app.py

import gradio as gr
import os
from dotenv import load_dotenv
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_huggingface.llms import HuggingFaceEndpoint
from huggingface_hub import login
from pathlib import Path
import chromadb
from unidecode import unidecode
from transformers import AutoTokenizer
import transformers
import torch
import tqdm 
import accelerate
import re

load_dotenv()

huggingface_api_key = os.getenv("HUGGINGFACE_API_KEY")

print('HF TOKEN: ', huggingface_api_key)

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, chunk_overlap):
    loaders = [PyPDFLoader(x) for x in list_file_path]
    pages = []
    for loader in loaders:
        pages.extend(loader.load())
    text_splitter = RecursiveCharacterTextSplitter(chunk_size = 600, chunk_overlap = 50)
    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 load_db():
    embedding = HuggingFaceEmbeddings()
    vectordb = Chroma(embedding_function=embedding)
    return vectordb

def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
    progress(0.1, desc="Initializing HF tokenizer...")
    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

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'
    print('Filepath: ', filepath)
    print('Collection name: ', collection_name)
    return collection_name

def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()):
    list_file_path = [x.name for x in list_file_obj if x is not None]
    progress(0.1, desc="Creating collection name...")
    collection_name = create_collection_name(list_file_path[0])
    progress(0.25, desc="Loading document...")
    doc_splits = load_doc(list_file_path, 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_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
    llm_name = list_llm[llm_option]
    print("llm_name: ",llm_name)
    qa_chain = initialize_llmchain(llm_name, 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 upload_file(file_obj):
    list_file_path = []
    for idx, file in enumerate(file_obj):
        file_path = file_obj.name
        list_file_path.append(file_path)
    return list_file_path

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"):
            with gr.Row():
                document = gr.Files(height=100, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload your PDF documents (single or multiple)")
        
        with gr.Tab("Step 2 - Process document"):
            with gr.Row():
                db_btn = gr.Radio(["ChromaDB"], label="Vector database type", value = "ChromaDB", type="index", info="Choose your vector database")
            with gr.Accordion("Advanced options - Document text splitter", open=False):
                with gr.Row():
                    slider_chunk_size = gr.Slider(minimum = 100, maximum = 1000, value=600, step=20, label="Chunk size", info="Chunk size", interactive=True)
                with gr.Row():
                    slider_chunk_overlap = gr.Slider(minimum = 10, maximum = 200, value=40, step=10, label="Chunk overlap", info="Chunk overlap", interactive=True)
            with gr.Row():
                db_progress = gr.Textbox(label="Vector database initialization", value="None")
            with gr.Row():
                db_generate_btn = gr.Button("Generate vector database")
            
        with gr.Tab("Step 3 - Initialize QA chain"):
            with gr.Row():
                llm_btn = gr.Radio(list_llm_simple, \
                    label="LLM models", value = list_llm_simple[0], type="index", info="Choose your LLM model")
            with gr.Accordion("Advanced options - LLM model", open=False):
                with gr.Row():
                    slider_temperature = gr.Slider(minimum = 0.01, maximum = 1.0, value=0.7, step=0.1, label="Temperature", info="Model temperature", interactive=True)
                with gr.Row():
                    slider_maxtokens = gr.Slider(minimum = 32, maximum = 2048, value=1024, step=16, label="Max tokens", info="Maximum tokens", interactive=True)
                with gr.Row():
                    slider_topk = gr.Slider(minimum = 10, maximum = 50, value=40, step=2, label="Top K", info="Top K", interactive=True)
            with gr.Row():
                llm_progress = gr.Textbox(label="LLM initialization", value="None")
            with gr.Row():
                llm_generate_btn = gr.Button("Initialize LLM chain")

        with gr.Tab("Step 4 - Ask questions to your chatbot"):
            with gr.Row():
                chatbot = gr.Chatbot(label="Langchain PDF chatbot", height=400)
            with gr.Row():
                msg = gr.Textbox(label="Ask anything about your PDF document", placeholder="Type your message here...", show_label=False)
            with gr.Row():
                response_source1 = gr.Textbox(label="Source document #1", value="", interactive=False)
                response_source1_page = gr.Number(label="Page", value=0, interactive=False)
            with gr.Row():
                response_source2 = gr.Textbox(label="Source document #2", value="", interactive=False)
                response_source2_page = gr.Number(label="Page", value=0, interactive=False)
            with gr.Row():
                response_source3 = gr.Textbox(label="Source document #3", value="", interactive=False)
                response_source3_page = gr.Number(label="Page", value=0, interactive=False)
            with gr.Row():
                clear = gr.Button("Clear")

        document.upload(upload_file, [document], [document])
        db_generate_btn.click(initialize_database, inputs=[document, slider_chunk_size, slider_chunk_overlap], outputs=[vector_db, collection_name, db_progress])
        llm_generate_btn.click(initialize_LLM, inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], outputs=[qa_chain, llm_progress])
        msg.submit(conversation, [qa_chain, msg, chatbot], [qa_chain, msg, chatbot, response_source1, response_source1_page, response_source2, response_source2_page, response_source3, response_source3_page])
        clear.click(lambda: None, None, chatbot, queue=False)

    return demo

if __name__ == "__main__":
    demo().queue().launch(debug=True, server_port=7861)  # Use a different port, e.g., 7861