import torch
import torch.nn as nn
import torch.nn.functional as F
from safetensors.torch import load_file
from PIL import Image
from torchvision import transforms
import string
torch.set_num_threads(20)

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1,64,kernel_size=3,padding=1)       
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)       
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1)   
        self.conv4 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
        self.conv5 = nn.Conv2d(512, 1024, kernel_size=3, padding=1)
        self.fc1 = nn.Linear(1024*8*8, 512)
        self.fc2 = nn.Linear(512, 256)
        self.fc3 = nn.Linear(256, 128)
        self.fc4 = nn.Linear(128, 26)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x,2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.conv3(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.conv4(x))
        x = F.relu(self.conv5(x))
        x = x.view(x.size(0), -1)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = F.relu(self.fc3(x))
        x = self.fc4(x)
        return x
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = CNN().to(device)
weights_dict = load_file("cnn_letters.safetensors")
model.load_state_dict(weights_dict)
model.eval()

#using

from PIL import Image
from torchvision import transforms
#get you image 
img = Image.open("my_letter.png").convert("L")

transform = transforms.Compose([
    transforms.Resize((64,64)),
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

x = transform(img).unsqueeze(0).to(device)

with torch.no_grad():
    output = model(x)
    pred_idx = output.argmax(dim=1).item()
    
letters = list(string.ascii_uppercase)
pred_letter = letters[pred_idx]
print(f"Predicted class: {pred_idx + 1}, Letter: {pred_letter}")