switch to using blocks

app.ipynb

@@ -20,6 +20,9 @@
     "#| export\n",
     "from fastai.vision.all import *\n",
     "import gradio as gr\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
     "title = \"FastAI - Big Cats Classifier\"\n",
     "description = \"Classify big cats using all Resnet models available pre-trained in FastAI\""
    ]
@@ -41,7 +44,7 @@
     "}\n",
     "models = list(learners.keys())\n",
     "\n",
-    "    "
+    "active_model = learners[\"resnet-18\"]\n"
    ]
   },
   {
@@ -53,11 +56,16 @@
    "source": [
     "#| export\n",
     "    \n",
-    "def classify_image(img, model_file=\"resnet-101\"):\n",
-    "    learn = load_learner(learners[model_file])\n",
+    "def classify_image(img):\n",
+    "    learn = load_learner(active_model)\n",
     "    pred,idx,probs = learn.predict(img)\n",
-    "    print(pred, idx, probs)\n",
-    "    return dict(zip(learn.dls.vocab, map(float, probs)))\n"
+    "    return dict(zip(learn.dls.vocab, map(float, probs)))\n",
+    "\n",
+    "def select_model(model_name):\n",
+    "    if model_name not in models:\n",
+    "        model_name = \"resnet-18\"\n",
+    "    active_model = learners[model_name]\n",
+    "    return model_name\n"
    ]
   },
   {
@@ -107,9 +115,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "cheetah TensorBase(1) TensorBase([2.9325e-08, 9.9999e-01, 1.2872e-09, 1.3284e-05, 3.6218e-08,\n",
-      "            6.6378e-07, 1.2428e-08, 7.0062e-09])\n",
-      "{'african leopard': 2.932508635922204e-08, 'cheetah': 0.9999860525131226, 'clouded leopard': 1.2872064525382143e-09, 'cougar': 1.3283532098284923e-05, 'jaguar': 3.6217517873637917e-08, 'lion': 6.637808382947696e-07, 'snow leopard': 1.242834812842375e-08, 'tiger': 7.0062102786039304e-09}\n"
+      "{'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"
      ]
     },
     {
@@ -153,9 +159,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "jaguar TensorBase(4) TensorBase([2.2414e-06, 4.8124e-07, 1.5911e-08, 1.5741e-08, 1.0000e+00,\n",
-      "            8.4150e-10, 2.4537e-08, 4.5623e-07])\n",
-      "{'african leopard': 2.241393531221547e-06, 'cheetah': 4.812366114492761e-07, 'clouded leopard': 1.5911437500903958e-08, 'cougar': 1.5740527103957902e-08, 'jaguar': 0.9999967813491821, 'lion': 8.415030339214979e-10, 'snow leopard': 2.453731973162121e-08, 'tiger': 4.562308788536029e-07}\n"
+      "{'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"
      ]
     },
     {
@@ -199,9 +203,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "tiger TensorBase(7) TensorBase([2.0140e-08, 3.2289e-10, 3.0278e-07, 1.7037e-07, 2.8471e-08,\n",
-      "            3.1560e-08, 5.5170e-08, 1.0000e+00])\n",
-      "{'african leopard': 2.0139752976433556e-08, 'cheetah': 3.228871059413052e-10, 'clouded leopard': 3.0278118856585934e-07, 'cougar': 1.7037031341260445e-07, 'jaguar': 2.8470973134631095e-08, 'lion': 3.15602726175257e-08, 'snow leopard': 5.5169955714973185e-08, 'tiger': 0.9999994039535522}\n"
+      "{'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"
      ]
     },
     {
@@ -245,9 +247,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "cougar TensorBase(3) TensorBase([7.7202e-04, 9.6453e-05, 3.6239e-04, 9.9550e-01, 5.8073e-04,\n",
-      "            1.0296e-03, 1.6978e-04, 1.4883e-03])\n",
-      "{'african leopard': 0.0007720203138887882, 'cheetah': 9.645262616686523e-05, 'clouded leopard': 0.00036238841130398214, 'cougar': 0.9955006241798401, 'jaguar': 0.0005807342822663486, 'lion': 0.0010295877000316978, 'snow leopard': 0.000169777573319152, 'tiger': 0.0014882636023685336}\n"
+      "{'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"
      ]
     },
     {
@@ -291,9 +291,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "lion TensorBase(5) TensorBase([6.3666e-10, 2.1585e-07, 6.5407e-09, 1.1020e-08, 1.3697e-08,\n",
-      "            9.9998e-01, 5.2166e-09, 1.6965e-05])\n",
-      "{'african leopard': 6.366598359619502e-10, 'cheetah': 2.1584540377261874e-07, 'clouded leopard': 6.540694652557022e-09, 'cougar': 1.1020346413204152e-08, 'jaguar': 1.3696873857327319e-08, 'lion': 0.9999828338623047, 'snow leopard': 5.2166360120509125e-09, 'tiger': 1.696465005807113e-05}\n"
+      "{'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"
      ]
     },
     {
@@ -337,9 +335,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "african leopard TensorBase(0) TensorBase([9.7809e-01, 1.9370e-03, 5.1859e-04, 1.8196e-05, 1.5251e-02,\n",
-      "            1.8402e-04, 3.8208e-03, 1.8130e-04])\n",
-      "{'african leopard': 0.9780895113945007, 'cheetah': 0.0019370485097169876, 'clouded leopard': 0.0005185850313864648, 'cougar': 1.819587851059623e-05, 'jaguar': 0.015250639989972115, 'lion': 0.00018402353452984244, 'snow leopard': 0.0038208006881177425, 'tiger': 0.00018130325770471245}\n"
+      "{'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"
      ]
     },
     {
@@ -383,9 +379,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "clouded leopard TensorBase(2) TensorBase([3.5035e-05, 2.8548e-06, 9.9938e-01, 1.8297e-06, 5.6521e-04,\n",
-      "            1.3141e-06, 7.5178e-06, 1.0570e-05])\n",
-      "{'african leopard': 3.5035314795095474e-05, 'cheetah': 2.8547888177854475e-06, 'clouded leopard': 0.9993757605552673, 'cougar': 1.8296907455805922e-06, 'jaguar': 0.0005652108229696751, 'lion': 1.314112978434423e-06, 'snow leopard': 7.517839094361989e-06, 'tiger': 1.0569940059212968e-05}\n"
+      "{'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"
      ]
     },
     {
@@ -429,9 +423,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "snow leopard TensorBase(6) TensorBase([1.9796e-07, 5.2659e-07, 1.7047e-04, 2.0246e-07, 1.5801e-08,\n",
-      "            5.4288e-06, 9.9982e-01, 6.8012e-09])\n",
-      "{'african leopard': 1.9796296157892357e-07, 'cheetah': 5.265908384899376e-07, 'clouded leopard': 0.00017047168512362987, 'cougar': 2.024643492859468e-07, 'jaguar': 1.5801049357833108e-08, 'lion': 5.4287702369038016e-06, 'snow leopard': 0.9998231530189514, 'tiger': 6.801158747293812e-09}\n"
+      "{'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"
      ]
     }
    ],
@@ -446,124 +438,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 19,
    "id": "a48e7483-c04b-4048-a1ae-34a8c7986a57",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/ajithj/Library/Python/3.8/lib/python/site-packages/gradio/inputs.py:256: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
-      "  warnings.warn(\n",
-      "/Users/ajithj/Library/Python/3.8/lib/python/site-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
-      "  warnings.warn(value)\n",
-      "/Users/ajithj/Library/Python/3.8/lib/python/site-packages/gradio/inputs.py:216: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
-      "  warnings.warn(\n",
-      "/Users/ajithj/Library/Python/3.8/lib/python/site-packages/gradio/outputs.py:196: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
-      "  warnings.warn(\n",
-      "/Users/ajithj/Library/Python/3.8/lib/python/site-packages/gradio/deprecation.py:40: UserWarning: The 'type' parameter has been deprecated. Use the Number component instead.\n",
-      "  warnings.warn(value)\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Running on local URL:  http://127.0.0.1:7860\n",
-      "Running on public URL: https://9569b03a-5208-4edb.gradio.live\n",
       "\n",
-      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<style>\n",
-       "    /* Turns off some styling */\n",
-       "    progress {\n",
-       "        /* gets rid of default border in Firefox and Opera. */\n",
-       "        border: none;\n",
-       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
-       "        background-size: auto;\n",
-       "    }\n",
-       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
-       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
-       "    }\n",
-       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-       "        background: #F44336;\n",
-       "    }\n",
-       "</style>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "snow leopard TensorBase(6) TensorBase([1.9796e-07, 5.2659e-07, 1.7047e-04, 2.0246e-07, 1.5801e-08,\n",
-      "            5.4288e-06, 9.9982e-01, 6.8012e-09])\n"
+      "To create a public link, set `share=True` in `launch()`.\n"
      ]
     },
     {
-     "data": {
-      "text/html": [
-       "\n",
-       "<style>\n",
-       "    /* Turns off some styling */\n",
-       "    progress {\n",
-       "        /* gets rid of default border in Firefox and Opera. */\n",
-       "        border: none;\n",
-       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
-       "        background-size: auto;\n",
-       "    }\n",
-       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
-       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
-       "    }\n",
-       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-       "        background: #F44336;\n",
-       "    }\n",
-       "</style>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "african leopard TensorBase(0) TensorBase([9.7809e-01, 1.9370e-03, 5.1859e-04, 1.8196e-05, 1.5251e-02,\n",
-      "            1.8402e-04, 3.8208e-03, 1.8130e-04])\n"
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/ajithj/Library/Python/3.8/lib/python/site-packages/gradio/routes.py\", line 321, in run_predict\n",
+      "    output = await app.blocks.process_api(\n",
+      "  File \"/Users/ajithj/Library/Python/3.8/lib/python/site-packages/gradio/blocks.py\", line 1015, in process_api\n",
+      "    result = await self.call_function(fn_index, inputs, iterator, request)\n",
+      "  File \"/Users/ajithj/Library/Python/3.8/lib/python/site-packages/gradio/blocks.py\", line 856, in call_function\n",
+      "    prediction = await anyio.to_thread.run_sync(\n",
+      "  File \"/Users/ajithj/Library/Python/3.8/lib/python/site-packages/anyio/to_thread.py\", line 31, in run_sync\n",
+      "    return await get_asynclib().run_sync_in_worker_thread(\n",
+      "  File \"/Users/ajithj/Library/Python/3.8/lib/python/site-packages/anyio/_backends/_asyncio.py\", line 937, in run_sync_in_worker_thread\n",
+      "    return await future\n",
+      "  File \"/Users/ajithj/Library/Python/3.8/lib/python/site-packages/anyio/_backends/_asyncio.py\", line 867, in run\n",
+      "    result = context.run(func, *args)\n",
+      "  File \"/var/folders/jk/w8lkkz7n40s81208_5_qd5_80000gn/T/ipykernel_3681/233086315.py\", line 5, in classify_image\n",
+      "    pred,idx,probs = learn.predict(img)\n",
+      "  File \"/Users/ajithj/Library/Python/3.8/lib/python/site-packages/fastai/learner.py\", line 312, in predict\n",
+      "    dl = self.dls.test_dl([item], rm_type_tfms=rm_type_tfms, num_workers=0)\n",
+      "  File \"/Users/ajithj/Library/Python/3.8/lib/python/site-packages/fastai/data/core.py\", line 532, in test_dl\n",
+      "    test_ds = test_set(self.valid_ds, test_items, rm_tfms=rm_type_tfms, with_labels=with_labels\n",
+      "  File \"/Users/ajithj/Library/Python/3.8/lib/python/site-packages/fastai/data/core.py\", line 511, in test_set\n",
+      "    if rm_tfms is None: rm_tfms = [tl.infer_idx(get_first(test_items)) for tl in test_tls]\n",
+      "  File \"/Users/ajithj/Library/Python/3.8/lib/python/site-packages/fastai/data/core.py\", line 511, in <listcomp>\n",
+      "    if rm_tfms is None: rm_tfms = [tl.infer_idx(get_first(test_items)) for tl in test_tls]\n",
+      "  File \"/Users/ajithj/Library/Python/3.8/lib/python/site-packages/fastai/data/core.py\", line 405, in infer_idx\n",
+      "    assert idx < len(self.types), f\"Expected an input of type in \\n{pretty_types}\\n but got {type(x)}\"\n",
+      "AssertionError: Expected an input of type in \n",
+      "  - <class 'pathlib.PosixPath'>\n",
+      "  - <class 'pathlib.Path'>\n",
+      "  - <class 'str'>\n",
+      "  - <class 'torch.Tensor'>\n",
+      "  - <class 'numpy.ndarray'>\n",
+      "  - <class 'bytes'>\n",
+      "  - <class 'fastai.vision.core.PILImage'>\n",
+      " but got <class 'NoneType'>\n"
      ]
     },
     {
@@ -603,14 +528,6 @@
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tiger TensorBase(7) TensorBase([2.0140e-08, 3.2289e-10, 3.0278e-07, 1.7037e-07, 2.8471e-08,\n",
-      "            3.1560e-08, 5.5170e-08, 1.0000e+00])\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -648,14 +565,6 @@
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "cheetah TensorBase(1) TensorBase([2.9325e-08, 9.9999e-01, 1.2872e-09, 1.3284e-05, 3.6218e-08,\n",
-      "            6.6378e-07, 1.2428e-08, 7.0062e-09])\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -697,84 +606,47 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "clouded leopard TensorBase(2) TensorBase([3.5035e-05, 2.8548e-06, 9.9938e-01, 1.8297e-06, 5.6521e-04,\n",
-      "            1.3141e-06, 7.5178e-06, 1.0570e-05])\n"
+      "Keyboard interruption in main thread... closing server.\n"
      ]
     },
     {
      "data": {
-      "text/html": [
-       "\n",
-       "<style>\n",
-       "    /* Turns off some styling */\n",
-       "    progress {\n",
-       "        /* gets rid of default border in Firefox and Opera. */\n",
-       "        border: none;\n",
-       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
-       "        background-size: auto;\n",
-       "    }\n",
-       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
-       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
-       "    }\n",
-       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-       "        background: #F44336;\n",
-       "    }\n",
-       "</style>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
+      "text/plain": []
      },
+     "execution_count": 19,
      "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "cougar TensorBase(3) TensorBase([7.7202e-04, 9.6453e-05, 3.6239e-04, 9.9550e-01, 5.8073e-04,\n",
-      "            1.0296e-03, 1.6978e-04, 1.4883e-03])\n",
-      "Keyboard interruption in main thread... closing server.\n",
-      "Killing tunnel 127.0.0.1:7860 <> https://9569b03a-5208-4edb.gradio.live\n"
-     ]
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "#| export\n",
-    "image = gr.inputs.Image()\n",
-    "model = gr.inputs.Dropdown(choices=models)\n",
-    "label = gr.outputs.Label()\n",
     "example_images = [ 'cheetah.jpg', 'jaguar.jpg', 'tiger.jpg', 'cougar.jpg', 'lion.jpg', 'african leopard.jpg', 'clouded leopard.jpg', 'snow leopard.jpg' ]\n",
-    "example_models = [] #list(learners.values())\n",
-    "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=example_images, title=title, description=description )\n",
-    "if __name__ == \"__main__\":\n",
-    "    intf.launch(debug=True, inline=False)\n"
+    "\n",
+    "demo = gr.Blocks()\n",
+    "with demo:\n",
+    "    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",
+    "        model.change(fn=select_model, inputs=model, outputs=None)\n",
+    "        btnClassify = gr.Button(\"Classify\")\n",
+    "    with gr.Column(variant=\"panel\"):\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",
+    "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",
+    "    # if __name__ == \"__main__\":\n",
+    "    #     intf.launch(debug=True, inline=False)\n"
    ]
   },
   {
    "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

@@ -1,12 +1,15 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
 
 # %% auto 0
-__all__ = ['title', 'description', 'learners', 'models', 'image', 'model', 'label', 'example_images', 'example_models', 'intf',
-           'classify_image']
+__all__ = ['title', 'description', 'learners', 'models', 'active_model', 'example_images', 'demo', 'classify_image',
+           'select_model']
 
 # %% app.ipynb 1
 from fastai.vision.all import *
 import gradio as gr
+import warnings
+warnings.filterwarnings('ignore')
+
 title = "FastAI - Big Cats Classifier"
 description = "Classify big cats using all Resnet models available pre-trained in FastAI"
 
@@ -20,23 +23,40 @@ learners = {
 }
 models = list(learners.keys())
 
-    
+active_model = learners["resnet-18"]
+
 
 # %% app.ipynb 3
-def classify_image(img, model_file="resnet-101"):
-    learn = load_learner(learners[model_file])
+def classify_image(img):
+    learn = load_learner(active_model)
     pred,idx,probs = learn.predict(img)
-    print(pred, idx, probs)
     return dict(zip(learn.dls.vocab, map(float, probs)))
 
+def select_model(model_name):
+    if model_name not in models:
+        model_name = "resnet-18"
+    active_model = learners[model_name]
+    return model_name
+
 
 # %% app.ipynb 5
-image = gr.inputs.Image()
-model = gr.inputs.Dropdown(choices=models)
-label = gr.outputs.Label()
 example_images = [ 'cheetah.jpg', 'jaguar.jpg', 'tiger.jpg', 'cougar.jpg', 'lion.jpg', 'african leopard.jpg', 'clouded leopard.jpg', 'snow leopard.jpg' ]
-example_models = [] #list(learners.values())
-intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=example_images, title=title, description=description )
-if __name__ == "__main__":
-    intf.launch(debug=True, inline=False)
+
+demo = gr.Blocks()
+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)
+        model.change(fn=select_model, inputs=model, outputs=None)
+        btnClassify = gr.Button("Classify")
+    with gr.Column(variant="panel"):
+        result = gr.outputs.Label(label="Result")
+        
+    btnClassify.click(fn=classify_image, inputs=image, outputs=result)
+    img_gallery = gr.Examples(examples=example_images, inputs=image)
+
+demo.launch(debug=True, inline=False)
+    # intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=example_images, title=title, description=description )
+    # if __name__ == "__main__":
+    #     intf.launch(debug=True, inline=False)