import streamlit as st
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np

# Define the CNN
class SimpleCNN(nn.Module):
    def __init__(self):
        super(SimpleCNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(32 * 8 * 8, 128)
        self.fc2 = nn.Linear(128, 10)
    
    def forward(self, x):
        x = self.pool(torch.relu(self.conv1(x)))
        x = self.pool(torch.relu(self.conv2(x)))
        x = x.view(-1, 32 * 8 * 8)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# Function to train the model
def train_model(num_epochs):
    transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
    ])

    trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
    trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)

    testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
    testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)

    CIFAR10_CLASSES = [
        'plane', 'car', 'bird', 'cat', 'deer',
        'dog', 'frog', 'horse', 'ship', 'truck'
    ]

    net = SimpleCNN()
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)

    loss_values = []
    st.write("Training the model...")

    for epoch in range(num_epochs):
        running_loss = 0.0
        for i, data in enumerate(trainloader, 0):
            inputs, labels = data
            optimizer.zero_grad()
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            running_loss += loss.item()
        loss_values.append(running_loss / len(trainloader))
        st.write(f'Epoch {epoch + 1}, Loss: {running_loss / len(trainloader):.3f}')
    st.write('Finished Training')

    # Plot the loss values
    plt.figure(figsize=(10, 5))
    plt.plot(range(1, num_epochs + 1), loss_values, marker='o')
    plt.title('Training Loss over Epochs')
    plt.xlabel('Epoch')
    plt.ylabel('Loss')
    st.pyplot(plt)

    correct = 0
    total = 0
    with torch.no_grad():
        for data in testloader:
            images, labels = data
            outputs = net(images)
            _, predicted = torch.max(outputs, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    st.write(f'Accuracy of the network on the 10000 test images: {100 * correct / total}%')

    # Visualize some test images and their predictions
    def imshow(img):
        img = img / 2 + 0.5  # Unnormalize
        npimg = img.numpy()
        plt.imshow(np.transpose(npimg, (1, 2, 0)))
        plt.show()

    dataiter = iter(testloader)
    images, labels = next(dataiter)

    imshow(torchvision.utils.make_grid(images))

    outputs = net(images)
    _, predicted = torch.max(outputs, 1)

    st.write('Predicted: ', ' '.join(f'{CIFAR10_CLASSES[predicted[j]]:5s}' for j in range(8)))
    st.write('Actual:    ', ' '.join(f'{CIFAR10_CLASSES[labels[j]]:5s}' for j in range(8)))
    st.pyplot()

# Streamlit interface
st.title('CIFAR-10 Classification with PyTorch')
num_epochs = st.number_input('Enter number of epochs:', min_value=1, max_value=100, value=10)
if st.button('Run'):
    train_model(num_epochs)