import express from 'express';
import { GoogleGenAI, mcpToTool } from '@google/genai';
import { Client } from '@modelcontextprotocol/sdk/client/index.js';
import { StreamableHTTPClientTransport } from '@modelcontextprotocol/sdk/client/streamableHttp.js';
import dotenv from 'dotenv';

dotenv.config();

interface Conversation {
  chatId: string;
  messages: { text: string }[];
  createdAt: number;
  updatedAt: number;
  messageCount: number;
}

const PORT = Number(process.env.PORT) || 8001;
const API_KEY = process.env.GEMINI_API_KEY || '';
console.log(`Using Gemini API key: ${API_KEY}`);

const genAI = API_KEY ? new GoogleGenAI({ apiKey: API_KEY }) : null;

const MCP_URL = process.env.MCP_URL || 'http://planilla.interno.com/mcp';
let mcpClient: Client | undefined;
let mcpTransport: StreamableHTTPClientTransport | undefined;

async function getMcpClient(): Promise<Client> {
  if (!mcpClient) {
    mcpClient = new Client({ name: 'planilla-client', version: '1.0.0' });
    mcpTransport = new StreamableHTTPClientTransport(new URL(MCP_URL));
    await mcpClient.connect(mcpTransport);
  }
  return mcpClient;
}
const repoInfo = `This repository contains a WhatsApp router, a simple chat UI and now a conversation-layer-agent service.
- whatsapp-router: Forwards WhatsApp messages to configured agents. 
- chat-ui: Minimal web interface that also talks to an agent. 
- conversation-layer-agent: Answers questions about the repository.
Run all services with docker-compose. Configure handler mappings in whatsapp-router/src/chatHandlers.ts.`;

const app = express();
app.use(express.json());

app.post('/', async (req, res) => {
  const conversation = req.body?.conversation as Conversation | undefined;
  if (!conversation) return res.status(400).json({ error: 'Missing conversation' });
  const message = conversation.messages[conversation.messages.length - 1]?.text || '';

  if (!genAI) {
    return res.json({ reply: repoInfo });
  }

  try {
    const contents = `Repo information: ${repoInfo}\nUser message: ${message}`;
    const config: any = {};
    if (message.toLowerCase().includes('/planilla')) {
      console.log('Using Model Context Protocol tools ', MCP_URL);
      const client = await getMcpClient();
      config.tools = [mcpToTool(client)];
    }
    const result = await genAI.models.generateContent({
      model: 'gemini-2.0-flash',
      contents,
      config,
    });
    const reply = (result.text || '').trim();
    res.json({ reply });
  } catch (err: any) {
    console.error('Gemini error', err.message);
    res.status(500).json({ error: 'Failed to generate reply' });
  }
});

app.get('/', (req, res) => {
  res.send(`
    <h1>Conversation Layer Agent</h1>
    <p>This service answers questions about the repository.</p>
    <p>Send a POST request to / with a JSON body containing {"conversation": {...}}</p>
    <p>Example: {"conversation": {"chatId": "123@c.us", "messages": [{"text": "hello"}]}}</p>
    <p>It will respond with a JSON object containing {"reply": "the answer"}</p>

    <p>Repository info: ${repoInfo}</p>
  `);
}
);

app.listen(PORT, () => {
  console.log(`conversation-layer-agent listening on ${PORT}`);
});