app.py

import gradio as gr
import os
import getpass
from pathlib import Path
import re
from unidecode import unidecode
import chromadb
from langchain_community.vectorstores import FAISS, ScaNN, Milvus
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_community.llms import HuggingFaceEndpoint
import torch
api_token = os.getenv("HF_TOKEN")



list_llm = ["meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.3"]  
list_llm_simple = [os.path.basename(llm) for llm in list_llm]

# 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, db_type):
    embedding = HuggingFaceEmbeddings()
    
    if db_type == "ChromaDB":
        new_client = chromadb.EphemeralClient()
        vectordb = Chroma.from_documents(
            documents=splits,
            embedding=embedding,
            client=new_client,
            collection_name=collection_name,
        )
    elif db_type == "FAISS":
        vectordb = FAISS.from_documents(
            documents=splits,
            embedding=embedding
        )
    elif db_type == "ScaNN":
        vectordb = ScaNN.from_documents(
            documents=splits,
            embedding=embedding
        )
    elif db_type == "Milvus":
        vectordb = Milvus.from_documents(
            documents=splits,
            embedding=embedding,
            collection_name=collection_name,
        )
    else:
        raise ValueError(f"Unsupported vector database type: {db_type}")
    
    return vectordb

# Load vector database
def load_db():
    embedding = HuggingFaceEmbeddings()
    vectordb = Chroma(
        embedding_function=embedding)
    return vectordb

# Initialize langchain LLM chain
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...")

    
    if llm_model == "meta-llama/Meta-Llama-3-8B-Instruct":
        llm = HuggingFaceEndpoint(
            repo_id=llm_model,
            huggingfacehub_api_token=api_token,
            temperature=temperature,
            max_new_tokens=max_tokens,
            top_k=top_k,
        )
   
    else:
        
        llm = HuggingFaceEndpoint(
            repo_id=llm_model,
            huggingfacehub_api_token=api_token,
            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

# Generate collection name for vector database
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

# Initialize database
def initialize_database(list_file_obj, chunk_size, chunk_overlap, db_type, 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, db_type)
    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 "Helpful Answer:" in response_answer:
        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 file in file_obj:
        list_file_path.append(file.name)
    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> Esta é a lucIAna, primeira Versão da IA para seus PDF documentos. 
        Este chatbot leva em consideração perguntas anteriores ao gerar respostas (por meio de memória conversacional) e inclui referências a documentos para fins de clareza.
        """)
        
        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_type_radio = gr.Radio(["ChromaDB", "FAISS", "ScaNN", "Milvus"], label="Vector database type", value="ChromaDB", type="value", 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_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=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(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")
            
        # Preprocessing events
        db_btn.click(initialize_database, 
            inputs=[document, slider_chunk_size, slider_chunk_overlap, db_type_radio], 
            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()