app/ml/model.py

import torch
from torch import nn


class ECGClassifier(nn.Module):
    """
    Simple 1D CNN for ECG classification.
    """

    def __init__(self, num_classes: int = 2):
        super().__init__()
        self.features = nn.Sequential(
            nn.Conv1d(1, 16, kernel_size=5, padding=2),
            nn.BatchNorm1d(16),
            nn.ReLU(inplace=True),
            nn.MaxPool1d(kernel_size=2),
            nn.Conv1d(16, 32, kernel_size=3, padding=1),
            nn.BatchNorm1d(32),
            nn.ReLU(inplace=True),
            nn.AdaptiveAvgPool1d(1),
        )
        self.classifier = nn.Sequential(
            nn.Flatten(),
            nn.Linear(32, num_classes),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # x shape: (batch, channels=1, length)
        feats = self.features(x)
        logits = self.classifier(feats)
        return logits