# 1️⃣ Crie um virtualenv Python
python -m venv .venv && source .venv/bin/activate

# 2️⃣ Instale dependências
pip install fastapi uvicorn openai playwright

# 3️⃣ Instale o driver do navegador (Playwright)
playwright install chromium

# app.py
import os, json, asyncio
from fastapi import FastAPI, WebSocket
from openai import AsyncOpenAI
from playwright.async_api import async_playwright

app = FastAPI()
client = AsyncOpenAI(api_key=os.getenv("OPENAI_API_KEY"))

# WebSocket para comunicação em tempo real com a extensão
@app.websocket("/ws")
async def ws_endpoint(ws: WebSocket):
    await ws.accept()
    async with async_playwright() as p:
        browser = await p.chromium.launch(headless=False)
        page = await browser.new_page()
        await page.goto("https://example.com/login")

        while True:
            data = await ws.receive_text()
            request = json.loads(data)          # {action:"fill", selector:"#email", value:""}
            if request["action"] == "fill":
                # Prompt para o LLM gerar valor (ex.: email aleatório)
                prompt = f"Generate a realistic email for testing."
                resp = await client.chat.completions.create(
                    model="gpt-4o-mini",
                    messages=[{"role":"user","content":prompt}],
                    stream=False,
                )
                value = resp.choices[0].message.content.strip()
                await page.fill(request["selector"], value)
                await ws.send_text(json.dumps({"status":"filled","selector":request["selector"],"value":value}))

// manifest.json
{
  "name": "IA Form Filler",
  "manifest_version": 3,
  "permissions": ["scripting", "activeTab", "webNavigation"],
  "background": { "service_worker": "bg.js" },
  "action": { "default_popup": "popup.html" }
}

// bg.js – abre WebSocket com o orquestrador
chrome.runtime.onInstalled.addListener(() => {
  const ws = new WebSocket("ws://localhost:8000/ws");
  ws.onmessage = (e) => console.log("IA:", e.data);
  chrome.action.onClicked.addListener(async (tab) => {
    ws.send(JSON.stringify({action:"fill", selector:"#email"}));
  });
});

# 1️⃣ Inicie o orquestrador
uvicorn app:app --reload --port 8000

# 2️⃣ Carregue a extensão no Chrome (chrome://extensions → "Modo de desenvolvedor" → "Carregar sem compactação")
# 3️⃣ Abra https://example.com/login e clique no ícone da extensão.

// monitor.js
const { chromium } = require('playwright');
const { OpenAI } = require('openai');
const sqlite3 = require('sqlite3').verbose();
const telegram = require('node-telegram-bot-api');

const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
const bot = new telegram(process.env.TELEGRAM_BOT_TOKEN);
const db = new sqlite3.Database('prices.db');

(async () => {
  const browser = await chromium.launch({ headless: true });
  const page = await browser.newPage();
  await page.goto('https://store.example.com/product/123');

  const priceText = await page.textContent('.price');
  const price = parseFloat(priceText.replace(/[^\d.,]/g, '').replace(',', '.'));

  db.get('SELECT last_price FROM product WHERE id = 123', async (err, row) => {
    const last = row?.last_price ?? price;
    const prompt = `O preço atual é R$${price}. O último preço registrado foi R$${last}. Deve eu avisar o usuário? Responda apenas "sim" ou "não".`;
    const resp = await openai.chat.completions.create({
      model: "gpt-4o-mini",
      messages: [{ role: "user", content: prompt }],
    });
    if (resp.choices[0].message.content.trim().toLowerCase() === "sim") {
      await bot.sendMessage(process.env.TELEGRAM_CHAT_ID,
        `📉 Preço caiu! De R$${last} para R$${price}.`);
    }
    db.run('INSERT OR REPLACE INTO product (id, last_price) VALUES (123, ?)', price);
  });

  await browser.close();
})();