#!/usr/bin/env python3
"""
Evaluate the trained per-letter model on the saved val split.
Prints confusion matrix and a classification report.

Usage:
  python eval_val.py --letter A
"""
import argparse, json
import numpy as np
from sklearn.metrics import confusion_matrix, classification_report
import torch
import torch.nn as nn

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)")
    args = ap.parse_args()
    L = args.letter.upper()

    # Load val split and classes
    X = np.load(f"landmarks_{L}/val_X.npy")
    y = np.load(f"landmarks_{L}/val_y.npy")
    classes = json.load(open(f"landmarks_{L}/class_names.json"))

    # Load checkpoint (disable weights-only safety; handle tensor/ndarray)
    state = torch.load(f"asl_{L}_mlp.pt", map_location="cpu", weights_only=False)
    X_mean = state["X_mean"]
    X_std  = state["X_std"]
    if isinstance(X_mean, torch.Tensor): X_mean = X_mean.cpu().numpy()
    if isinstance(X_std,  torch.Tensor): X_std  = X_std.cpu().numpy()
    X_mean = np.asarray(X_mean, dtype=np.float32)
    X_std  = np.asarray(X_std, dtype=np.float32) + 1e-6

    model = MLP(X.shape[1], len(classes))
    model.load_state_dict(state["model"])
    model.eval()

    # Normalize and predict
    Xn = (X - X_mean) / X_std
    with torch.no_grad():
        probs = torch.softmax(model(torch.from_numpy(Xn).float()), dim=1).numpy()
    pred = probs.argmax(axis=1)

    print("Classes:", classes)  # e.g., ['Not_A','A']
    print("\nConfusion matrix (rows=true, cols=pred):")
    print(confusion_matrix(y, pred))
    print("\nReport:")
    print(classification_report(y, pred, target_names=classes, digits=3))

if __name__ == "__main__":
    main()