import torch
import torchvision
import torch.nn as nn
from torchvision import transforms
import torch.nn.functional as F
## 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 Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        
       
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=3)
         
                   
        self.conv2 = nn.Conv2d(in_channels=16, out_channels=64, kernel_size=3)

        self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3)
        
        # Define the Fully connected layers
        # The output of the second convolution layer will be input to the first fully connected layer
        self.fc1 = nn.Linear(128*10*10, 256)
        # 256 input features, 128 output features 
        self.fc2 = nn.Linear(256, 128)
        # 128 input features, 64 output features 
        self.fc3 = nn.Linear(128, 64)
        # 64 input features, 7 output features for our 7 defined classes
        self.fc4 = nn.Linear(64, 7)

        # Max pooling
        self.pool = nn.MaxPool2d(kernel_size=2)  # Max pooling layer with filter size 2x2
   
    def forward(self, x):

        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = self.pool(F.relu(self.conv3(x)))
        # Flatten the image
        x = x.view(-1, 128*10*10)  # Output shape of convolutional layer is 16*5*5 
        
        # Linear layers with RELU activation
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = F.relu(self.fc3(x))
        x = self.fc4(x)
        x = F.log_softmax(x, dim=1)
        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.Resize((224,224)), transforms.ToTensor()])