index.js

const express = require('express');
const cors = require('cors');
const fetch = require('node-fetch');

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

const NVIDIA_API_KEY = process.env.NVIDIA_API_KEY;
const NVIDIA_BASE_URL = 'https://integrate.api.nvidia.com/v1';

const TEXT_MODELS = [
  'meta/llama-3.1-405b-instruct',
  'moonshotai/kimi-k2-instruct',
  'qwen/qwen3-coder-480b-a35b-instruct',
  'mistralai/mistral-large-3-675b-instruct-2512'
];

const IMAGE_MODEL = 'nvidia/sdxl';
const IMAGE_FALLBACK = 'black-forest-labs/flux-1-dev';
const VISION_MODEL = 'nvidia/neva-22b';

async function describeImage(base64Image) {
  try {
    const res = await fetch(`${NVIDIA_BASE_URL}/chat/completions`, {
      method: 'POST',
      headers: {
        'Authorization': `Bearer ${NVIDIA_API_KEY}`,
        'Content-Type': 'application/json'
      },
      body: JSON.stringify({
        model: VISION_MODEL,
        messages: [{
          role: 'user',
          content: [
            { type: 'text', text: 'Describe this image in detail, including any text visible.' },
            { type: 'image_url', image_url: { url: `data:image/png;base64,${base64Image}` } }
          ]
        }],
        max_tokens: 300,
        temperature: 0.2
      })
    });
    if (!res.ok) return null;
    const json = await res.json();
    return json.choices?.[0]?.message?.content || null;
  } catch (e) { return null; }
}

async function generateImage(prompt) {
  const models = [IMAGE_MODEL, IMAGE_FALLBACK];
  for (const model of models) {
    try {
      const res = await fetch(`${NVIDIA_BASE_URL}/images/generations`, {
        method: 'POST',
        headers: {
          'Authorization': `Bearer ${NVIDIA_API_KEY}`,
          'Content-Type': 'application/json'
        },
        body: JSON.stringify({ model, prompt, n: 1, size: '1024x1024', response_format: 'b64_json' })
      });
      if (!res.ok) continue;
      const json = await res.json();
      const b64 = json.data?.[0]?.b64_json;
      if (b64) return `data:image/png;base64,${b64}`;
    } catch (e) { continue; }
  }
  return null;
}

async function callNVIDIA(messages, temperature = 0.6, max_tokens = 800) {
  for (const model of TEXT_MODELS) {
    try {
      const res = await fetch(`${NVIDIA_BASE_URL}/chat/completions`, {
        method: 'POST',
        headers: {
          'Authorization': `Bearer ${NVIDIA_API_KEY}`,
          'Content-Type': 'application/json'
        },
        body: JSON.stringify({ model, messages, temperature, max_tokens })
      });
      if (!res.ok) continue;
      const json = await res.json();
      return json.choices?.[0]?.message?.content || '';
    } catch (e) { continue; }
  }
  throw new Error('All models failed');
}

app.post('/ask', async (req, res) => {
  const { question, system_prompt, image_base64 } = req.body;
  const today = new Date().toDateString();

  let systemContent = system_prompt || `Your name is GENUS GPT. Today: ${today}`;
  let userMessage = question || 'What do you see?';

  if (image_base64) {
    const desc = await describeImage(image_base64);
    if (desc) userMessage = `[Image: ${desc}]\n\nQuestion: ${question || 'What do you see?'}`;
  }

  const messages = [
    { role: 'system', content: systemContent },
    { role: 'user', content: userMessage }
  ];

  try {
    const rawAnswer = await callNVIDIA(messages);
    let reasoning = '', answer = rawAnswer;
    const m = rawAnswer.match(/\[REASONING\]([\s\S]*?)\[\/REASONING\]/i);
    if (m) { reasoning = m[1].trim(); answer = rawAnswer.replace(/\[REASONING\][\s\S]*?\[\/REASONING\]/i, '').trim(); }

    let asset = null, type = 'text';
    if (answer.includes('[GENERATE_IMG:')) {
      const p = answer.split('[GENERATE_IMG:')[1].split(']')[0];
      asset = await generateImage(p);
      type = asset ? 'image' : 'text';
    }

    res.json({ answer, reasoning, asset, type });
  } catch (e) {
    res.status(500).json({ error: 'Overloaded' });
  }
});

app.listen(7860, () => console.log('Genus GPT Engine Live on NVIDIA'));