app.py

# ================================
# ChatVLD Futurista - Gradio 5.x
# ================================

import os
from pathlib import Path
import requests
import gradio as gr
import time

from langchain_groq import ChatGroq
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.messages import AIMessage, HumanMessage
from langchain_community.document_loaders import PyMuPDFLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.chains import create_history_aware_retriever, create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain

# ================================
# API KEY
# ================================
if "GROQ_API_KEY" not in os.environ:
    raise ValueError("❌ A variável de ambiente GROQ_API_KEY não está definida.")

# ================================
# PDFs
# ================================
pdf_urls = {
    "Codeline SS5632": "https://drive.google.com/uc?id=1s1OPWbxxu8ADBQBjmTfPe1tj-aLcEEIH",
    "Linx 7900": "https://drive.google.com/uc?id=1GVbPq8SDriIS5CQo0kT0EZEqwWwjGJmY"
}

for pdf_name, pdf_url in pdf_urls.items():
    pdf_path = f"{pdf_name}.pdf"
    if not Path(pdf_path).exists():
        r = requests.get(pdf_url)
        with open(pdf_path, "wb") as f:
            f.write(r.content)

# ================================
# CONFIG
# ================================
ID_MODEL = "deepseek-r1-distill-llama-70b"
TEMPERATURE = 0.7

# ================================
# FUNÇÕES
# ================================
def load_llm(model_id, temperature):
    return ChatGroq(
        model=model_id,
        temperature=temperature,
        groq_api_key=os.environ["GROQ_API_KEY"],
        max_tokens=None,
        timeout=None,
        max_retries=2,
    )

def extract_text_pdf(file_path):
    loader = PyMuPDFLoader(file_path)
    docs = loader.load()
    return "\n".join([page.page_content for page in docs])

def config_retriever(pdf_files, nome_impressora):
    embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-m3")
    faiss_path = f"index_faiss_{nome_impressora.replace(' ', '_')}"  # índice único por impressora
    
    if Path(faiss_path).exists():
        vectorstore = FAISS.load_local(faiss_path, embeddings, allow_dangerous_deserialization=True)
    else:
        all_texts = ""
        for file_path in pdf_files:
            all_texts += extract_text_pdf(file_path) + "\n"
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
        chunks = text_splitter.split_text(all_texts)
        vectorstore = FAISS.from_texts(chunks, embeddings)
        vectorstore.save_local(faiss_path)
    return vectorstore.as_retriever(search_type='mmr', search_kwargs={'k': 3, 'fetch_k': 4})

def config_rag_chain(llm, retriever):
    context_q_prompt = ChatPromptTemplate.from_messages([
        ("system","Dada a conversa e a pergunta, formule uma pergunta independente."),
        MessagesPlaceholder("chat_history"),
        ("human", "Pergunta: {input}"),
    ])
    hist_aware_retriever = create_history_aware_retriever(
        llm=llm,
        retriever=retriever,
        prompt=context_q_prompt
    )

    system_prompt = """Você é um assistente virtual futurista da empresa VLD. Responda de forma clara e objetiva em português. Se não souber, diga que não sabe."""

    qa_prompt = ChatPromptTemplate.from_messages([
        ("system", system_prompt),
        MessagesPlaceholder("chat_history"),
        ("human", "Pergunta: {input}\n\nContexto: {context}"),
    ])
    qa_chain = create_stuff_documents_chain(llm, qa_prompt)
    return create_retrieval_chain(hist_aware_retriever, qa_chain)

# ================================
# VARIÁVEIS GLOBAIS
# ================================
llm = load_llm(ID_MODEL, TEMPERATURE)
retrievers_cache = {}
chains_cache = {}
chat_history = [AIMessage(content="🚀 Olá, sou o seu suporte virtual futurista! Como posso te ajudar?")]

# ================================
# PRÉ-CARREGAR TODOS OS ÍNDICES
# ================================
for nome in pdf_urls.keys():
    retrievers_cache[nome] = config_retriever([f"{nome}.pdf"], nome)
    chains_cache[nome] = config_rag_chain(llm, retrievers_cache[nome])

# Define padrão inicial
current_chain = chains_cache["Codeline SS5632"]

# ================================
# SELEÇÃO DE IMPRESSORA
# ================================
def set_impressora(nome_impressora):
    global current_chain
    current_chain = chains_cache[nome_impressora]
    return f"📂 Impressora selecionada: {nome_impressora}"

# ================================
# FUNÇÃO DE RESPOSTA
# ================================
def responder(pergunta):
    global current_chain
    if current_chain is None:
        return "⚠️ Por favor, escolha primeiro a impressora que deseja consultar."
    
    chat_history.append(HumanMessage(content=pergunta))
    try:
        resposta = current_chain.invoke({"input": pergunta, "chat_history": chat_history})["answer"]
        resposta = resposta.split("</think>")[-1].strip() if "</think>" in resposta else resposta.strip()
    except Exception as e:
        resposta = f"❌ Erro: {str(e)}"
    chat_history.append(AIMessage(content=resposta))
    return resposta

# ================================
# CSS FUTURISTA
# ================================
custom_css = """
.gradio-chatbot { background-color: #f8f9fa; color: #111; }
.gradio-textbox textarea { background-color: #1c1c1c; color: #fff; border-radius: 8px; border: 1px solid #333; padding: 8px; }
.gradio-button, .gradio-button:active { background: linear-gradient(to right, #00c6ff, #0072ff); color: #fff; border: none; border-radius: 8px; }

/* 🎇 Estilo futurista para o título principal */
#titulo-principal {
    text-align: center;
    font-size: 40px;
    font-weight: bold;
    background: linear-gradient(90deg, #00c6ff, #8a2be2, #ff0080, #00c6ff);
    -webkit-background-clip: text;
    -webkit-text-fill-color: transparent;
    animation: brilho 5s linear infinite;
    margin-bottom: 20px;
}

@keyframes brilho {
    0% { background-position: 0% 50%; }
    50% { background-position: 100% 50%; }
    100% { background-position: 0% 50%; }
}
"""

# ================================
# INTERFACE GRADIO 5.x
# ================================
with gr.Blocks(css=custom_css, theme="soft") as iface:
    # TÍTULO PRINCIPAL
    gr.Markdown("🛸 SUPORTE AUTOMATIZADO", elem_id="titulo-principal")

    with gr.Tabs():
        # ====================
        # Aba Chat Futurista
        # ====================
        with gr.TabItem("💭 CHATVLD"):
            gr.Markdown("""🤖 **Olá!**  
            Sou o suporte para as impressoras **Codeline SS5632** e **LINX 7900**.""")

            # Aviso acima da seleção
            gr.Markdown("📌 **Por gentileza, escolha a impressora que deseja consultar.**")

            # Dropdown para escolher impressora
            impressora_select = gr.Dropdown(
                choices=list(pdf_urls.keys()),
                label="Selecione a impressora",
                value="Codeline SS5632"  # já vem carregada
            )
            status_box = gr.Textbox(label="Status", interactive=False)

            def troca_impressora(nome_impressora):
                return "⏳ Montando base de conhecimento, aguarde...", set_impressora(nome_impressora)

            impressora_select.change(
                fn=troca_impressora,
                inputs=impressora_select,
                outputs=[status_box, status_box]
            )

            chatbot = gr.Chatbot(type="messages")

            with gr.Row():
                txt = gr.Textbox(
                    placeholder="Diz o que tu quer macho...",
                    show_label=False,
                    lines=2
                )
                submit_btn = gr.Button("🚀 Arrocha")

            with gr.Row():
                clear_btn = gr.Button("🧹 Barrer a prosa")
                new_chat_btn = gr.Button("✨ Nova prosa")

            def enviar(msg, history):
                history.append({"role": "user", "content": msg})
                yield history, ""

                history.append({"role": "assistant", "content": "🤖 Digitando..."})
                yield history, ""

                resposta = responder(msg)
                history[-1] = {"role": "assistant", "content": resposta}
                yield history, ""

            def limpar():
                chat_history.clear()
                return [], ""

            def novo_chat():
                chat_history.clear()
                chat_history.append(AIMessage(content="🤖 Novo chat iniciado. Como posso te ajudar?"))
                return [{"role": "assistant", "content": "🤖 Novo chat iniciado. Como posso te ajudar?"}], ""

            txt.submit(enviar, [txt, chatbot], [chatbot, txt])
            submit_btn.click(enviar, [txt, chatbot], [chatbot, txt])
            clear_btn.click(limpar, outputs=[chatbot, txt])
            new_chat_btn.click(novo_chat, outputs=[chatbot, txt])

        # ====================
        # Aba VALID NODE com botão fixo
        # ====================
        with gr.TabItem("🌐 VALID N.O.D.E"):
            gr.Markdown("### Acesse o VALID NODE clicando no botão abaixo:")
            gr.HTML(
                '<button onclick="window.open(\'https://172.17.200.97\', \'_blank\')" '
                'style="background: linear-gradient(to right, #00c6ff, #0072ff); '
                'color: #fff; border: none; border-radius: 8px; padding: 10px 20px; '
                'font-size: 16px; cursor: pointer;">🖥️ VALID N.O.D.E</button>'
            )

iface.launch()