#!/usr/bin/env python3
"""
train_mlp.py
Train a small MLP on landmarks for a single letter (binary: Letter vs Not_Letter).

Expected workflow:
  python prep_landmarks_binary.py --letter A   # saves landmarks_A/
  python train_mlp.py --letter A --epochs 40 --batch 64
  python infer_webcam.py --letter A
"""
import os, json, argparse
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import TensorDataset, DataLoader

def get_device():
    return torch.device("mps") if torch.backends.mps.is_available() else torch.device("cpu")

class MLP(nn.Module):
    def __init__(self, in_dim, num_classes):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(in_dim, 128),
            nn.ReLU(),
            nn.Dropout(0.2),
            nn.Linear(128, 64),
            nn.ReLU(),
            nn.Dropout(0.1),
            nn.Linear(64, num_classes),
        )
    def forward(self, x): return self.net(x)

def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--letter", required=True, help="Target letter (A–Z)")
    ap.add_argument("--epochs", type=int, default=40)
    ap.add_argument("--batch", type=int, default=64)
    ap.add_argument("--lr", type=float, default=1e-3)
    ap.add_argument("--landmarks", default=None,
        help="Landmarks folder (default: landmarks_<LETTER>)")
    ap.add_argument("--out", default=None,
        help="Output filename (default: asl_<LETTER>_mlp.pt)")
    args = ap.parse_args()

    letter = args.letter.upper()
    landmarks_dir = args.landmarks or f"landmarks_{letter}"
    out_file = args.out or f"asl_{letter}_mlp.pt"

    # Load data
    trX = np.load(os.path.join(landmarks_dir, "train_X.npy"))
    trY = np.load(os.path.join(landmarks_dir, "train_y.npy"))
    vaX = np.load(os.path.join(landmarks_dir, "val_X.npy"))
    vaY = np.load(os.path.join(landmarks_dir, "val_y.npy"))
    with open(os.path.join(landmarks_dir, "class_names.json")) as f:
        classes = json.load(f)

    print(f"Letter: {letter}")
    print(f"Loaded: train {trX.shape}  val {vaX.shape}  classes={classes}")

    # Standardize using train mean/std
    X_mean_np = trX.mean(axis=0, keepdims=True).astype(np.float32)
    X_std_np  = (trX.std(axis=0, keepdims=True) + 1e-6).astype(np.float32)
    trXn   = (trX - X_mean_np) / X_std_np
    vaXn   = (vaX - X_mean_np) / X_std_np

    # Torch datasets
    tr_ds = TensorDataset(torch.from_numpy(trXn).float(), torch.from_numpy(trY).long())
    va_ds = TensorDataset(torch.from_numpy(vaXn).float(), torch.from_numpy(vaY).long())
    tr_dl = DataLoader(tr_ds, batch_size=args.batch, shuffle=True)
    va_dl = DataLoader(va_ds, batch_size=args.batch, shuffle=False)

    device = get_device()
    model = MLP(in_dim=trX.shape[1], num_classes=len(classes)).to(device)
    criterion = nn.CrossEntropyLoss()
    opt = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)
    sched = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=args.epochs)

    best_acc, best_state = 0.0, None

    for epoch in range(1, args.epochs + 1):
        # Train
        model.train()
        tot, correct, loss_sum = 0, 0, 0.0
        for xb, yb in tr_dl:
            xb, yb = xb.to(device), yb.to(device)
            opt.zero_grad(set_to_none=True)
            logits = model(xb)
            loss = criterion(logits, yb)
            loss.backward()
            opt.step()
            loss_sum += loss.item() * yb.size(0)
            correct += (logits.argmax(1) == yb).sum().item()
            tot += yb.size(0)
        tr_loss = loss_sum / max(1, tot)
        tr_acc  = correct / max(1, tot)

        # Validate
        model.eval()
        vtot, vcorrect = 0, 0
        with torch.no_grad():
            for xb, yb in va_dl:
                xb, yb = xb.to(device), yb.to(device)
                logits = model(xb)
                vcorrect += (logits.argmax(1) == yb).sum().item()
                vtot += yb.size(0)
        va_acc = vcorrect / max(1, vtot)
        sched.step()

        print(f"Epoch {epoch:02d}: train_loss={tr_loss:.4f}  train_acc={tr_acc:.3f}  val_acc={va_acc:.3f}")

        if va_acc > best_acc:
            best_acc = va_acc
            # Save stats as **tensors** (future-proof for torch.load safety)
            best_state = {
                "model": model.state_dict(),
                "classes": classes,
                "X_mean": torch.from_numpy(X_mean_np),  # tensor
                "X_std": torch.from_numpy(X_std_np),    # tensor
            }
            torch.save(best_state, out_file)
            print(f"  ✅ Saved best → {out_file} (val_acc={best_acc:.3f})")

    print("Done. Best val_acc:", best_acc)

if __name__ == "__main__":
    main()