initial commit

app.py

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+import gradio as gr
+from fastai.vision.all import *
+from pathlib import Path
+
+
+__all__ = ['load_learner', 'lean', 'greet', 'classify_image',
+           'image', 'label', 'examples', 'interface']
+
+path = Path(__file__).parent
+
+
+def greet(name):
+    return "Hello " + name + "!!"
+
+
+learn_inf = load_learner(path/'plane.pkl')
+# learn_inf.predict('test.png')
+
+
+# print(learn_inf.predict('test.png'))
+# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+# iface.launch()
+
+# categories = ['F-22 Raptor', 'SR-71 Blackbird', 'B-2 Spirit']
+categories = learn_inf.dls.vocab
+
+
+def classify_image(img):
+    pred, pred_idx, probs = learn_inf.predict(img)
+    return {categories[i]: float(probs[i]) for i in range(len(probs))}
+
+
+image = gr.inputs.Image(shape=(224, 224))
+label = gr.outputs.Label(num_top_classes=3)
+
+
+examples = [['b2.jpg'], ['f22.jpg'], ['sr71.jpg']]
+
+interface = gr.Interface(fn=classify_image, inputs=image,
+                         outputs=label, examples=examples)
+interface.launch(inline=False)
+
+
+# print(learn_inf.predict('b2.jpg'))

requirements.txt

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+gradio
+fastai
+pathlib