Create app.py

app.py

@@ -0,0 +1,39 @@
+import gradio as gr
+import torch
+from torch import nn
+from pathlib import Path
+
+model = nn.Sequential(
+    nn.Conv2d(1, 32, 3, padding='same'),
+    nn.ReLU(),
+    nn.MaxPool2d(2),
+    nn.Conv2d(32, 64, 3, padding='same'),
+    nn.ReLU(),
+    nn.MaxPool2d(2),
+    nn.Conv2d(64, 128, 3, padding='same'),
+    nn.ReLU(),
+    nn.MaxPool2d(2),
+    nn.Flatten(),
+    nn.Linear(1152, 256),
+    nn.ReLU(),
+    nn.Linear(256, len(LABELS)),
+)
+state_dict = torch.load('pytorch_model.bin',    map_location='cpu')
+model.load_state_dict(state_dict, strict=False)
+model.eval()
+
+LABELS = Path('class_names.txt').read_text().splitlines()
+
+def predict(img):
+    x = torch.tensor(img, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255.
+    with torch.no_grad():
+        out = model(x)
+    probabilities = torch.nn.functional.softmax(out[0], dim=0)
+    values, indices = torch.topk(probabilities, 3)
+    confidences = {LABELS[i]: v.item() for i, v in zip(indices, values)}
+    return confidences
+
+gr.Interface(fn=predict, 
+             inputs="sketchpad",
+             outputs="label",
+             live=True).launch(debug=True)