Flagging

demo.py

@@ -1,7 +1,7 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: model-test.ipynb.
 
 # %% auto 0
-__all__ = ['plt', 'learn', 'categories', 'image', 'label', 'examples', 'iface', 'is_albani', 'classify_image']
+__all__ = ['plt', 'learn', 'categories', 'title', 'image', 'label', 'examples', 'iface', 'is_albani', 'classify_image']
 
 # %% model-test.ipynb 2
 from fastai.vision.all import *
@@ -21,10 +21,23 @@ def classify_image(img):
   pred,idx,probs = learn.predict(img)
   return dict(zip(categories, map(float, probs)))
 
-image = gr.inputs.Image(shape=(192, 192))
-label = gr.outputs.Label()
+title = """
+  ## Er du i tvivl om at den øl du sidder med i hånden lige nu er god? 
+  Tvivl ej 68 års invotation inden for machine learning skal nok fortælle dig om øllen er god eller ej
+"""
+image = gr.Image(shape=(192, 192))
+label = gr.Label()
 examples = ['albani.jpg', 'albani2.jpg', 'heineken.jpg', 'carlsberg.jpg']
 
-iface = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+iface = gr.Interface(
+  fn=classify_image, 
+  inputs=image, 
+  outputs=label, 
+  examples=examples, 
+  description=title, 
+  allow_flagging="manual", 
+  flagging_options=["wrong", "ambiguous"]
+)
+
 iface.launch(inline=False)
 

model-test.ipynb

@@ -75,14 +75,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on local URL:  http://127.0.0.1:7862\n",
+      "Running on local URL:  http://127.0.0.1:7870\n",
       "\n",
       "To create a public link, set `share=True` in `launch()`.\n"
      ]
@@ -91,7 +91,7 @@
      "data": {
       "text/plain": []
      },
-     "execution_count": 26,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -113,11 +113,24 @@
     "  pred,idx,probs = learn.predict(img)\n",
     "  return dict(zip(categories, map(float, probs)))\n",
     "\n",
-    "image = gr.inputs.Image(shape=(192, 192))\n",
-    "label = gr.outputs.Label()\n",
+    "title = \"\"\"\n",
+    "  ## Er du i tvivl om at den øl du sidder med i hånden lige nu er god? \n",
+    "  Tvivl ej 68 års invotation inden for machine learning skal nok fortælle dig om øllen er god eller ej\n",
+    "\"\"\"\n",
+    "image = gr.Image(shape=(192, 192))\n",
+    "label = gr.Label()\n",
     "examples = ['albani.jpg', 'albani2.jpg', 'heineken.jpg', 'carlsberg.jpg']\n",
     "\n",
-    "iface = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
+    "iface = gr.Interface(\n",
+    "  fn=classify_image, \n",
+    "  inputs=image, \n",
+    "  outputs=label, \n",
+    "  examples=examples, \n",
+    "  description=title, \n",
+    "  allow_flagging=\"manual\", \n",
+    "  flagging_options=[\"wrong\", \"ambiguous\"]\n",
+    ")\n",
+    "\n",
     "iface.launch(inline=False)\n"
    ]
   },
@@ -131,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {