Delete app.py

app.py

@@ -1,54 +0,0 @@
-
-import gradio as gr
-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.load_state_dict(torch.hub.load_state_dict_from_url('https://huggingface.co/username/mnist-cnn-pytorch/resolve/main/mnist_cnn.pt', 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", shape=(28,28), image_mode="L", label="Draw a digit"),
-    outputs=gr.Label(num_top_classes=10),
-    title="MNIST Digit Classifier (PyTorch)"
-)
-
-iface.launch()