app.py

from huggingface_hub import InferenceClient
import gradio as gr
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import HuggingFaceEmbeddings
import os

# CSS para estilização
css = '''
.gradio-container{max-width: 1000px !important}
h1{text-align:center}
footer {visibility: hidden}
'''

# Inicializar o cliente de inferência
client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")

# Configurar o retriever globalmente
def initialize_retriever(file_objs):
    """Carrega documentos PDFs e cria um retriever."""
    if not file_objs:
        return None, "Nenhum documento carregado."
    
    # Carregar e dividir documentos
    documents = []
    for file_obj in file_objs:
        loader = PyPDFLoader(file_obj.name)
        documents.extend(loader.load())
    
    # Dividir em pedaços menores
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=2048, chunk_overlap=128)
    splits = text_splitter.split_documents(documents)
    
    # Criar embeddings e banco de vetores
    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
    vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings)
    retriever = vectorstore.as_retriever(search_kwargs={"k": 2})  # Retorna 2 documentos mais relevantes
    
    return retriever, "Documentos processados com sucesso!"

# Formatar o prompt para RAG
def format_prompt(message, history, retriever=None, system_prompt=None):
    prompt = "<s>"
    
    # Adicionar histórico
    for user_prompt, bot_response in history:
        prompt += f"[INST] {user_prompt} [/INST]"
        prompt += f" {bot_response}</s> "
    
    # Adicionar instrução do sistema, se fornecida
    if system_prompt:
        prompt += f"[SYS] {system_prompt} [/SYS]"
    
    # Adicionar contexto recuperado, se houver retriever
    if retriever:
        # Buscar documentos relevantes
        docs = retriever.get_relevant_documents(message)
        context = "\n".join([doc.page_content for doc in docs])
        prompt += f"[CONTEXT] {context} [/CONTEXT]"
    
    # Adicionar a mensagem do usuário
    prompt += f"[INST] {message} [/INST]"
    return prompt

# Função de geração com RAG
def generate(
    prompt, history, retriever=None, system_prompt=None, temperature=0.2, max_new_tokens=1024, top_p=0.95, repetition_penalty=1.0
):
    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )

    # Formatar o prompt com contexto RAG, se disponível
    formatted_prompt = format_prompt(prompt, history, retriever, system_prompt)

    # Gerar resposta em streaming
    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
    output = ""

    for response in stream:
        output += response.token.text
        yield output

# Interface Gradio com RAG
def create_demo():
    with gr.Blocks(css=css) as demo:
        retriever_state = gr.State(value=None)
        status = gr.State(value="Nenhum documento carregado")

        # Título
        gr.Markdown("<h1>RAG Chatbot</h1>")

        # Seção de upload de documentos
        with gr.Row():
            with gr.Column(scale=1):
                gr.Markdown("### Carregar Documentos")
                file_input = gr.Files(label="Upload PDFs", file_types=["pdf"], file_count="multiple")
                process_btn = gr.Button("Processar Documentos")
                status_output = gr.Textbox(label="Status", value="Nenhum documento carregado")

        # Interface de chat
        chat_interface = gr.ChatInterface(
            fn=generate,
            additional_inputs=[
                gr.State(value=retriever_state),  # Passa o retriever como entrada adicional
                gr.Textbox(label="System Prompt", placeholder="Digite um prompt de sistema (opcional)", value=None)
            ],
            title="",
        )

        # Evento para processar documentos
        process_btn.click(
            fn=initialize_retriever,
            inputs=[file_input],
            outputs=[retriever_state, status_output]
        )

    return demo

# Lançar a aplicação
demo = create_demo()
demo.queue().launch(share=False)