Update app.py

app.py

@@ -1,51 +1,52 @@
-from pathlib import Path
-
-import torch
-import gradio as gr
-from torch import nn
-
-
-LABELS = Path('class_names.txt').read_text().splitlines()
-
-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()
-
-def predict(im):
-    x = torch.tensor(im, 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, 5)
-
-    return {LABELS[i]: v.item() for i, v in zip(indices, values)}
-
-
-interface = gr.Interface(
-    predict, 
-    inputs='sketchpad', 
-    outputs='label', 
-    theme="huggingface", 
-    title="Sketch Recognition", 
-    description="Who wants to play Pictionary? Draw a common object like a shovel or a laptop, and the algorithm will guess in real time!", 
-    article = "<p style='text-align: center'>Sketch Recognition | Demo Model</p>",
-    live=True)
-interface.launch(debug=True)
+from pathlib import Path
+
+import torch
+import gradio as gr
+from torch import nn
+
+
+LABELS = Path('class_names.txt').read_text().splitlines()
+
+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()
+
+def predict(im):
+    x = torch.tensor(im, 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, 5)
+
+    return {LABELS[i]: v.item() for i, v in zip(indices, values)}
+
+sketchpad = gr.Image(label="Sketchpad", source="canvas"),
+
+interface = gr.Interface(
+    predict, 
+    inputs=sketchpad, 
+    outputs='label', 
+    theme="huggingface", 
+    title="Sketch Recognition", 
+    description="Who wants to play Pictionary? Draw a common object like a shovel or a laptop, and the algorithm will guess in real time!", 
+    article = "<p style='text-align: center'>Sketch Recognition | Demo Model</p>",
+    live=True)
+interface.launch(debug=True)