dowload.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from safetensors.torch import load_file

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 16, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
        self.fc1 = nn.Linear(32*7*7, 64)
        self.fc2 = nn.Linear(64, 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 = x.view(x.size(0), -1)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

model = CNN()
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((28,28)),
#    transforms.ToTensor(),
#    transforms.Normalize((0.5,), (0.5,))
#])

#x = transform(img).unsqueeze(0)

#with torch.no_grad():
#    output = model(x)
#    pred = output.argmax(dim=1)
#print(f"Predicted class: {pred.item() + 1}")