import torch
import torch.nn as nn
import torch.nn.functional as F

class FFTCNN(nn.Module):
    """
    Defines the Convolutional Neural Network architecture.
    This structure must match the model that was trained and saved.
    """
    def __init__(self):
        super(FFTCNN, self).__init__()
         # Ensure 'self.' is used here to define the layers as instance attributes
        self.conv_layers = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(16, 32, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        
        # Ensure 'self.' is used here as well
        self.fc_layers = nn.Sequential(
            nn.Linear(32 * 56 * 56, 128), # This size depends on your 224x224 input
            nn.ReLU(),
            nn.Linear(128, 2) # 2 output classes
        )

    def forward(self, x):
        # Now, 'self.conv_layers' can be found because it was defined correctly
        x = self.conv_layers(x)
        x = x.view(x.size(0), -1) # Flatten the feature maps
        x = self.fc_layers(x)
        return x