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

# 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()
])

##Example Network
class Siamese(torch.nn.Module):
    def __init__(self):
        super(Siamese, self).__init__()
        self.cnn1 = nn.Sequential(
            nn.ReflectionPad2d(1),       #Pads the input tensor using the reflection of the input boundary, it similar to the padding.
            nn.Conv2d(1, 4, kernel_size=3),
            nn.ReLU(inplace=True),
            nn.BatchNorm2d(4),

            nn.ReflectionPad2d(1),
            nn.Conv2d(4, 8, kernel_size=3),
            nn.ReLU(inplace=True),
            nn.BatchNorm2d(8),

            nn.ReflectionPad2d(1),
            nn.Conv2d(8, 8, kernel_size=3),
            nn.ReLU(inplace=True),
            nn.BatchNorm2d(8),
        )

        self.fc1 = nn.Sequential(
            nn.Linear(8*100*100, 500),
            nn.ReLU(inplace=True),

            nn.Linear(500, 500),
            nn.ReLU(inplace=True),

            nn.Linear(500, 5))

    # forward_once is for one image. This can be used while classifying the face images
    def forward_once(self, x):
        output = self.cnn1(x)
        output = output.view(output.size()[0], -1)
        output = self.fc1(output)
        return output

    def forward(self, input1, input2):
        output1 = self.forward_once(input1)
        output2 = self.forward_once(input2)
        return output1, output2
        
##########################################################################################################
## Sample classification network (Specify if you are using a pytorch classifier during the training)    ##
## classifier = nn.Sequential(nn.Linear(64, 64), nn.BatchNorm1d(64), nn.ReLU(), nn.Linear...)           ##
##########################################################################################################

# Load the existing trained DecisionTree classifier
import joblib
import os

# Get the current directory path
current_path = os.path.dirname(os.path.abspath(__file__))

# Load the pre-trained DecisionTree classifier
try:
    classifier_path = os.path.join(current_path, 'decision_tree_model.sav')
    classifier = joblib.load(classifier_path)
    print(f"✓ Loaded DecisionTree classifier with {len(classifier.classes_)} classes")
except Exception as e:
    print(f"✗ Error loading DecisionTree classifier: {e}")
    classifier = None

# Definition of classes as dictionary
classes = ['Person1','Person2','Person3']