import torch
from torch import nn
import gradio as gr

class CNN(nn.Module):
    def __init__(self):
        super(CNN,self).__init__()
        
        self.conv1 = nn.Sequential(
            nn.Conv2d(1,16,5,stride=1,padding=2),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2),
        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(16,32,5,1,2),
            nn.ReLU(),
            nn.MaxPool2d(2),
        )
        self.out = nn.Linear(32*7*7,10)
        
    def forward(self,x):
        x=self.conv1(x)
        x=self.conv2(x)
        x = x.view(-1,32*7*7)
        return self.out(x)

model = CNN()
model.load_state_dict(torch.load('mnist2.pkl',map_location=torch.device('cpu')))
model.eval()

def predict(img):
    x = torch.tensor(img, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255.
    with torch.no_grad():
        pred = model(x)[0]
    return int(pred.argmax())


gr.Interface(fn=predict, 
             inputs="sketchpad",
             outputs="label",
              ).launch()