import torch
import torchvision
import torch.nn as nn
from torchvision import transforms
## Add more imports if required

####################################################################################################################
# Define your model and transform and all necessary helper functions here                                          #
# They will be imported to the exp_recognition.py file                                                             #
####################################################################################################################

# Definition of classes as dictionary
classes = {0: 'ANGER', 1: 'DISGUST', 2: 'FEAR', 3: 'HAPPINESS', 4: 'NEUTRAL', 5: 'SADNESS', 6: 'SURPRISE'}

# Example Network
class facExpRec(nn.Module):
    def __init__(self, num_classes):
        super(facExpRec, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, padding=1)
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1)
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1)
        self.relu3 = nn.ReLU()
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)

        # Calculate the size of the flattened layer after convolutional and pooling layers
        # Assuming input image size is 48x48
        self.fc1 = nn.Linear(128 * 6 * 6, 512) # 48 / 2 / 2 / 2 = 6
        self.relu4 = nn.ReLU()
        self.dropout = nn.Dropout(0.5)

        self.fc2 = nn.Linear(512, num_classes)

    def forward(self, x):
        x = self.pool1(self.relu1(self.conv1(x)))
        x = self.pool2(self.relu2(self.conv2(x)))
        x = self.pool3(self.relu3(self.conv3(x)))

        x = x.view(-1, 128 * 6 * 6) # Flatten the output for the fully connected layers
        x = self.dropout(self.relu4(self.fc1(x)))
        x = self.fc2(x)
        return x

     
# Sample Helper function
def rgb2gray(image):
    return image.convert('L')
    
# Enhanced Transformation function with augmentations
trnscm = transforms.Compose([
    rgb2gray,                                    # Convert to grayscale
    transforms.Resize((48, 48)),                 # Resize to model input size
    transforms.RandomHorizontalFlip(p=0.3),      # Random horizontal flip
    transforms.RandomRotation(10),               # Slight random rotation
    transforms.ColorJitter(                      # Adjust image properties
        brightness=0.2,
        contrast=0.2
    ),
    transforms.ToTensor(),                       # Convert to tensor
    transforms.Normalize(mean=[0.485],           # Normalize the grayscale image
                       std=[0.229])
])