flat_tire_detector.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import *\n",
    "from fastai2.vision.widgets import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "key = '3af425cec4554f979de0490d75aba1d5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = search_images_bing(key, 'flat tire')\n",
    "ims = results.attrgot('content_url')\n",
    "len(ims)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ims = ['http://1.bp.blogspot.com/-o4iwEQxqOmM/TbkJD71uscI/AAAAAAAADv0/IHi8vofRnkE/s1600/114_365%2BFlat%2BTire.JPG']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dest = 'images/flat_tire.jpg'\n",
    "download_url(ims[0], dest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install voila\n",
    "# !jupyter serverextension enable voila --sys-prefix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1ffe2812a7df494189cbb7b89fdbf3f6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FileUpload(value={}, description='Upload')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "btn_upload = widgets.FileUpload()\n",
    "btn_upload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = PILImage.create(btn_upload.data[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "49ee1fbe64c0408db1f5e4acf1cf979f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_pl = widgets.Output()\n",
    "out_pl.clear_output()\n",
    "with out_pl: display(img.to_thumb(128,128))\n",
    "out_pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(#1) [Path('export.pkl')]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = Path()\n",
    "path.ls(file_exts='.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn_inf = load_learner(path/'export.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred,pred_idx,probs = learn_inf.predict(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d1f92ea8db0d4ba48607571b86140c9a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Label(value='Prediction: full; Probability: 0.5334')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lbl_pred = widgets.Label()\n",
    "lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'\n",
    "lbl_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "751e601b2b1e480687d5cdfcd7c09130",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Classify', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "btn_run = widgets.Button(description='Classify')\n",
    "btn_run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def on_click_classify(change):\n",
    "    img = PILImage.create(btn_upload.data[-1])\n",
    "    out_pl.clear_output()\n",
    "    with out_pl: display(img.to_thumb(128,128))\n",
    "    pred,pred_idx,probs = learn_inf.predict(img)\n",
    "    lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'\n",
    "\n",
    "btn_run.on_click(on_click_classify)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "btn_upload = widgets.FileUpload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cac67ec9ea6741dc9d64637f9bbd4212",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Label(value='Choose the tire to analyze'), FileUpload(value={}, description='Upload'), Button(d…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "VBox([widgets.Label('Choose the tire to analyze'), \n",
    "      btn_upload, btn_run, out_pl, lbl_pred])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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