app.py

import gradio as gr
import os

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

from pathlib import Path
import chromadb
from unidecode import unidecode

from transformers import AutoTokenizer
import transformers
import torch
import tqdm 
import accelerate
import re

# LlamaParse import
from llama_parse import LlamaParse
import asyncio
from llama_index.core.async_utils import DEFAULT_NUM_WORKERS, run_jobs
from llama_index.core.base.response.schema import PydanticResponse
from llama_index.core.bridge.pydantic import BaseModel, Field, ValidationError
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.llms.llm import LLM
from llama_index.core.node_parser.interface import NodeParser
from llama_index.core.schema import BaseNode, Document, IndexNode, TextNode
from llama_index.core.utils import get_tqdm_iterable

from io import StringIO
from typing import Any, Callable, List, Optional

import pandas as pd
from llama_index.core.node_parser.relational.base_element import (
#     BaseElementNodeParser,
    Element,
)
from llama_index.core.schema import BaseNode, TextNode


# Obtenha o token da variável de ambiente
api_token = os.getenv("HF_TOKEN")

# Verifique se o token foi obtido corretamente
if api_token is None:
    raise ValueError("O token de API não foi encontrado. Verifique se a variável de ambiente HF_TOKEN está configurada corretamente.")

# Função para ofuscar o token
def obscure_token(token, num_visible=4):
    return '*' * (len(token) - num_visible) + token[-num_visible:]

# Exibir o token de API ofuscado (apenas para debug; remova em produção)
print(f"Token de API: {obscure_token(api_token)}")

# default_persist_directory = './chroma_HF/'
list_llm = ["mistralai/Mistral-7B-Instruct-v0.3", "mistralai/Mixtral-8x7B-Instruct-v0.1", "mistralai/Mistral-7B-Instruct-v0.1", 
    "google/gemma-7b-it","google/gemma-2b-it", 
    "HuggingFaceH4/zephyr-7b-beta", "HuggingFaceH4/zephyr-7b-gemma-v0.1", 
    "meta-llama/Llama-2-7b-chat-hf", "microsoft/phi-2", 
    "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "mosaicml/mpt-7b-instruct", "tiiuae/falcon-7b-instruct", 
    "google/flan-t5-xxl"
]
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):
    embedding = HuggingFaceEmbeddings()
    new_client = chromadb.EphemeralClient()
    vectordb = Chroma.from_documents(
        documents=splits,
        embedding=embedding,
        client=new_client,
        collection_name=collection_name,
    )
    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 == "mistralai/Mixtral-8x7B-Instruct-v0.3":
        llm = HuggingFaceEndpoint(
            repo_id=llm_model, 
            api_key=api_token,
            temperature = temperature,
            max_new_tokens = max_tokens,
            top_k = top_k,
            load_in_8bit = True,
        )
    elif llm_model in ["HuggingFaceH4/zephyr-7b-gemma-v0.1","mosaicml/mpt-7b-instruct"]:
        raise gr.Error("LLM model is too large to be loaded automatically on free inference endpoint")
        llm = HuggingFaceEndpoint(
            repo_id=llm_model, 
            api_key=api_token,
            temperature = temperature,
            max_new_tokens = max_tokens,
            top_k = top_k,
        )
    elif llm_model == "microsoft/phi-2":
        llm = HuggingFaceEndpoint(
            repo_id=llm_model, 
            api_key=api_token,
            temperature = temperature,
            max_new_tokens = max_tokens,
            top_k = top_k,
            trust_remote_code = True,
            torch_dtype = "auto",
        )
    elif llm_model == "TinyLlama/TinyLlama-1.1B-Chat-v1.0":
        llm = HuggingFaceEndpoint(
            repo_id=llm_model,
            api_key=api_token,
            temperature = temperature,
            max_new_tokens = 250,
            top_k = top_k,
        )
    elif llm_model == "meta-llama/Llama-2-7b-chat-hf":
        raise gr.Error("Llama-2-7b-chat-hf model requires a Pro subscription...")
        llm = HuggingFaceEndpoint(
            repo_id=llm_model, 
            api_key=api_token,
            temperature = temperature,
            max_new_tokens = max_tokens,
            top_k = top_k,
        )
    else:
        llm = HuggingFaceEndpoint(
            repo_id=llm_model, 
            api_key=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, 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)
    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 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 upload_file(file_obj):
    list_file_path = []
    for idx, file in enumerate(file_obj):
        file_path = file_obj.name
        list_file_path.append(file_path)
    return list_file_path

# Initialize LlamaIndex parsing
def initialize_llama_index(file_obj):
    documents = LlamaParse(result_type="markdown", api_key=api_token).load_data(file_obj[0].name)
    node_parser = MarkdownElementNodeParser(llm=None, num_workers=8)
    nodes = node_parser.get_nodes_from_documents(documents)
    base_nodes, objects = node_parser.get_nodes_and_objects(nodes)
    index_with_obj = VectorStoreIndex(nodes=base_nodes + objects)
    index_ret = index_with_obj.as_retriever(top_k=15)
    recursive_query_engine = RetrieverQueryEngine.from_args(index_ret, node_postprocessors=[FlagEmbeddingReranker(
        top_n=5,
        model="BAAI/bge-reranker-large",
    )], verbose=False)
    return recursive_query_engine, "LlamaIndex parsing complete"

def demo():
    with gr.Blocks(theme="base") as demo:
        vector_db = gr.State()
        qa_chain = gr.State()
        collection_name = gr.State()
        llama_index_engine = 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_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):
                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 - LlamaIndex parsing"):
            with gr.Row():
                llama_index_btn = gr.Button("Parse with LlamaIndex")
            with gr.Row():
                llama_index_progress = gr.Textbox(label="LlamaIndex parsing status", value="None")

        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")
            
        # Preprocessing events
        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)
        llama_index_btn.click(initialize_llama_index, 
            inputs=[document], 
            outputs=[llama_index_engine, llama_index_progress])

        # 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()