import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
import pygame
import numpy as np
import cv2

class ANN(nn.Module):
    def __init__(self):
        super(ANN,self).__init__()
        self.fc1 = nn.Linear(28*28,128)
        self.fc2 = nn.Linear(128,128)
        self.fc3 = nn.Linear(128,128)
        self.fc4 = nn.Linear(128,10)
        self.activation = nn.ReLU()
    def forward(self,x):
        x = x.view(-1,28*28)
        x = self.activation(self.fc1(x))
        x = self.activation(self.fc2(x))
        x = self.activation(self.fc3(x))
        x = self.fc4(x)
        return x
# load dataset
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,),(0.5,))]
    )
trainset = torchvision.datasets.MNIST(root='./data',train=True,download=True,transform=transform)
testset = torchvision.datasets.MNIST(root='./data',train=False,download=True,transform=transform)

trainloader = DataLoader(trainset,batch_size=64,shuffle=True)
testloader = DataLoader(testset,batch_size=64,shuffle=False)

model = ANN()
los_fn = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(),lr=0.001)

# training loop
episodes = 10


for epoch in range(episodes):
    running_loss = 0.0
    for images, labels in trainloader:
        optimizer.zero_grad()
        outputs = model(images)
        loss = los_fn(outputs,labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print(f"Epoch {epoch+1}, Loss: {running_loss/len(trainloader)}")

# evaluation
correct = 0
total = 0
model.eval()
with torch.no_grad():
    for images, labels in testloader:
        outputs = model(images)
        _, predicted = torch.max(outputs.data,1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
print(f"Test Accuracy: {100 * correct / total}%")

def draw_digi():
    pygame.init()
    window_size = 280
    display_height = window_size+50
    screen = pygame.display.set_mode((window_size,display_height))
    pygame.display.set_caption("Draw a digit")
    clock = pygame.time.Clock()
    screen.fill((0,0,0))
    drawing = False
    prediction = None

    font = pygame.font.Font(None,36)
    while True:
        # screen.fill((0,0,0))
        for event in pygame.event.get():
            if event.type == pygame.QUIT:
                pygame.quit()
                return
            if event.type == pygame.MOUSEBUTTONDOWN:
                drawing = True
            if event.type == pygame.MOUSEBUTTONUP:
                drawing = False
                prediction = predict_digit(screen)
            if event.type == pygame.KEYDOWN:
                if event.key == pygame.K_c:
                    screen.fill((0,0,0))
                    prediction = None

            if event.type == pygame.MOUSEMOTION and drawing:
                pygame.draw.circle(screen,(255,255,255),event.pos,8)
            
        if prediction is not None:
                text = font.render(f"Prediction: {prediction}",True,(0,255,0))
                screen.blit(text,(10,window_size+10))
            
        pygame.display.flip()
        clock.tick(60)

def process_drawing(screen):
    surface = pygame.surfarray.array3d(screen)
    gray = np.dot(surface[...,:3],[0.2989,0.5870,0.1140])
    gray = np.transpose(gray,(1,0))
    gray = cv2.resize(gray,(28,28),interpolation=cv2.INTER_AREA)
    gray = gray.astype(np.float32)/255.0
    gray = (gray - 0.5)/0.5
    tensor = torch.tensor(gray,dtype=torch.float32).unsqueeze(0).unsqueeze(0)
    return tensor
def predict_digit(screen):
    image = process_drawing(screen)
    if image is None:
        return None
    model.eval()
    with torch.no_grad():
        output = model(image)
        _, predicted = torch.max(output.data,1)
    return predicted.item()

draw_digi()