.path.exists(pdf_path):
        st.error("File not found. Please check the file path.")
        return

    if "chat_history" not in st.session_state:
        st.session_state['chat_history'] = []

    display_chat_history(st.session_state['chat_history'])

    st.write("<!-- Start Spacer -->", unsafe_allow_html=True)
    st.write("<div style='flex: 1;'></div>", unsafe_allow_html=True)
    st.write("<!-- End Spacer -->", unsafe_allow_html=True)

    if pdf_path is not None:
        query = st.text_input("Ask questions about your PDF file (in any preferred language):", key="user_query")

        cloud_buttons = [
            ("Was genau ist ein Belegarzt?", "Was genau ist ein Belegarzt?", "1"),
            ("Wofür wird die Alpha-ID verwendet?", "Wofür wird die Alpha-ID verwendet?", "2"),
            # Add more buttons as needed
        ]

        for label, query, color in cloud_buttons:
            cloud_button(label, query, color=color)

        user_input = st.empty()

        if "button_clicked" in st.session_state:
            button_info = st.session_state["button_clicked"]
            if button_info:
                st.write(f"You clicked: {button_info['label']}")
                st.write(f"Query: {button_info['query']}")
                # Handle the button click as needed
                # For example, you can call a function to process the query
                # process_query(button_info['query'])
                st.session_state["button_clicked"] = None  # Reset after handling


        if st.button("Ask"):
            user_input = st.session_state.user_query
            handle_query(user_input, pdf_path)

def handle_query(query, pdf_path):
    if not query:
        st.warning("Please enter a query.")
        return

    st.session_state['chat_history'].append(("User", query, "new"))
    loading_message = st.empty()
    loading_message.text('Bot is thinking...')

    VectorStore = load_pdf(pdf_path)
    chain = load_chatbot()
    docs = VectorStore.similarity_search(query=query, k=3)
    with get_openai_callback() as cb:
        response = chain.run(input_documents=docs, question=query)

    st.session_state['chat_history'].append(("Bot", response, "new"))
    display_chat_history(st.session_state['chat_history'][-2:])
    loading_message.empty()
    st.session_state['chat_history'] = [(sender, msg, "old") for sender, msg, _ in st.session_state['chat_history']]

if __name__ == "__main__":
    main()