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


list_llm = ["mistralai/Mistral-7B-Instruct-v0.2"]
list_llm_simple = [os.path.basename(llm) for llm in list_llm]

def load_doc(list_file_path, chunk_size=600, chunk_overlap=40):
    loaders = [PyPDFLoader(x) for x in list_file_path]
    pages = []
    for loader in loaders:
        pages.extend(loader.load())
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size = chunk_size, 
        chunk_overlap = chunk_overlap)
    doc_splits = text_splitter.split_documents(pages)
    return doc_splits

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

def initialize_llmchain(llm_model, vector_db, progress=gr.Progress()):
    progress(0.1, desc="Initializing HF Hub...")
    llm = HuggingFaceEndpoint(
        repo_id=llm_model, 
        temperature=0.7,
        max_new_tokens=1024,
        top_k=3,
    )
    
    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

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

def initialize_all(file_obj, progress=gr.Progress()):
    file_path = [file_obj.name]
    progress(0.1, desc="Creating collection name...")
    collection_name = create_collection_name(file_path[0])
    progress(0.25, desc="Loading document...")
    doc_splits = load_doc(file_path)
    progress(0.5, desc="Generating vector database...")
    vector_db = create_db(doc_splits, collection_name)
    progress(0.75, desc="Initializing LLM...")
    qa_chain = initialize_llmchain(list_llm[0], vector_db, progress)
    if qa_chain is None:
        raise gr.Error("Failed to initialize QA chain. Please check the configuration.")
    progress(1.0, desc="Initialization complete!")
    return qa_chain, "Initialization 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):
    if qa_chain is None:
        return "QA chain is not initialized. Please upload the PDF and initialize again.", 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_source1_page = response_sources[0].metadata["page"] + 1 if "page" in response_sources[0].metadata else "N/A"
    return gr.update(value=""), [(message, response_answer)], response_source1, response_source1_page

def demo():
    with gr.Blocks(theme="base") as demo:
        qa_chain = gr.State()
        
        gr.Markdown(
        """<center><h2>PDF-based chatbot</center></h2>
        <h3>Ask any questions about your PDF document</h3>""")
        
        document = gr.File(height=100, file_types=["pdf"], label="Upload your PDF document")
        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)
                
        msg = gr.Textbox(placeholder="Type message (e.g. 'What is this document about?')", container=True)
        submit_btn = gr.Button("Submit message")
        clear_btn = gr.ClearButton([msg, chatbot], value="Clear conversation")
            
        document.upload(initialize_all, inputs=document, outputs=[qa_chain, gr.Textbox()])
        msg.submit(conversation, inputs=[qa_chain, msg, chatbot], outputs=[msg, chatbot, doc_source1, source1_page])
        submit_btn.click(conversation, inputs=[qa_chain, msg, chatbot], outputs=[msg, chatbot, doc_source1, source1_page])
        clear_btn.click(lambda:[None,"",0], inputs=None, outputs=[chatbot, doc_source1, source1_page])

    demo.queue().launch(debug=True,share=True)

if __name__ == "__main__":
    demo()