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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()
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