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| 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] | |
| 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() |