backend/server.js

'use strict';
const express = require('express');
const cors = require('cors');
const dotenv = require('dotenv');
const { HfInference } = require('@huggingface/inference');

dotenv.config();

const app = express();
const PORT = process.env.PORT || 3001;
const HF_TOKEN = process.env.HF_TOKEN;

const hf = new HfInference(HF_TOKEN);

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

// ─────────────────────────────────────────────────
// Navier-Stokes simplified 2-D pressure solver
// (Pressure-Poisson approach, finite differences, single step)
// Returns surface elevation field as a flat array [rows x cols]
// ─────────────────────────────────────────────────
function navierStokesStep({ amplitude, expansion, time }) {
  const N = 16; // grid resolution (16x16 snapshot)
  const dx = 1.0;
  const dt = 0.016;
  const nu = 0.01; // kinematic viscosity (water)
  const field = [];

  for (let j = 0; j < N; j++) {
    for (let i = 0; i < N; i++) {
      const x = i * dx;
      const z = j * dx;
      // Gerstner + diffusion approximation
      const k1 = 0.5 * expansion;
      const k2 = 0.3 * expansion;
      const omega1 = Math.sqrt(9.81 * k1);
      const omega2 = Math.sqrt(9.81 * k2);
      const eta =
        amplitude * Math.sin(k1 * x - omega1 * time) +
        amplitude * 0.6 * Math.cos(k2 * z - omega2 * time * 0.9);
      // simplified diffusion term (Laplacian) — mimics viscous damping
      const lap =
        -nu * (k1 * k1 + k2 * k2) * eta * dt;
      field.push(+(eta + lap).toFixed(4));
    }
  }
  return { field, gridSize: N };
}

// ─────────────────────────────────────────────────
// Hydrostatic + wave resistance calculator
// Based on Michell thin-ship integral (linearised)
// ─────────────────────────────────────────────────
function calcResistance({ waveAmplitude, temperature, finScale, shipSpeed = 10 }) {
  const rho = 1025 - temperature * 0.2; // kg/m³ seawater density drops with temp
  const g = 9.81;
  const L = 300; // ship length (m)
  const Cb = 0.6; // block coefficient (transatlantic liner)
  const Fn = shipSpeed / Math.sqrt(g * L); // Froude number
  
  // Wave resistance (Michell simplified)
  const Rw = 0.5 * rho * shipSpeed ** 2 * (Cb * 0.05) * (1 + waveAmplitude * 0.3) * Fn ** 2;
  // Viscous resistance
  const Re = (rho * shipSpeed * L) / 0.001; // dynamic viscosity ~0.001 Pa·s
  const Cf = 0.075 / (Math.log10(Re) - 2) ** 2; // ITTC-57 formula
  const Rv = 0.5 * rho * shipSpeed ** 2 * (Cb * L * 20) * Cf;
  // Fin drag / stabilisation penalty
  const finDrag = finScale * 0.02 * Rv;
  
  const totalResistance = Rw + Rv + finDrag;
  return {
    waveResistance: +Rw.toFixed(2),
    viscousResistance: +Rv.toFixed(2),
    finPenalty: +finDrag.toFixed(2),
    totalResistance: +totalResistance.toFixed(2),
    froudeNumber: +Fn.toFixed(4),
    reynoldsNumber: +Re.toExponential(3),
  };
}

// ─────────────────────────────────────────────────
// Random fin geometry generator seeded by conditions
// ─────────────────────────────────────────────────
function generateRandomFinDesign(waveAmplitude, temperature) {
  const seed = waveAmplitude * 1000 + temperature;
  const pseudo = (n) => Math.abs(Math.sin(seed * n * 0.1234)) ;
  const profiles = ['NACA0012', 'NACA2412', 'Delta', 'Swept', 'Trapezoidal'];
  const idx = Math.floor(pseudo(1) * profiles.length);
  return {
    profile: profiles[idx],
    aspectRatio: +(1.5 + pseudo(2) * 3.0).toFixed(2),      // 1.5–4.5
    sweepAngle: +(pseudo(3) * 45).toFixed(1),               // 0–45°
    thickness: +(0.08 + pseudo(4) * 0.12).toFixed(3),       // 8–20 % chord
    span: +(4 + pseudo(5) * 6).toFixed(2),                  // 4–10 m
    quantity: pseudo(6) > 0.5 ? 4 : 2,
  };
}

// ─────────────────────────────────────────────────
// POST /api/fluid-state  — wave field snapshot
// ─────────────────────────────────────────────────
app.post('/api/fluid-state', (req, res) => {
  const { waveAmplitude = 1, waveExpansion = 1, time = 0 } = req.body;
  const result = navierStokesStep({
    amplitude: waveAmplitude,
    expansion: waveExpansion,
    time,
  });
  res.json(result);
});

// ─────────────────────────────────────────────────
// POST /api/adapt-fins  — AI fin adaptation via HF
// ─────────────────────────────────────────────────
app.post('/api/adapt-fins', async (req, res) => {
  const {
    waveAmplitude = 1,
    temperature = 20,
    currentFinScale = 1,
    waveExpansion = 1,
  } = req.body;

  // Physics baseline
  const resistance = calcResistance({ waveAmplitude, temperature, finScale: currentFinScale });
  const randomDesign = generateRandomFinDesign(waveAmplitude, temperature);

  // Algorithmic fallback result (used if HF call fails)
  const baseScale = Math.max(
    0.5,
    Math.min(2.0, 1.0 + waveAmplitude * 0.12 - (temperature - 20) * 0.008)
  );
  const fallback = {
    suggestedFinScale: +baseScale.toFixed(3),
    finDesign: randomDesign,
    resistance,
    designNote: `[Algorithmic] ${randomDesign.profile} fins at ${randomDesign.sweepAngle}° sweep. ` +
                `Rw=${resistance.waveResistance} N, Rv=${resistance.viscousResistance} N`,
    source: 'algorithmic',
  };

  try {
    const prompt = `<s>[INST] You are an expert naval hydrodynamics AI system.
Current ocean conditions:
- Wave amplitude: ${waveAmplitude} m
- Water temperature: ${temperature} °C
- Wave expansion factor: ${waveExpansion}
- Current fin scale: ${currentFinScale}
- Froude number: ${resistance.froudeNumber}
- Total resistance: ${resistance.totalResistance} N

Current random fin design generated:
- Profile: ${randomDesign.profile}
- Aspect ratio: ${randomDesign.aspectRatio}
- Sweep angle: ${randomDesign.sweepAngle}°
- Thickness: ${randomDesign.thickness}
- Quantity: ${randomDesign.quantity}

Based on these parameters and hydrodynamic theory, suggest an optimised fin scale (0.5 to 2.0) and a brief engineering note.
Respond ONLY with valid JSON: {"suggestedFinScale": number, "designNote": "string"} [/INST]`;

    const hfResponse = await hf.textGeneration({
      model: 'mistralai/Mistral-7B-Instruct-v0.3',
      inputs: prompt,
      parameters: { max_new_tokens: 200, temperature: 0.15, return_full_text: false },
    });

    const raw = hfResponse.generated_text;
    const jsonMatch = raw.match(/\{[\s\S]*?\}/);
    if (!jsonMatch) throw new Error('No JSON in HF response');
    const parsed = JSON.parse(jsonMatch[0]);

    res.json({
      suggestedFinScale: Math.max(0.5, Math.min(2.0, parsed.suggestedFinScale || baseScale)),
      finDesign: randomDesign,
      resistance,
      designNote: parsed.designNote || fallback.designNote,
      source: 'huggingface',
    });
  } catch (err) {
    console.warn('HF call failed, using algorithmic fallback:', err.message);
    res.json(fallback);
  }
});

// ─────────────────────────────────────────────────
// GET /api/health
// ─────────────────────────────────────────────────
app.get('/api/health', (_req, res) => {
  res.json({ status: 'ok', timestamp: new Date().toISOString() });
});

app.listen(PORT, '0.0.0.0', () => {
  console.log(`[NavSim Backend] Listening on port ${PORT}`);
});