app.py

import gradio as gr
import os
from transformers import AutoTokenizer, AutoModelForMaskedLM, pipeline
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.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from pathlib import Path
import chromadb
from unidecode import unidecode
import re

# Define MuRIL model and tokenizer
muril_tokenizer = AutoTokenizer.from_pretrained("google/muril-base-cased")
muril_model = AutoModelForMaskedLM.from_pretrained("google/muril-base-cased")

# Function to initialize MuRIL pipeline
def initialize_muril_pipeline(temperature, max_tokens, top_k):
    muril_pipeline = pipeline(
        "text-generation",
        model=muril_model,
        tokenizer=muril_tokenizer,
        torch_dtype=torch.bfloat16,
        device_map="auto",
        max_new_tokens=max_tokens,
        do_sample=True,
        top_k=top_k,
        num_return_sequences=1,
        eos_token_id=muril_tokenizer.eos_token_id
    )
    return muril_pipeline

# Load PDF document and create doc splits
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

# 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 using MuRIL
def initialize_llmchain(temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
    progress(0.1, desc="Initializing MuRIL model...")
    muril_pipeline = initialize_muril_pipeline(temperature, max_tokens, top_k)
    
    # Integrate pipeline with langchain
    llm = HuggingFacePipeline(pipeline=muril_pipeline, model_kwargs={'temperature': temperature})
    
    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

# Initialize the LLM chain for your chatbot
def initialize_LLM(llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
    qa_chain = initialize_llmchain(llm_temperature, max_tokens, top_k, vector_db, progress)
    return qa_chain, "Complete!"

# Demo function with Gradio UI
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>BookMyDarshan: Your Personalized Spiritual Assistant</h2></center>
        <h3>Explore Sacred Texts and Enhance Your Spiritual Journey</h3>""")
        
        gr.Markdown(
        """<b>About BookMyDarshan.in:</b> We are a Hyderabad-based startup dedicated to providing pilgrims with exceptional temple darshan experiences. 
        Our platform offers a comprehensive suite of spiritual and religious services, customized to meet your devotional needs.<br><br>
        <b>Note:</b> This spiritual assistant uses state-of-the-art AI to help you explore and understand your uploaded spiritual documents. 
        With a blend of technology and tradition, this tool assists in connecting you more deeply with your faith.<br>""")

        with gr.Tab("Step 1: Upload PDF"):
            document = gr.Files(label="Upload your PDF documents", file_count="multiple", file_types=["pdf"], interactive=True)

        with gr.Tab("Step 2: Process Document"):
            db_btn = gr.Radio(["ChromaDB"], label="Select Vector Database", value="ChromaDB", info="Choose your vector database")
            with gr.Accordion("Advanced Options: Text Splitter", open=False):
                slider_chunk_size = gr.Slider(minimum=100, maximum=1000, value=600, step=20, label="Chunk Size", info="Adjust chunk size for text splitting")
                slider_chunk_overlap = gr.Slider(minimum=10, maximum=200, value=40, step=10, label="Chunk Overlap", info="Adjust overlap between chunks")
            db_progress = gr.Textbox(label="Vector Database Initialization Status", value="None", interactive=False)
            generate_db_btn = gr.Button("Generate Vector Database")

        with gr.Tab("Step 3 - Initialize QA chain"):
            with gr.Row():
                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=224, maximum=4096, value=1024, step=32, label="Max Tokens", info="Model max tokens", interactive=True)
                    with gr.Row():
                        slider_topk = gr.Slider(minimum=1, maximum=10, value=3, step=1, label="top-k samples", info="Model top-k samples", interactive=True)
            with gr.Row():
                llm_progress = gr.Textbox(value="None", label="QA chain initialization")
            with gr.Row():
                qachain_btn = gr.Button("Initialize Question Answering chain")

        with gr.Tab("Step 4: Chatbot"):
            chatbot = gr.Chatbot(label="Chat with your PDF", height=300)
            with gr.Accordion("Advanced: Document References", open=False):
                with gr.Row():
                    doc_source1 = gr.Textbox(label="Reference 1", lines=2)
                    source1_page = gr.Number(label="Page", interactive=True)
                with gr.Row():
                    doc_source2 = gr.Textbox(label="Reference 2", lines=2)
                    source2_page = gr.Number(label="Page", interactive=True)
                with gr.Row():
                    doc_source3 = gr.Textbox(label="Reference 3", lines=2)
                    source3_page = gr.Number(label="Page", interactive=True)
            msg = gr.Textbox(placeholder="Type your question here...", label="Ask a Question", container=True)
            with gr.Row():
                submit_btn = gr.Button("Submit")
                clear_btn = gr.Button("Clear Conversation")

        # Preprocessing events
        generate_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=[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.launch()