Put model in app file

app.py

@@ -1,7 +1,25 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../testing.ipynb.
+
+# %% auto 0
+__all__ = ['learn', 'categories', 'examples', 'intf', 'classify_img']
+
+# %% ../testing.ipynb 1
+from fastai.vision.all import *
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+# %% ../testing.ipynb 3
+learn = load_learner("model.pkl")
+
+# %% ../testing.ipynb 5
+categories = ('American Robin', 'Downy Woodpecker', 'Great Blue Heron',  'Hairy Woodpecker', 'House Sparrow')
+def classify_img(img):
+  pred,idx,probs = learn.predict(img)
+  return dict(zip(categories, map(float,probs)))
+
+# %% ../testing.ipynb 7
+# image = gr.Image(shape=(256,256))
+# label = gr.Label()
+examples = ['american_robin.jpeg', 'downy_woodpecker.jpeg', 'great_blue_heron.jpeg', 'hairy_woodpecker.jpeg', 'house_sparrow.jpeg']
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+intf = gr.Interface(fn=classify_img, inputs='image', outputs='label', examples=examples)
+intf.launch(inline=False)

testing.ipynb

@@ -571,12 +571,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from nbdev.export import notebook2script\n"
+    "import nbdev"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Export successful\n"
+     ]
+    }
+   ],
+   "source": [
+    "nbdev.export.nb_export('testing.ipynb', 'app')\n",
+    "print('Export successful')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {