add a clear btn

app.ipynb

@@ -20,8 +20,9 @@
     "#| export\n",
     "from fastai.vision.all import *\n",
     "import gradio as gr\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "interpretation='default'\n",
+    "enable_queue=True\n",
     "\n",
     "title = \"FastAI - Big Cats Classifier\"\n",
     "description = \"Classify big cats using all Resnet models available pre-trained in FastAI\""
@@ -124,7 +125,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'african leopard': 0.0005852991016581655, 'cheetah': 0.9993988275527954, 'clouded leopard': 1.7600793000838166e-07, 'cougar': 6.112059963925276e-06, 'jaguar': 7.491902579204179e-06, 'lion': 1.3097942428430542e-06, 'snow leopard': 6.794325599912554e-07, 'tiger': 1.22832446436405e-07}\n"
+      "{'african leopard': 0.00045245178625918925, 'cheetah': 0.9994743466377258, 'clouded leopard': 3.061432778395101e-07, 'cougar': 8.726581654627807e-06, 'jaguar': 4.878858817392029e-05, 'lion': 1.4129628652881365e-05, 'snow leopard': 1.2738197483486147e-06, 'tiger': 1.1983513736879559e-08}\n"
      ]
     },
     {
@@ -168,7 +169,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'african leopard': 0.2962114214897156, 'cheetah': 2.706606210267637e-05, 'clouded leopard': 0.0008470952161587775, 'cougar': 1.0193979505856987e-05, 'jaguar': 0.701975405216217, 'lion': 1.3766093616141006e-05, 'snow leopard': 0.0008549779886379838, 'tiger': 6.007726915413514e-05}\n"
+      "{'african leopard': 8.918660228118824e-07, 'cheetah': 3.004239079729132e-09, 'clouded leopard': 1.0275688282490592e-06, 'cougar': 1.8215871477877954e-08, 'jaguar': 0.9999979734420776, 'lion': 7.327587425720594e-10, 'snow leopard': 1.3988608316140017e-07, 'tiger': 4.418302523845341e-08}\n"
      ]
     },
     {
@@ -212,7 +213,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'african leopard': 2.0210626061611947e-08, 'cheetah': 1.6748231246310752e-08, 'clouded leopard': 1.1174745395692298e-06, 'cougar': 2.63490710494807e-06, 'jaguar': 2.399448703727103e-06, 'lion': 6.196571433747522e-08, 'snow leopard': 2.4245096028607804e-06, 'tiger': 0.9999912977218628}\n"
+      "{'african leopard': 1.279351291572084e-08, 'cheetah': 3.040315732505405e-08, 'clouded leopard': 4.387358387702989e-08, 'cougar': 1.2642824458453106e-06, 'jaguar': 3.0061545430726255e-07, 'lion': 2.5054502472698914e-08, 'snow leopard': 4.821659516096588e-08, 'tiger': 0.9999983310699463}\n"
      ]
     },
     {
@@ -256,7 +257,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'african leopard': 9.39465026021935e-05, 'cheetah': 0.00021114452101755887, 'clouded leopard': 8.688175876159221e-05, 'cougar': 0.9761292934417725, 'jaguar': 7.082346655806759e-06, 'lion': 0.02333180606365204, 'snow leopard': 0.00011577722762012854, 'tiger': 2.4006889361771755e-05}\n"
+      "{'african leopard': 2.2317146886052797e-06, 'cheetah': 6.153353297122521e-06, 'clouded leopard': 3.5761433991865488e-06, 'cougar': 0.9940788745880127, 'jaguar': 7.271950153153739e-08, 'lion': 0.005906379781663418, 'snow leopard': 1.0360908220263809e-07, 'tiger': 2.569006483099656e-06}\n"
      ]
     },
     {
@@ -300,7 +301,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'african leopard': 1.3545766286426897e-08, 'cheetah': 2.635677674334147e-06, 'clouded leopard': 7.659965994832874e-09, 'cougar': 9.957815017003213e-09, 'jaguar': 1.497639772196635e-07, 'lion': 0.9999957084655762, 'snow leopard': 1.294516778216348e-07, 'tiger': 1.2779944427165901e-06}\n"
+      "{'african leopard': 7.383512135028525e-10, 'cheetah': 1.6924343526625307e-06, 'clouded leopard': 3.8847122740826023e-10, 'cougar': 1.4941306858418102e-08, 'jaguar': 3.277633942033731e-09, 'lion': 0.9999983310699463, 'snow leopard': 4.2623696572263725e-08, 'tiger': 5.7686470711360016e-08}\n"
      ]
     },
     {
@@ -344,7 +345,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'african leopard': 0.024091463536024094, 'cheetah': 0.0014163728337734938, 'clouded leopard': 0.008692733943462372, 'cougar': 0.0010448594111949205, 'jaguar': 0.7156786322593689, 'lion': 0.017859801650047302, 'snow leopard': 0.22819218039512634, 'tiger': 0.0030239589978009462}\n"
+      "{'african leopard': 0.11080536246299744, 'cheetah': 0.00025237080990336835, 'clouded leopard': 0.0003655211767181754, 'cougar': 1.1126862773380708e-05, 'jaguar': 0.8603838086128235, 'lion': 8.311066630994901e-05, 'snow leopard': 0.028046416118741035, 'tiger': 5.234780110185966e-05}\n"
      ]
     },
     {
@@ -388,7 +389,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'african leopard': 7.144178198359441e-06, 'cheetah': 3.725538704202336e-07, 'clouded leopard': 0.9994736313819885, 'cougar': 6.0378228226909414e-05, 'jaguar': 3.279747033957392e-05, 'lion': 1.1806019273308266e-07, 'snow leopard': 0.0003000575816258788, 'tiger': 0.0001255277602467686}\n"
+      "{'african leopard': 5.991949336703328e-08, 'cheetah': 1.2888077272066312e-08, 'clouded leopard': 0.9999984502792358, 'cougar': 7.355600928349304e-07, 'jaguar': 5.131531679580803e-07, 'lion': 5.543293823961903e-09, 'snow leopard': 3.404375448212704e-08, 'tiger': 2.0324510785485472e-07}\n"
      ]
     },
     {
@@ -432,7 +433,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'african leopard': 2.8642458346439525e-05, 'cheetah': 0.00017579919949639589, 'clouded leopard': 0.08972200006246567, 'cougar': 7.897598698036745e-05, 'jaguar': 2.5307128453277983e-05, 'lion': 1.8576161892269738e-05, 'snow leopard': 0.9099361896514893, 'tiger': 1.4485961401078384e-05}\n"
+      "{'african leopard': 2.2017589799361303e-05, 'cheetah': 9.802879503695294e-05, 'clouded leopard': 0.0109814228489995, 'cougar': 1.8166520021623e-06, 'jaguar': 5.0095695769414306e-06, 'lion': 5.28784084963263e-06, 'snow leopard': 0.988881528377533, 'tiger': 4.889693173026899e-06}\n"
      ]
     }
    ],
@@ -447,7 +448,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "a48e7483-c04b-4048-a1ae-34a8c7986a57",
    "metadata": {},
    "outputs": [
@@ -496,21 +497,6 @@
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Keyboard interruption in main thread... closing server.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -522,7 +508,9 @@
     "    with gr.Column(variant=\"panel\"):\n",
     "        image = gr.inputs.Image(label=\"Pick an image\")\n",
     "        model = gr.inputs.Dropdown(label=\"Select a model\", choices=models)\n",
-    "        btnClassify = gr.Button(\"Classify\")\n",
+    "        with gr.Row(equal_height=True):\n",
+    "            btnClassify = gr.Button(\"Classify\")\n",
+    "            btnClear = gr.Button(\"Clear\")\n",
     "    with gr.Column(variant=\"panel\"):\n",
     "        selected = gr.outputs.Textbox(label=\"Active Model\")\n",
     "        with gr.Row(equal_height=True):\n",
@@ -530,13 +518,14 @@
     "            losses=gr.outputs.Image(type='filepath', label=\"Top Losses\")\n",
     "        result = gr.outputs.Label(label=\"Result\")\n",
     "        \n",
-    "    btnClassify.click(fn=classify_image, inputs=image, outputs=result)\n",
     "    img_gallery = gr.Examples(examples=example_images, inputs=image)\n",
     "\n",
-    "    # Register all ev\n",
+    "    # Register all event listeners\n",
     "    model.change(fn=select_model, inputs=model, outputs=selected)\n",
     "    model.change(fn=update_matrix, outputs=matrix)\n",
     "    model.change(fn=update_losses, outputs=losses)\n",
+    "    btnClassify.click(fn=classify_image, inputs=image, outputs=result)\n",
+    "    btnClear.click(fn=lambda: gr.Image.update(value=None), inputs=None, outputs=None)\n",
     "\n",
     "demo.launch(debug=True, inline=False)\n",
     "    # intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=example_images, title=title, description=description )\n",
@@ -546,18 +535,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "cab071f9-7c3b-4b35-a0d1-3687731ffce5",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Export successful\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import nbdev\n",
     "nbdev.export.nb_export('app.ipynb', './')\n",

app.py

@@ -1,14 +1,15 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
 
 # %% auto 0
-__all__ = ['title', 'description', 'learners', 'models', 'active_name', 'active_model', 'example_images', 'demo',
-           'classify_image', 'select_model', 'update_matrix', 'update_losses']
+__all__ = ['interpretation', 'enable_queue', 'title', 'description', 'learners', 'models', 'active_name', 'active_model',
+           'example_images', 'demo', 'classify_image', 'select_model', 'update_matrix', 'update_losses']
 
 # %% app.ipynb 1
 from fastai.vision.all import *
 import gradio as gr
-import warnings
-warnings.filterwarnings('ignore')
+
+interpretation='default'
+enable_queue=True
 
 title = "FastAI - Big Cats Classifier"
 description = "Classify big cats using all Resnet models available pre-trained in FastAI"
@@ -55,7 +56,9 @@ with demo:
     with gr.Column(variant="panel"):
         image = gr.inputs.Image(label="Pick an image")
         model = gr.inputs.Dropdown(label="Select a model", choices=models)
-        btnClassify = gr.Button("Classify")
+        with gr.Row(equal_height=True):
+            btnClassify = gr.Button("Classify")
+            btnClear = gr.Button("Clear")
     with gr.Column(variant="panel"):
         selected = gr.outputs.Textbox(label="Active Model")
         with gr.Row(equal_height=True):
@@ -63,13 +66,14 @@ with demo:
             losses=gr.outputs.Image(type='filepath', label="Top Losses")
         result = gr.outputs.Label(label="Result")
         
-    btnClassify.click(fn=classify_image, inputs=image, outputs=result)
     img_gallery = gr.Examples(examples=example_images, inputs=image)
 
-    # Register all ev
+    # Register all event listeners
     model.change(fn=select_model, inputs=model, outputs=selected)
     model.change(fn=update_matrix, outputs=matrix)
     model.change(fn=update_losses, outputs=losses)
+    btnClassify.click(fn=classify_image, inputs=image, outputs=result)
+    btnClear.click(fn=lambda: gr.Image.update(value=None), inputs=None, outputs=None)
 
 demo.launch(debug=True, inline=False)
     # intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=example_images, title=title, description=description )