hugging

.ipynb_checkpoints/app-checkpoint.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
+
+# %% auto 0
+__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'inter', 'classify_image']
+
+# %% app.ipynb 3
+from fastai.vision.all import *
+import gradio as gr
+
+# %% app.ipynb 5
+learn = load_learner('model.pkl')
+
+# %% app.ipynb 7
+categories = ('bicycle', 'car', 'plane', 'train')
+
+def classify_image(img):
+    preds, idx, probs = learn.predict(img)
+    return dict(zip(categories, map(float, probs)))
+
+# %% app.ipynb 9
+image = gr.inputs.Image(shape=(192, 192))
+label = gr.outputs.Label()
+examples = ['car.jpg', 'plane.jpg']
+inter = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+inter.launch(inline=False, share=True)

.ipynb_checkpoints/requirements-checkpoint.txt

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+fastai
+gradio

app.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
+
+# %% auto 0
+__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'inter', 'classify_image']
+
+# %% app.ipynb 3
+from fastai.vision.all import *
+import gradio as gr
+
+# %% app.ipynb 5
+learn = load_learner('model.pkl')
+
+# %% app.ipynb 7
+categories = ('bicycle', 'car', 'plane', 'train')
+
+def classify_image(img):
+    preds, idx, probs = learn.predict(img)
+    return dict(zip(categories, map(float, probs)))
+
+# %% app.ipynb 9
+image = gr.inputs.Image(shape=(192, 192))
+label = gr.outputs.Label()
+examples = ['car.jpg', 'plane.jpg']
+inter = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+inter.launch(inline=False, share=True)

requirements.txt

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+fastai
+gradio