app.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#/default_exp app"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Successfully installed MarkupSafe-2.1.5 annotated-types-0.6.0 blis-0.7.11 catalogue-2.0.10 click-8.1.7 cloudpathlib-0.16.0 confection-0.1.4 contourpy-1.2.0 cycler-0.12.1 cymem-2.0.8 fastai-2.7.14 fastcore-1.5.29 fastdownload-0.0.7 fastprogress-1.0.3 filelock-3.13.1 fonttools-4.49.0 fsspec-2024.2.0 jinja2-3.1.3 joblib-1.3.2 kiwisolver-1.4.5 langcodes-3.3.0 matplotlib-3.8.3 mpmath-1.3.0 murmurhash-1.0.10 networkx-3.2.1 numpy-1.26.4 pandas-2.2.1 pillow-10.2.0 preshed-3.0.9 pydantic-2.6.3 pydantic-core-2.16.3 pyparsing-3.1.2 pytz-2024.1 pyyaml-6.0.1 scikit-learn-1.4.1.post1 scipy-1.12.0 smart-open-6.4.0 spacy-3.7.4 spacy-legacy-3.0.12 spacy-loggers-1.0.5 srsly-2.4.8 sympy-1.12 thinc-8.2.3 threadpoolctl-3.3.0 torch-2.2.1 torchvision-0.17.1 typer-0.9.0 tzdata-2024.1 wasabi-1.1.2 weasel-0.3.4\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install fastai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Building wheels for collected packages: ffmpy\n",
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install gradio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#/export\n",
    "from fastai.vision.all import *\n",
    "import gradio as gr\n",
    "from fastbook import *\n",
    "def is_cat(x): return x[0].isupper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "urls = search_images_ddg('dog photos', max_images=1)\n",
    "dest = Path('dog.jpg')\n",
    "if not dest.exists(): download_url(urls[0], dest, show_progress=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "PILImage mode=RGB size=192x128"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im = PILImage.create('dog.jpg')\n",
    "im.thumbnail((192,192))\n",
    "im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#/export\n",
    "learn = load_learner('model.pkl')"
   ]
  },
  {
   "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"
    },
    {
     "data": {
      "text/plain": [
       "('False', tensor(0), tensor([1.0000e+00, 5.9419e-08]))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.predict(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#/export\n",
    "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": 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": [
       "{'Dog': 1.0, 'Cat': 5.941916825236149e-08}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classify_image(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"/Users/imsang-yeob/miniconda3/lib/python3.12/site-packages/gradio/queueing.py\", line 501, in call_prediction\n",
      "    output = await route_utils.call_process_api(\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/Users/imsang-yeob/miniconda3/lib/python3.12/site-packages/gradio/route_utils.py\", line 252, in call_process_api\n",
      "    output = await app.get_blocks().process_api(\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/Users/imsang-yeob/miniconda3/lib/python3.12/site-packages/gradio/blocks.py\", line 1664, in process_api\n",
      "    result = await self.call_function(\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/Users/imsang-yeob/miniconda3/lib/python3.12/site-packages/gradio/blocks.py\", line 1205, in call_function\n",
      "    prediction = await anyio.to_thread.run_sync(\n",
      "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/Users/imsang-yeob/miniconda3/lib/python3.12/site-packages/anyio/to_thread.py\", line 56, in run_sync\n",
      "    return await get_async_backend().run_sync_in_worker_thread(\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/Users/imsang-yeob/miniconda3/lib/python3.12/site-packages/anyio/_backends/_asyncio.py\", line 2144, in run_sync_in_worker_thread\n",
      "    return await future\n",
      "           ^^^^^^^^^^^^\n",
      "  File \"/Users/imsang-yeob/miniconda3/lib/python3.12/site-packages/anyio/_backends/_asyncio.py\", line 851, in run\n",
      "    result = context.run(func, *args)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/Users/imsang-yeob/miniconda3/lib/python3.12/site-packages/gradio/utils.py\", line 690, in wrapper\n",
      "    response = f(*args, **kwargs)\n",
      "               ^^^^^^^^^^^^^^^^^^\n",
      "  File \"/var/folders/9d/8jyn_9md5xs0dcw7638m62g40000gn/T/ipykernel_70967/3391707847.py\", line 5, in classify_image\n",
      "    pred,idx,prob = learn.predict(img)\n",
      "                    ^^^^^\n",
      "NameError: name 'learn' is not defined\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/imsang-yeob/miniconda3/lib/python3.12/site-packages/gradio/queueing.py\", line 501, in call_prediction\n",
      "    output = await route_utils.call_process_api(\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/Users/imsang-yeob/miniconda3/lib/python3.12/site-packages/gradio/route_utils.py\", line 252, in call_process_api\n",
      "    output = await app.get_blocks().process_api(\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/Users/imsang-yeob/miniconda3/lib/python3.12/site-packages/gradio/blocks.py\", line 1664, in process_api\n",
      "    result = await self.call_function(\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/Users/imsang-yeob/miniconda3/lib/python3.12/site-packages/gradio/blocks.py\", line 1205, in call_function\n",
      "    prediction = await anyio.to_thread.run_sync(\n",
      "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/Users/imsang-yeob/miniconda3/lib/python3.12/site-packages/anyio/to_thread.py\", line 56, in run_sync\n",
      "    return await get_async_backend().run_sync_in_worker_thread(\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/Users/imsang-yeob/miniconda3/lib/python3.12/site-packages/anyio/_backends/_asyncio.py\", line 2144, in run_sync_in_worker_thread\n",
      "    return await future\n",
      "           ^^^^^^^^^^^^\n",
      "  File \"/Users/imsang-yeob/miniconda3/lib/python3.12/site-packages/anyio/_backends/_asyncio.py\", line 851, in run\n",
      "    result = context.run(func, *args)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/Users/imsang-yeob/miniconda3/lib/python3.12/site-packages/gradio/utils.py\", line 690, in wrapper\n",
      "    response = f(*args, **kwargs)\n",
      "               ^^^^^^^^^^^^^^^^^^\n",
      "  File \"/var/folders/9d/8jyn_9md5xs0dcw7638m62g40000gn/T/ipykernel_70967/3391707847.py\", line 5, in classify_image\n",
      "    pred,idx,prob = learn.predict(img)\n",
      "                    ^^^^^\n",
      "NameError: name 'learn' is not defined\n"
     ]
    }
   ],
   "source": [
    "#/export\n",
    "image = gr.Image()\n",
    "label = gr.Label()\n",
    "examples = ['dog.jpg', 'cat.jpg', 'dunno.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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = learn.model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nbdev\n",
    "nbdev.export.nb_export('app.ipynb', './')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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