Create Pictionary/app.py

Pictionary/app.py

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+from pathlib import Path
+
+import gradio as gr
+import torch
+from huggingface_hub import hf_hub_download
+from torch import nn
+
+LABELS = Path(hf_hub_download('nateraw/quickdraw', '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)),
+)
+weights_file = hf_hub_download('nateraw/quickdraw', 'pytorch_model.bin')
+state_dict = torch.load(weights_file, 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) / 255.0
+
+    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', live=True)
+
+if __name__ == '__main__':
+    interface.launch(debug=True)