finished app.py

app.ipynb

@@ -381,27 +381,6 @@
     "# *export*"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ImportError",
-     "evalue": "cannot import name 'notebook2script' from 'nbdev.export' (/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/nbdev/export.py)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn [27], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnbdev\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexport\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m notebook2script\n",
-      "\u001b[0;31mImportError\u001b[0m: cannot import name 'notebook2script' from 'nbdev.export' (/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/nbdev/export.py)"
-     ]
-    }
-   ],
-   "source": [
-    "from nbdev.export import notebook2script"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 28,

app.py

@@ -1,7 +1,27 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../app.ipynb.
+
+# %% auto 0
+__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'intf', 'classify_image']
+
+# %% ../app.ipynb 1
+from fastai.vision.all import *
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+# %% ../app.ipynb 7
+from fastcore.all import *
+learn = load_learner('donut_model2.pkl')
+
+# %% ../app.ipynb 9
+categories = ('donut', 'scone')
+
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float, probs)))
+
+# %% ../app.ipynb 11
+image = gr.inputs.Image(shape = (192,192))
+label = gr.outputs.Label()
+examples = ['donut.jpg', 'scone.jpg']
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+intf = gr.Interface(fn = classify_image, inputs = image, outputs = label, examples = examples)
+intf.launch(inline=False, share=True)

app/app.py

@@ -1,27 +0,0 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: ../app.ipynb.
-
-# %% auto 0
-__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'intf', 'classify_image']
-
-# %% ../app.ipynb 1
-from fastai.vision.all import *
-import gradio as gr
-
-# %% ../app.ipynb 7
-from fastcore.all import *
-learn = load_learner('donut_model2.pkl')
-
-# %% ../app.ipynb 9
-categories = ('donut', 'scone')
-
-def classify_image(img):
-    pred,idx,probs = learn.predict(img)
-    return dict(zip(categories, map(float, probs)))
-
-# %% ../app.ipynb 11
-image = gr.inputs.Image(shape = (192,192))
-label = gr.outputs.Label()
-examples = ['donut.jpg', 'scone.jpg']
-
-intf = gr.Interface(fn = classify_image, inputs = image, outputs = label, examples = examples)
-intf.launch(inline=False, share=True)