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
from torch.utils.data import DataLoader
import numpy as np

# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Streamlit interface
st.title("CNN for Image Classification using CIFAR-10")

# Hyperparameters
num_epochs = st.sidebar.slider("Number of epochs", 1, 20, 10)
batch_size = st.sidebar.slider("Batch size", 10, 200, 100, step=10)
learning_rate = st.sidebar.slider("Learning rate", 0.0001, 0.01, 0.001, step=0.0001)

# CIFAR-10 dataset
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                             download=True, transform=transform)

test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                            download=True, transform=transform)

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

# Define a Convolutional Neural Network
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.layer2 = nn.Sequential(
            nn.Conv2d(32, 64, kernel_size=3),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(2))
        self.fc1 = nn.Linear(6*6*64, 600)
        self.drop = nn.Dropout2d(0.25)
        self.fc2 = nn.Linear(600, 100)
        self.fc3 = nn.Linear(100, 10)

    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.view(out.size(0), -1)
        out = self.fc1(out)
        out = self.drop(out)
        out = self.fc2(out)
        out = self.fc3(out)
        return out

model = CNN().to(device)

# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

# Lists to store losses
train_losses = []
test_losses = []

# Train the model
total_step = len(train_loader)
for epoch in range(num_epochs):
    train_loss = 0
    for i, (images, labels) in enumerate(train_loader):
        images = images.to(device)
        labels = labels.to(device)

        # Forward pass
        outputs = model(images)
        loss = criterion(outputs, labels)

        # Backward and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        train_loss += loss.item()

    train_loss /= total_step
    train_losses.append(train_loss)
    st.write(f'Epoch [{epoch+1}/{num_epochs}], Loss: {train_loss:.4f}')

    # Test the model
    model.eval()
    with torch.no_grad():
        test_loss = 0
        correct = 0
        total = 0
        for images, labels in test_loader:
            images = images.to(device)
            labels = labels.to(device)
            outputs = model(images)
            loss = criterion(outputs, labels)
            test_loss += loss.item()
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

        test_loss /= len(test_loader)
        test_losses.append(test_loss)
        accuracy = 100 * correct / total
        st.write(f'Test Loss: {test_loss:.4f}, Accuracy: {accuracy:.2f}%')
    model.train()

# Plotting the loss
fig, ax = plt.subplots()
ax.plot(range(1, num_epochs + 1), train_losses, label='Train Loss')
ax.plot(range(1, num_epochs + 1), test_losses, label='Test Loss')
ax.set_xlabel('Epoch')
ax.set_ylabel('Loss')
ax.set_title('Training and Test Loss')
ax.legend()
st.pyplot(fig)

# Save the model checkpoint
torch.save(model.state_dict(), 'cnn_model.pth')