# -*- coding: utf-8 -*-
"""main.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/#fileId=https%3A//huggingface.co/spaces/neelimapreeti297/panda_cat_dog_classification/blob/main/main.ipynb
"""

#import libraries
import torch
from torchvision import datasets, transforms
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder

#define the data transforms

transform = transforms.Compose([
  transforms.Resize((224,224)),
  transforms.ToTensor(),
  transforms.Normalize((0.485,0.456,0.406),(0.229,0.224,0.225))
  ])

#insert the datasets

train_dataset = ImageFolder('./data/train', transform=transform)
test_dataset =ImageFolder('./data/test', transform=transform)

# make cnn model

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(16 * 53 * 53, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 3)

    def forward(self, x):
        x = self.conv1(x)
        x = self.pool(x)
        x = self.conv2(x)
        x = self.pool(x)
        x = x.view(-1, 16 * 53 * 53)
        x = self.fc1(x)
        x = self.fc2(x)
        x = self.fc3(x)
        return x

batch_size = 8

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)

model = CNN()
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

#Train the model

for epoch in range(10):
    for i, (images, labels) in enumerate(train_loader):

        outputs = model(images)

        loss = loss_function(outputs, labels)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if i % 200 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch + 1, 10, i + 1, len(train_loader), loss.item()))

#iterate over the test data

correct = 0
total = 0
for i, (images, labels) in enumerate(test_loader):
  outputs = model(images)

  _, predicted = torch.max(outputs.data, 1)
  correct += (predicted == labels).sum().item()
  total += labels.size(0)

#calculate the accuracy
accuracy = 100 * correct / total
print('Accuracy: {}%' .format(accuracy))

model_scripted = torch.jit.script(model)
model_scripted.save('./models/cat_dog_cnn.pt')