image classifier app

app.py

@@ -1,7 +1,35 @@
-import gradio as gr
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../app.ipynb.
 
-def greet(name):
-    return "Hello " + name + "!"
+# %% auto 0
+__all__ = ['learn', 'categories', 'im_size', 'examples', 'intf', 'classify_image']
 
-iface = gr.Interface(fn=greet, inputs='text', outputs='text')
-iface.launch()
+# %% ../app.ipynb 4
+learn = load_learner('ft_resnet18_10epochs.pkl')
+
+# %% ../app.ipynb 6
+categories = ['city','town','countryside','ocean']
+
+def classify_image(img):
+    pred, idx, probs = learn.predict(img)
+    ret = dict(
+        zip(categories,
+            map(float,probs)
+           )
+    )
+    return ret
+
+# %% ../app.ipynb 8
+im_size = 192
+
+#image = gr.inputs.Image(shape=(im_size,im_size))
+#label = gr.outputs.Label()
+examples = ['images/test/035cd8f1-2c9c-45ac-9d67-e0b80043ef22.jpg',
+            'images/test/b8f604be-44c2-4ad1-badd-f9c2ed0b2b13.jpg',
+            'images/test/92358e88-e4cd-4cd8-87dd-bfa90d0a861e.jpg',
+            'images/test/a9614023-dae6-437e-ae88-4677e9035a1d.jpg']
+
+intf = gr.Interface(fn=classify_image,
+                    inputs="image",
+                    outputs="label",
+                    examples=examples)
+intf.launch(inline=False)