Create app.py

app.py

@@ -0,0 +1,55 @@
+from pathlib import Path
+
+import torch
+import gradio as gr
+from torch import nn
+from PIL import Image
+import numpy as np
+
+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_dict):
+    im_raw = im_dict['composite'][:,:,3]
+    img = Image.fromarray(im_raw)
+    img_small = img.resize([24,28],resample=0)
+    im = np.array(np.uint8(img_small))
+    im = np.transpose(im,(1,0))
+    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',
+    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)