{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\admin\\anaconda3\\lib\\site-packages\\torchvision\\io\\image.py:13: UserWarning: Failed to load image Python extension: Could not find module 'C:\\Users\\admin\\anaconda3\\Lib\\site-packages\\torchvision\\image.pyd' (or one of its dependencies). Try using the full path with constructor syntax.\n",
      "  warn(f\"Failed to load image Python extension: {e}\")\n"
     ]
    }
   ],
   "source": [
    "from fastai.vision.all import *\n",
    "import gradio as gr\n",
    "\n",
    "def is_cat(x):\n",
    "    return x[0].isupper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pathlib\n",
    "\n",
    "temp = pathlib.PosixPath\n",
    "pathlib.PosixPath = pathlib.WindowsPath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = load_learner('model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PILImage mode=RGB size=192x144"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = PILImage.create('dog.jpg')\n",
    "img.thumbnail((192, 192))\n",
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "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": [
      "CPU times: total: 78.1 ms\n",
      "Wall time: 169 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('False', tensor(0), tensor([1.0000e+00, 1.6431e-06]))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time learn.predict(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PILImage mode=RGB size=192x120"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img2 = PILImage.create('doraemon.jpg')\n",
    "img2.thumbnail((192, 192))\n",
    "img2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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"
    },
    {
     "data": {
      "text/plain": [
       "('True', tensor(1), tensor([0.1912, 0.8088]))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.predict(img2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PILImage mode=RGB size=192x127"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img3 = PILImage.create('cat.jpg')\n",
    "img3.thumbnail((192, 192))\n",
    "img3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "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"
    },
    {
     "data": {
      "text/plain": [
       "('True', tensor(1), tensor([2.0690e-14, 1.0000e+00]))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.predict(img3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "categories = ('Dog', 'Cat')\n",
    "\n",
    "def classify_image(img):\n",
    "    pred, idx, prob = learn.predict(img)\n",
    "    return dict(zip(categories, map(float, prob)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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"
    },
    {
     "data": {
      "text/plain": [
       "{'Dog': 0.9999983310699463, 'Cat': 1.6431489484602935e-06}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classify_image(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "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"
    },
    {
     "data": {
      "text/plain": [
       "{'Dog': 0.19116763770580292, 'Cat': 0.8088324069976807}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classify_image(img2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "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"
    },
    {
     "data": {
      "text/plain": [
       "{'Dog': 2.069018242324694e-14, 'Cat': 1.0}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classify_image(img3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7860\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\gradio\\queueing.py\", line 536, in process_events\n",
      "    response = await route_utils.call_process_api(\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\gradio\\route_utils.py\", line 276, in call_process_api\n",
      "    output = await app.get_blocks().process_api(\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\gradio\\blocks.py\", line 1923, in process_api\n",
      "    result = await self.call_function(\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\gradio\\blocks.py\", line 1508, in call_function\n",
      "    prediction = await anyio.to_thread.run_sync(  # type: ignore\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\anyio\\to_thread.py\", line 28, in run_sync\n",
      "    return await get_asynclib().run_sync_in_worker_thread(func, *args, cancellable=cancellable,\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 818, in run_sync_in_worker_thread\n",
      "    return await future\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 754, in run\n",
      "    result = context.run(func, *args)\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\gradio\\utils.py\", line 818, in wrapper\n",
      "    response = f(*args, **kwargs)\n",
      "  File \"C:\\Users\\admin\\AppData\\Local\\Temp\\ipykernel_19432\\4081285573.py\", line 4, in classify_image\n",
      "    pred, idx, prob = learn.predict(img)\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\fastai\\learner.py\", line 320, in predict\n",
      "    dl = self.dls.test_dl([item], rm_type_tfms=rm_type_tfms, num_workers=0)\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\fastai\\data\\core.py\", line 524, in test_dl\n",
      "    test_ds = test_set(self.valid_ds, test_items, rm_tfms=rm_type_tfms, with_labels=with_labels\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\fastai\\data\\core.py\", line 503, 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 \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\fastai\\data\\core.py\", line 503, in <listcomp>\n",
      "    if rm_tfms is None: rm_tfms = [tl.infer_idx(get_first(test_items)) for tl in test_tls]\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\fastai\\data\\core.py\", line 397, 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.WindowsPath'>\n",
      "  - <class 'pathlib.Path'>\n",
      "  - <class 'str'>\n",
      "  - <class 'torch.Tensor'>\n",
      "  - <class 'numpy.ndarray'>\n",
      "  - <class 'bytes'>\n",
      "  - <class 'PIL.Image.Image'>\n",
      "  - <class 'fastai.vision.core.PILImage'>\n",
      " but got <class 'NoneType'>\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\gradio\\queueing.py\", line 536, in process_events\n",
      "    response = await route_utils.call_process_api(\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\gradio\\route_utils.py\", line 276, in call_process_api\n",
      "    output = await app.get_blocks().process_api(\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\gradio\\blocks.py\", line 1923, in process_api\n",
      "    result = await self.call_function(\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\gradio\\blocks.py\", line 1508, in call_function\n",
      "    prediction = await anyio.to_thread.run_sync(  # type: ignore\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\anyio\\to_thread.py\", line 28, in run_sync\n",
      "    return await get_asynclib().run_sync_in_worker_thread(func, *args, cancellable=cancellable,\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 818, in run_sync_in_worker_thread\n",
      "    return await future\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 754, in run\n",
      "    result = context.run(func, *args)\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\gradio\\utils.py\", line 818, in wrapper\n",
      "    response = f(*args, **kwargs)\n",
      "  File \"C:\\Users\\admin\\AppData\\Local\\Temp\\ipykernel_19432\\4081285573.py\", line 4, in classify_image\n",
      "    pred, idx, prob = learn.predict(img)\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\fastai\\learner.py\", line 320, in predict\n",
      "    dl = self.dls.test_dl([item], rm_type_tfms=rm_type_tfms, num_workers=0)\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\fastai\\data\\core.py\", line 524, in test_dl\n",
      "    test_ds = test_set(self.valid_ds, test_items, rm_tfms=rm_type_tfms, with_labels=with_labels\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\fastai\\data\\core.py\", line 503, 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 \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\fastai\\data\\core.py\", line 503, in <listcomp>\n",
      "    if rm_tfms is None: rm_tfms = [tl.infer_idx(get_first(test_items)) for tl in test_tls]\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\fastai\\data\\core.py\", line 397, 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.WindowsPath'>\n",
      "  - <class 'pathlib.Path'>\n",
      "  - <class 'str'>\n",
      "  - <class 'torch.Tensor'>\n",
      "  - <class 'numpy.ndarray'>\n",
      "  - <class 'bytes'>\n",
      "  - <class 'PIL.Image.Image'>\n",
      "  - <class 'fastai.vision.core.PILImage'>\n",
      " but got <class 'NoneType'>\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>"
      ]
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    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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"
    },
    {
     "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": "stderr",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\gradio\\queueing.py\", line 536, in process_events\n",
      "    response = await route_utils.call_process_api(\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\gradio\\route_utils.py\", line 276, in call_process_api\n",
      "    output = await app.get_blocks().process_api(\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\gradio\\blocks.py\", line 1923, in process_api\n",
      "    result = await self.call_function(\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\gradio\\blocks.py\", line 1508, in call_function\n",
      "    prediction = await anyio.to_thread.run_sync(  # type: ignore\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\anyio\\to_thread.py\", line 28, in run_sync\n",
      "    return await get_asynclib().run_sync_in_worker_thread(func, *args, cancellable=cancellable,\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 818, in run_sync_in_worker_thread\n",
      "    return await future\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 754, in run\n",
      "    result = context.run(func, *args)\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\gradio\\utils.py\", line 818, in wrapper\n",
      "    response = f(*args, **kwargs)\n",
      "  File \"C:\\Users\\admin\\AppData\\Local\\Temp\\ipykernel_19432\\4081285573.py\", line 4, in classify_image\n",
      "    pred, idx, prob = learn.predict(img)\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\fastai\\learner.py\", line 320, in predict\n",
      "    dl = self.dls.test_dl([item], rm_type_tfms=rm_type_tfms, num_workers=0)\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\fastai\\data\\core.py\", line 524, in test_dl\n",
      "    test_ds = test_set(self.valid_ds, test_items, rm_tfms=rm_type_tfms, with_labels=with_labels\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\fastai\\data\\core.py\", line 503, 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 \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\fastai\\data\\core.py\", line 503, in <listcomp>\n",
      "    if rm_tfms is None: rm_tfms = [tl.infer_idx(get_first(test_items)) for tl in test_tls]\n",
      "  File \"c:\\Users\\admin\\anaconda3\\lib\\site-packages\\fastai\\data\\core.py\", line 397, 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.WindowsPath'>\n",
      "  - <class 'pathlib.Path'>\n",
      "  - <class 'str'>\n",
      "  - <class 'torch.Tensor'>\n",
      "  - <class 'numpy.ndarray'>\n",
      "  - <class 'bytes'>\n",
      "  - <class 'PIL.Image.Image'>\n",
      "  - <class 'fastai.vision.core.PILImage'>\n",
      " but got <class 'NoneType'>\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"
    }
   ],
   "source": [
    "image = gr.Image(height=192, width=192)\n",
    "label = gr.Label()\n",
    "examples = ['dog.jpg', 'doraemon.jpg', 'cat.jpg']\n",
    "\n",
    "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
    "intf.launch(inline=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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