import torch.nn as nn

class ScriptClassifier(nn.Module):
    """Lightweight Bengali/English script classifier. 23K params, ~0.1MB."""
    def __init__(self):
        super().__init__()
        self.features = nn.Sequential(
            nn.Conv2d(1,16,3,padding=1), nn.BatchNorm2d(16), nn.ReLU(True),
            nn.MaxPool2d(2),
            nn.Conv2d(16,32,3,padding=1), nn.BatchNorm2d(32), nn.ReLU(True),
            nn.MaxPool2d(2),
            nn.Conv2d(32,64,3,padding=1), nn.BatchNorm2d(64), nn.ReLU(True),
            nn.MaxPool2d(2),
            nn.Conv2d(64,64,3,padding=1), nn.BatchNorm2d(64), nn.ReLU(True),
            nn.AdaptiveAvgPool2d((1,1)),
        )
        self.classifier = nn.Sequential(
            nn.Flatten(), nn.Dropout(0.3), nn.Linear(64,2)
        )
    def forward(self, x):
        return self.classifier(self.features(x))
    def predict(self, x):
        """x: (1,1,64,256) tensor. Returns 'bengali' or 'english'."""
        with __import__('torch').no_grad():
            return ['bengali','english'][self.forward(x).argmax(1).item()]