app.py

import gradio as gr
import os

from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain.chains import ConversationalRetrievalChain
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.llms import HuggingFacePipeline
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain_community.llms import HuggingFaceEndpoint

from pathlib import Path
import chromadb
from unidecode import unidecode

from transformers import AutoTokenizer
import transformers
import torch
import tqdm
import accelerate
import re

LLM_MODEL = "mistralai/Mistral-7B-Instruct-v0.2"
LLM_MAX_TOKEN = 512
DB_CHUNK_SIZE = 512
CHUNK_OVERLAP = 24
TEMPERATURE = 0.1
MAX_TOKENS = 512
TOP_K = 20
pdf_url = "https://huggingface.co/spaces/CCCDev/PDFChat/resolve/main/Privacy-Policy%20(1).pdf"  # Replace with your static PDF URL or path


# Load PDF document and create doc splits
def load_doc(pdf_url, chunk_size, chunk_overlap):
    loader = PyPDFLoader(pdf_url)
    pages = loader.load()
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
    doc_splits = text_splitter.split_documents(pages)
    return doc_splits


# Create vector database
def create_db(splits, collection_name):
    embedding = HuggingFaceEmbeddings()
    new_client = chromadb.EphemeralClient()
    vectordb = Chroma.from_documents(
        documents=splits,
        embedding=embedding,
        client=new_client,
        collection_name=collection_name,
    )
    return vectordb


# Initialize langchain LLM chain
def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
    progress(0.5, desc="Initializing HF Hub...")
    llm = HuggingFaceEndpoint(
        repo_id=llm_model,
        temperature=temperature,
        max_new_tokens=max_tokens,
        top_k=top_k,
    )

    progress(0.75, desc="Defining buffer memory...")
    memory = ConversationBufferMemory(
        memory_key="chat_history",
        output_key='answer',
        return_messages=True
    )
    retriever = vector_db.as_retriever()
    progress(0.8, desc="Defining retrieval chain...")
    qa_chain = ConversationalRetrievalChain.from_llm(
        llm,
        retriever=retriever,
        chain_type="stuff",
        memory=memory,
        return_source_documents=True,
        verbose=False,
    )
    progress(0.9, desc="Done!")
    return qa_chain


# Generate collection name for vector database
def create_collection_name(filepath):
    collection_name = Path(filepath).stem
    collection_name = collection_name.replace(" ", "-")
    collection_name = unidecode(collection_name)
    collection_name = re.sub('[^A-Za-z0-9]+', '-', collection_name)
    collection_name = collection_name[:50]
    if len(collection_name) < 3:
        collection_name = collection_name + 'xyz'
    if not collection_name[0].isalnum():
        collection_name = 'A' + collection_name[1:]
    if not collection_name[-1].isalnum():
        collection_name = collection_name[:-1] + 'Z'
    return collection_name


# Initialize database
def initialize_database(pdf_url, chunk_size, chunk_overlap, progress=gr.Progress()):
    collection_name = create_collection_name(pdf_url)
    progress(0.25, desc="Loading document...")
    doc_splits = load_doc(pdf_url, chunk_size, chunk_overlap)
    progress(0.5, desc="Generating vector database...")
    vector_db = create_db(doc_splits, collection_name)
    progress(0.9, desc="Done!")
    return vector_db, collection_name, "Complete!"


def initialize_LLM(llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
    qa_chain = initialize_llmchain(LLM_MODEL, llm_temperature, max_tokens, top_k, vector_db, progress)
    return qa_chain, "Complete!"


def format_chat_history(message, chat_history):
    formatted_chat_history = []
    for user_message, bot_message in chat_history:
        formatted_chat_history.append(f"User: {user_message}")
        formatted_chat_history.append(f"Assistant: {bot_message}")
    return formatted_chat_history


def conversation(qa_chain, message, history):
    formatted_chat_history = format_chat_history(message, history)
    response = qa_chain({"question": message, "chat_history": formatted_chat_history})
    response_answer = response["answer"]
    if response_answer.find("Helpful Answer:") != -1:
        response_answer = response_answer.split("Helpful Answer:")[-1]
    response_sources = response["source_documents"]
    response_source1 = response_sources[0].page_content.strip()
    response_source2 = response_sources[1].page_content.strip()
    response_source3 = response_sources[2].page_content.strip()
    response_source1_page = response_sources[0].metadata["page"] + 1
    response_source2_page = response_sources[1].metadata["page"] + 1
    response_source3_page = response_sources[2].metadata["page"] + 1
    new_history = history + [(message, response_answer)]
    return qa_chain, gr.update(
        value=""), new_history, response_source1, response_source1_page, response_source2, response_source2_page, response_source3, response_source3_page


def demo():
    with gr.Blocks(theme="base") as demo:
        vector_db = gr.State()
        qa_chain = gr.State()
        collection_name = gr.State()

        gr.Markdown(
            """<center><h2>PDF-based chatbot</center></h2>
            <h3>Ask any questions about your PDF documents</h3>""")
        gr.Markdown(
            """<b>Note:</b> This AI assistant, using Langchain and open-source LLMs, performs retrieval-augmented generation (RAG) from your PDF documents. \
            The user interface explicitely shows multiple steps to help understand the RAG workflow. 
            This chatbot takes past questions into account when generating answers (via conversational memory), and includes document references for clarity purposes.<br>
            <br><b>Warning:</b> This space uses the free CPU Basic hardware from Hugging Face. Some steps and LLM models used below (free inference endpoints) can take some time to generate a reply.
            """)

        # with gr.Tab("Step 1 - Upload PDF"):
        #     gr.Markdown("Using static PDF link: path/to/your/static.pdf")

        # with gr.Tab("Step 2 - Process document"):
        #     gr.Markdown("Processing document automatically...")

        # with gr.Tab("Step 3 - Initialize QA chain"):
        #     gr.Markdown("Initializing QA chain automatically...")

        with gr.Tab("Step 4 - Chatbot"):
            chatbot = gr.Chatbot(height=300)
            with gr.Accordion("Advanced - Document references", open=False):
                with gr.Row():
                    doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20)
                    source1_page = gr.Number(label="Page", scale=1)
                with gr.Row():
                    doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20)
                    source2_page = gr.Number(label="Page", scale=1)
                with gr.Row():
                    doc_source3 = gr.Textbox(label="Reference 3", lines=2, container=True, scale=20)
                    source3_page = gr.Number(label="Page", scale=1)
            with gr.Row():
                msg = gr.Textbox(placeholder="Type message (e.g. 'What is this document about?')", container=True)
            with gr.Row():
                submit_btn = gr.Button("Submit message")
                clear_btn = gr.ClearButton([msg, chatbot], value="Clear conversation")

        # Automatic preprocessing
        db_progress = gr.Textbox(label="Vector database initialization", value="Initializing...")
        db_btn = gr.Button("Generate vector database", visible=False)
        qachain_btn = gr.Button("Initialize Question Answering chain", visible=False)
        llm_progress = gr.Textbox(value="None", label="QA chain initialization")

        def auto_initialize():
            vector_db, collection_name, db_status = initialize_database(pdf_url, DB_CHUNK_SIZE, CHUNK_OVERLAP)
            qa_chain, llm_status = initialize_LLM(TEMPERATURE, LLM_MAX_TOKEN, 20, vector_db)
            return vector_db, collection_name, db_status, qa_chain, llm_status, "Initialization complete."

        demo.load(auto_initialize, [], [vector_db, collection_name, db_progress, qa_chain, llm_progress])

        # Chatbot events
        msg.submit(conversation, \
                   inputs=[qa_chain, msg, chatbot], \
                   outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3,
                            source3_page], \
                   queue=False)
        submit_btn.click(conversation, \
                         inputs=[qa_chain, msg, chatbot], \
                         outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page,
                                  doc_source3, source3_page], \
                         queue=False)
    return demo.queue().launch(debug=True)


# demo().launch(server_name="0.0.0.0")
if __name__ == "__main__":
    demo()