Delete Pictionary

Pictionary/app.py

@@ -1,47 +0,0 @@
-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)