# ============================================================
# MNIST Digit Classification using a Simple Neural Network (PyTorch)
# ============================================================

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np

# ------------------------------------------------------------
# 1. Device Configuration (GPU if available)
# ------------------------------------------------------------
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("Using device:", device)

# ------------------------------------------------------------
# 2. Load MNIST Dataset
# ------------------------------------------------------------
transform = transforms.Compose([
    transforms.ToTensor(),                # convert to tensor
    transforms.Normalize((0.5,), (0.5,))  # normalize
])

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

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

train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)

# ------------------------------------------------------------
# 3. Define Simple Neural Network
# ------------------------------------------------------------
class SimpleNN(nn.Module):
    def __init__(self):
        super(SimpleNN, self).__init__()
        self.fc1 = nn.Linear(28*28, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, 10)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = x.view(-1, 784)    # flatten image
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.fc3(x)        # no softmax (CrossEntropyLoss handles it)
        return x

model = SimpleNN().to(device)

# ------------------------------------------------------------
# 4. Loss and Optimizer
# ------------------------------------------------------------
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# ------------------------------------------------------------
# 5. Training Loop
# ------------------------------------------------------------
num_epochs = 5
train_loss_list = []
train_acc_list = []

for epoch in range(num_epochs):
    total_loss = 0
    correct = 0
    total = 0

    for images, labels in train_loader:
        images, labels = images.to(device), labels.to(device)

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

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

        total_loss += loss.item()

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

    avg_loss = total_loss / len(train_loader)
    accuracy = 100 * correct / total

    train_loss_list.append(avg_loss)
    train_acc_list.append(accuracy)

    print(f"Epoch [{epoch+1}/{num_epochs}]  Loss: {avg_loss:.4f}  Accuracy: {accuracy:.2f}%")

# ------------------------------------------------------------
# 6. Testing / Evaluation
# ------------------------------------------------------------
model.eval()
correct = 0
total = 0

with torch.no_grad():
    for images, labels in test_loader:
        images, labels = images.to(device), labels.to(device)
        outputs = model(images)

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

test_accuracy = 100 * correct / total
print("\nTest Accuracy:", test_accuracy)

# ------------------------------------------------------------
# 7. Plot Accuracy and Loss Graphs
# ------------------------------------------------------------
plt.figure(figsize=(8, 4))
plt.plot(train_acc_list, label="Training Accuracy")
plt.title("Training Accuracy")
plt.xlabel("Epoch")
plt.ylabel("Accuracy (%)")
plt.grid()
plt.legend()
plt.show()

plt.figure(figsize=(8, 4))
plt.plot(train_loss_list, label="Training Loss")
plt.title("Training Loss")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.grid()
plt.legend()
plt.show()

# ------------------------------------------------------------
# 8. Show Predictions for First 5 Test Images
# ------------------------------------------------------------
data_iter = iter(test_loader)
images, labels = next(data_iter)
images, labels = images.to(device), labels.to(device)

outputs = model(images[:5])
_, preds = outputs.max(1)

print("\nPredicted:", preds.cpu().numpy())
print("Actual:   ", labels[:5].cpu().numpy())

# Plot the first 5 images
plt.figure(figsize=(10, 2))
for i in range(5):
    plt.subplot(1, 5, i + 1)
    plt.imshow(images[i].cpu().squeeze(), cmap="gray")
    plt.title(f"P:{preds[i].item()} / T:{labels[i].item()}")
    plt.axis("off")

plt.show()