app.py

import gradio as gr
import os
from huggingface_hub import login
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
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.memory import ConversationBufferMemory
import spaces
from pathlib import Path
import chromadb
from unidecode import unidecode
import re

# Global variables
global_llm = None
global_tokenizer = None
hf_token = os.environ.get("HUGGINGFACEHUB_API_TOKEN")

if not hf_token:
    raise ValueError("Hugging Face token not found. Please set the HF_TOKEN environment variable.")

# Log in to Hugging Face
login(token=hf_token)

list_llm = ["mistralai/Mistral-7B-Instruct-v0.2", "mistralai/Mixtral-8x7B-Instruct-v0.1", "mistralai/Mistral-7B-Instruct-v0.1"]
list_llm_simple = [os.path.basename(llm) for llm in list_llm]

@spaces.GPU
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=chunk_size, 
        chunk_overlap=chunk_overlap)
    doc_splits = text_splitter.split_documents(pages)
    return doc_splits

def create_db(splits, collection_name):
    if torch.cuda.is_available():
        os.environ["CUDA_VISIBLE_DEVICES"] = "0"
    
    embedding = HuggingFaceEmbeddings(
        model_name="sentence-transformers/all-MiniLM-L6-v2",
        model_kwargs={"use_auth_token": hf_token}
    )
    new_client = chromadb.EphemeralClient()
    vectordb = Chroma.from_documents(
        documents=splits,
        embedding=embedding,
        client=new_client,
        collection_name=collection_name,
    )
    return vectordb

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_global_llm(llm_model, temperature, max_tokens, top_k, progress=gr.Progress()):
    global global_llm, global_tokenizer
    
    if global_llm is None:
        progress(0.1, desc="Initializing HF tokenizer...")
        global_tokenizer = AutoTokenizer.from_pretrained(llm_model, use_auth_token=hf_token)
        
        progress(0.3, desc="Loading model...")
        try:
            model = AutoModelForCausalLM.from_pretrained(
                llm_model, 
                use_auth_token=hf_token,
                torch_dtype=torch.float16, 
                device_map="auto"
            )
        except RuntimeError as e:
            if "CUDA out of memory" in str(e):
                raise gr.Error("GPU memory exceeded. Try a smaller model or reduce batch size.")
            else:
                raise e
        
        progress(0.5, desc="Initializing HF pipeline...")
        pipeline = transformers.pipeline(
            "text-generation",
            model=model,
            tokenizer=global_tokenizer,
            torch_dtype=torch.float16,
            device_map="auto",
            max_new_tokens=max_tokens,
            do_sample=True,
            top_k=top_k,
            num_return_sequences=1,
            eos_token_id=global_tokenizer.eos_token_id
        )
        global_llm = HuggingFacePipeline(pipeline=pipeline, model_kwargs={'temperature': temperature})
        
        progress(0.9, desc="LLM initialization complete!")
        return "LLM initialized successfully!"
    else:
        progress(0.9, desc="Using previously initialized LLM.")
        return "Using previously initialized LLM."

def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()):
    if list_file_obj is None or len(list_file_obj) == 0:
        return None, None, "Error: No files uploaded. Please upload PDF files first."

    list_file_path = [x.name for x in list_file_obj if x is not None]
    if not list_file_path:
        return None, None, "Error: No valid files found. Please upload PDF files."

    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]
    global_llm = initialize_global_llm(llm_name, llm_temperature, max_tokens, top_k, progress)
    
    memory = ConversationBufferMemory(
        memory_key="chat_history",
        output_key='answer',
        return_messages=True
    )
    retriever = vector_db.as_retriever()
    qa_chain = ConversationalRetrievalChain.from_llm(
        global_llm,
        retriever=retriever,
        chain_type="stuff", 
        memory=memory,
        return_source_documents=True,
        verbose=False,
    )
    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>GPU-Accelerated PDF-based Chatbot</center></h2>
        <h3>Ask any questions about your PDF documents</h3>""")
        gr.Markdown(
        """<b>Note:</b> This AI assistant uses GPU acceleration for faster processing. 
        It performs retrieval-augmented generation (RAG) from your PDF documents using Langchain and open-source LLMs. 
        This chatbot takes past questions into account and includes document references.""")
        
        with gr.Tab("Step 1 - Initialize LLM"):
            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):
                slider_temperature = gr.Slider(minimum=0.01, maximum=1.0, value=0.7, step=0.1, label="Temperature", info="Model temperature", interactive=True)
                slider_maxtokens = gr.Slider(minimum=224, maximum=4096, value=1024, step=32, label="Max Tokens", info="Model max tokens", interactive=True)
                slider_topk = gr.Slider(minimum=1, maximum=10, value=3, step=1, label="top-k samples", info="Model top-k samples", interactive=True)
            llm_progress = gr.Textbox(value="Not initialized", label="LLM initialization status")
            init_llm_btn = gr.Button("Initialize LLM")

        with gr.Tab("Step 2 - Upload PDF"):
            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 3 - Process document"):
            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):
                slider_chunk_size = gr.Slider(minimum = 100, maximum = 1000, value=600, step=20, label="Chunk size", info="Chunk size", interactive=True)
                slider_chunk_overlap = gr.Slider(minimum = 10, maximum = 200, value=40, step=10, label="Chunk overlap", info="Chunk overlap", interactive=True)
            db_progress = gr.Textbox(label="Vector database initialization", value="None")
            db_btn = gr.Button("Generate vector database")
            
        with gr.Tab("Step 4 - Initialize QA chain"):
            llm_progress = gr.Textbox(value="None",label="QA chain initialization")
            qachain_btn = gr.Button("Initialize Question Answering chain")

        with gr.Tab("Step 5 - 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")
            
        # Event handlers
        init_llm_btn.click(
            initialize_global_llm,
            inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk],
            outputs=[llm_progress]
        )
        
        db_btn.click(initialize_database, 
            inputs=[document, slider_chunk_size, slider_chunk_overlap], 
            outputs=[vector_db, collection_name, db_progress])
        
        qachain_btn.click(initialize_LLM, 
            inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], 
            outputs=[qa_chain, llm_progress]).then(lambda:[None,"",0,"",0,"",0], 
            inputs=None, 
            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], 
            queue=False)

        # 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)
        clear_btn.click(lambda:[None,"",0,"",0,"",0], 
            inputs=None, 
            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], 
            queue=False)

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

if __name__ == "__main__":
    demo()