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import gradio as gr |
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import os |
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from transformers import AutoTokenizer, AutoModelForMaskedLM, pipeline |
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from langchain_community.document_loaders import PyPDFLoader |
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from langchain.text_splitter import RecursiveCharacterTextSplitter |
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from langchain_community.vectorstores import Chroma |
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from langchain.chains import ConversationalRetrievalChain |
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from langchain_community.embeddings import HuggingFaceEmbeddings |
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from langchain.chains import ConversationChain |
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from langchain.memory import ConversationBufferMemory |
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from pathlib import Path |
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import chromadb |
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from unidecode import unidecode |
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import re |
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muril_tokenizer = AutoTokenizer.from_pretrained("google/muril-base-cased") |
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muril_model = AutoModelForMaskedLM.from_pretrained("google/muril-base-cased") |
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def initialize_muril_pipeline(temperature, max_tokens, top_k): |
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muril_pipeline = pipeline( |
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"text-generation", |
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model=muril_model, |
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tokenizer=muril_tokenizer, |
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torch_dtype=torch.bfloat16, |
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device_map="auto", |
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max_new_tokens=max_tokens, |
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do_sample=True, |
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top_k=top_k, |
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num_return_sequences=1, |
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eos_token_id=muril_tokenizer.eos_token_id |
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) |
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return muril_pipeline |
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def load_doc(list_file_path, chunk_size, chunk_overlap): |
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loaders = [PyPDFLoader(x) for x in list_file_path] |
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pages = [] |
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for loader in loaders: |
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pages.extend(loader.load()) |
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text_splitter = RecursiveCharacterTextSplitter( |
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chunk_size=chunk_size, |
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chunk_overlap=chunk_overlap) |
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doc_splits = text_splitter.split_documents(pages) |
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return doc_splits |
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def create_db(splits, collection_name): |
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embedding = HuggingFaceEmbeddings() |
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new_client = chromadb.EphemeralClient() |
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vectordb = Chroma.from_documents( |
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documents=splits, |
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embedding=embedding, |
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client=new_client, |
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collection_name=collection_name, |
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) |
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return vectordb |
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def initialize_llmchain(temperature, max_tokens, top_k, vector_db, progress=gr.Progress()): |
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progress(0.1, desc="Initializing MuRIL model...") |
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muril_pipeline = initialize_muril_pipeline(temperature, max_tokens, top_k) |
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llm = HuggingFacePipeline(pipeline=muril_pipeline, model_kwargs={'temperature': temperature}) |
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progress(0.75, desc="Defining buffer memory...") |
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memory = ConversationBufferMemory( |
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memory_key="chat_history", |
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output_key='answer', |
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return_messages=True |
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) |
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retriever = vector_db.as_retriever() |
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progress(0.8, desc="Defining retrieval chain...") |
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qa_chain = ConversationalRetrievalChain.from_llm( |
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llm, |
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retriever=retriever, |
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chain_type="stuff", |
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memory=memory, |
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return_source_documents=True, |
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verbose=False, |
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) |
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progress(0.9, desc="Done!") |
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return qa_chain |
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def initialize_LLM(llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()): |
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qa_chain = initialize_llmchain(llm_temperature, max_tokens, top_k, vector_db, progress) |
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return qa_chain, "Complete!" |
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def demo(): |
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with gr.Blocks(theme="base") as demo: |
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vector_db = gr.State() |
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qa_chain = gr.State() |
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collection_name = gr.State() |
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gr.Markdown( |
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"""<center><h2>BookMyDarshan: Your Personalized Spiritual Assistant</h2></center> |
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<h3>Explore Sacred Texts and Enhance Your Spiritual Journey</h3>""") |
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gr.Markdown( |
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"""<b>About BookMyDarshan.in:</b> We are a Hyderabad-based startup dedicated to providing pilgrims with exceptional temple darshan experiences. |
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Our platform offers a comprehensive suite of spiritual and religious services, customized to meet your devotional needs.<br><br> |
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<b>Note:</b> This spiritual assistant uses state-of-the-art AI to help you explore and understand your uploaded spiritual documents. |
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With a blend of technology and tradition, this tool assists in connecting you more deeply with your faith.<br>""") |
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with gr.Tab("Step 1: Upload PDF"): |
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document = gr.Files(label="Upload your PDF documents", file_count="multiple", file_types=["pdf"], interactive=True) |
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with gr.Tab("Step 2: Process Document"): |
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db_btn = gr.Radio(["ChromaDB"], label="Select Vector Database", value="ChromaDB", info="Choose your vector database") |
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with gr.Accordion("Advanced Options: Text Splitter", open=False): |
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slider_chunk_size = gr.Slider(minimum=100, maximum=1000, value=600, step=20, label="Chunk Size", info="Adjust chunk size for text splitting") |
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slider_chunk_overlap = gr.Slider(minimum=10, maximum=200, value=40, step=10, label="Chunk Overlap", info="Adjust overlap between chunks") |
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db_progress = gr.Textbox(label="Vector Database Initialization Status", value="None", interactive=False) |
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generate_db_btn = gr.Button("Generate Vector Database") |
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with gr.Tab("Step 3 - Initialize QA chain"): |
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with gr.Row(): |
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with gr.Accordion("Advanced options - LLM model", open=False): |
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with gr.Row(): |
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slider_temperature = gr.Slider(minimum=0.01, maximum=1.0, value=0.7, step=0.1, label="Temperature", info="Model temperature", interactive=True) |
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with gr.Row(): |
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slider_maxtokens = gr.Slider(minimum=224, maximum=4096, value=1024, step=32, label="Max Tokens", info="Model max tokens", interactive=True) |
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with gr.Row(): |
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slider_topk = gr.Slider(minimum=1, maximum=10, value=3, step=1, label="top-k samples", info="Model top-k samples", interactive=True) |
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with gr.Row(): |
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llm_progress = gr.Textbox(value="None", label="QA chain initialization") |
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with gr.Row(): |
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qachain_btn = gr.Button("Initialize Question Answering chain") |
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with gr.Tab("Step 4: Chatbot"): |
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chatbot = gr.Chatbot(label="Chat with your PDF", height=300) |
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with gr.Accordion("Advanced: Document References", open=False): |
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with gr.Row(): |
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doc_source1 = gr.Textbox(label="Reference 1", lines=2) |
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source1_page = gr.Number(label="Page", interactive=True) |
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with gr.Row(): |
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doc_source2 = gr.Textbox(label="Reference 2", lines=2) |
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source2_page = gr.Number(label="Page", interactive=True) |
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with gr.Row(): |
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doc_source3 = gr.Textbox(label="Reference 3", lines=2) |
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source3_page = gr.Number(label="Page", interactive=True) |
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msg = gr.Textbox(placeholder="Type your question here...", label="Ask a Question", container=True) |
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with gr.Row(): |
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submit_btn = gr.Button("Submit") |
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clear_btn = gr.Button("Clear Conversation") |
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generate_db_btn.click(initialize_database, inputs=[document, slider_chunk_size, slider_chunk_overlap], outputs=[vector_db, collection_name, db_progress]) |
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qachain_btn.click( |
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initialize_LLM, |
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inputs=[slider_temperature, slider_maxtokens, slider_topk, vector_db], |
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outputs=[qa_chain, llm_progress] |
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).then( |
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lambda: [None, "", 0, "", 0, "", 0], |
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inputs=None, |
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outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], |
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queue=False |
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) |
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msg.submit( |
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conversation, |
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inputs=[qa_chain, msg, chatbot], |
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outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], |
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queue=False |
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) |
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submit_btn.click( |
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conversation, |
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inputs=[qa_chain, msg, chatbot], |
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outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], |
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queue=False |
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) |
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clear_btn.click( |
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lambda: [None, "", 0, "", 0, "", 0], |
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inputs=None, |
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outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], |
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queue=False |
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) |
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demo.launch() |
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