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(torch.nn.Module):
    def __init__(self):
        pass   # remove 'pass' once you have written your code
        #YOUR CODE HERE
        
    def forward(self, x):
        pass   # remove 'pass' once you have written your code
        #YOUR CODE HERE

class ExpressionRecognitionCNN(nn.Module):
    def __init__(self, num_classes = 7):
        super(ExpressionRecognitionCNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(64 * 25 * 25, 128)
        self.fc2 = nn.Linear(128, num_classes)

    def forward(self, x):
        x = self.pool(torch.relu(self.conv1(x)))
        x = self.pool(torch.relu(self.conv2(x)))
        x = x.view(-1, 64 * 25 * 25)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x
        
# Sample Helper function
def rgb2gray(image):
    return image.convert('L')
    
# Sample Transformation function
#YOUR CODE HERE for changing the Transformation values.
trnscm = transforms.Compose([transforms.Grayscale(num_output_channels=1),
    transforms.Resize((100, 100)),
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,)),])