app.py

import gradio as gr
from huggingface_hub import hf_hub_download
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms
from PIL import Image

class MNISTNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout(0.25)
        self.dropout2 = nn.Dropout(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = F.relu(self.fc1(x))
        x = self.dropout2(x)
        x = self.fc2(x)
        return x

model = MNISTNet()
model_file = hf_hub_download(repo_id="Gaimundo/mnist-nn", filename="mnist_cnn.pt")
model.load_state_dict(torch.load(model_file, map_location="cpu"))
model.eval()

transform = transforms.Compose([
    transforms.Grayscale(),
    transforms.Resize((28,28)),
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])

def predict(image):
    image = transform(image).unsqueeze(0)
    with torch.no_grad():
        output = model(image)
        probs = torch.softmax(output, dim=1)[0]
    return {str(i): float(probs[i]) for i in range(10)}

iface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil", image_mode="L", label="Draw a digit"),
    outputs=gr.Label(num_top_classes=10),
    title="MNIST Digit Classifier (PyTorch)"
)

iface.launch()