marii/study_group_example

1{2 "cells": [3  {4   "cell_type": "code",5   "execution_count": 9,6   "metadata": {},7   "outputs": [],8   "source": [9    "# default_exp core"10   ]11  },12  {13   "cell_type": "markdown",14   "metadata": {},15   "source": [16    "# module name here\n",17    "\n",18    "> API details."19   ]20  },21  {22   "cell_type": "code",23   "execution_count": 2,24   "metadata": {},25   "outputs": [],26   "source": [27    "#hide\n",28    "from nbdev.showdoc import *"29   ]30  },31  {32   "cell_type": "code",33   "execution_count": 3,34   "metadata": {},35   "outputs": [36    {37     "data": {38      "text/html": [39       "<table border=\"1\" class=\"dataframe\">\n",40       "  <thead>\n",41       "    <tr style=\"text-align: left;\">\n",42       "      <th>epoch</th>\n",43       "      <th>train_loss</th>\n",44       "      <th>valid_loss</th>\n",45       "      <th>accuracy</th>\n",46       "      <th>time</th>\n",47       "    </tr>\n",48       "  </thead>\n",49       "  <tbody>\n",50       "    <tr>\n",51       "      <td>0</td>\n",52       "      <td>0.966888</td>\n",53       "      <td>0.311951</td>\n",54       "      <td>0.899188</td>\n",55       "      <td>00:28</td>\n",56       "    </tr>\n",57       "  </tbody>\n",58       "</table>"59      ],60      "text/plain": [61       "<IPython.core.display.HTML object>"62      ]63     },64     "metadata": {},65     "output_type": "display_data"66    },67    {68     "data": {69      "text/html": [70       "<table border=\"1\" class=\"dataframe\">\n",71       "  <thead>\n",72       "    <tr style=\"text-align: left;\">\n",73       "      <th>epoch</th>\n",74       "      <th>train_loss</th>\n",75       "      <th>valid_loss</th>\n",76       "      <th>accuracy</th>\n",77       "      <th>time</th>\n",78       "    </tr>\n",79       "  </thead>\n",80       "  <tbody>\n",81       "    <tr>\n",82       "      <td>0</td>\n",83       "      <td>0.458197</td>\n",84       "      <td>0.272663</td>\n",85       "      <td>0.908660</td>\n",86       "      <td>00:31</td>\n",87       "    </tr>\n",88       "  </tbody>\n",89       "</table>"90      ],91      "text/plain": [92       "<IPython.core.display.HTML object>"93      ]94     },95     "metadata": {},96     "output_type": "display_data"97    }98   ],99   "source": [100    "from fastai.vision.all import *\n",101    "path = untar_data(URLs.PETS)\n",102    "dls = ImageDataLoaders.from_name_re(path, get_image_files(path/'images'), pat='(.+)_\\d+.jpg', item_tfms=Resize(460), batch_tfms=aug_transforms(size=224, min_scale=0.75))\n",103    "learn = vision_learner(dls, models.resnet50, metrics=accuracy)\n",104    "learn.fine_tune(1)\n",105    "learn.path = Path('.')\n",106    "learn.export()"107   ]108  },109  {110   "cell_type": "code",111   "execution_count": 4,112   "metadata": {},113   "outputs": [],114   "source": [115    "#export\n",116    "learn = load_learner('export.pkl')"117   ]118  },119  {120   "cell_type": "code",121   "execution_count": 5,122   "metadata": {},123   "outputs": [],124   "source": [125    "#export\n",126    "labels = learn.dls.vocab\n",127    "def predict(img):\n",128    "    img = PILImage.create(img)\n",129    "    pred,pred_idx,probs = learn.predict(img)\n",130    "\n",131    "    return {labels[i]: float(probs[i]) for i in range(len(labels))}"132   ]133  },134  {135   "cell_type": "code",136   "execution_count": 6,137   "metadata": {},138   "outputs": [139    {140     "name": "stdout",141     "output_type": "stream",142     "text": [143      "Running on local URL:  http://127.0.0.1:7860/\n",144      "Running on public URL: https://31643.gradio.app\n",145      "\n",146      "This share link expires in 72 hours. For free permanent hosting, check out Spaces (https://huggingface.co/spaces)\n"147     ]148    },149    {150     "data": {151      "text/html": [152       "\n",153       "        <iframe\n",154       "            width=\"900\"\n",155       "            height=\"500\"\n",156       "            src=\"https://31643.gradio.app\"\n",157       "            frameborder=\"0\"\n",158       "            allowfullscreen\n",159       "            \n",160       "        ></iframe>\n",161       "        "162      ],163      "text/plain": [164       "<IPython.lib.display.IFrame at 0x7f5124f51430>"165      ]166     },167     "metadata": {},168     "output_type": "display_data"169    },170    {171     "data": {172      "text/plain": [173       "(<fastapi.applications.FastAPI at 0x7f51600bf880>,\n",174       " 'http://127.0.0.1:7860/',\n",175       " 'https://31643.gradio.app')"176      ]177     },178     "execution_count": 6,179     "metadata": {},180     "output_type": "execute_result"181    },182    {183     "data": {184      "text/html": [],185      "text/plain": [186       "<IPython.core.display.HTML object>"187      ]188     },189     "metadata": {},190     "output_type": "display_data"191    }192   ],193   "source": [194    "#export\n",195    "import gradio as gr\n",196    "gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=3)).launch(share=True)"197   ]198  },199  {200   "cell_type": "code",201   "execution_count": 10,202   "metadata": {},203   "outputs": [204    {205     "name": "stdout",206     "output_type": "stream",207     "text": [208      "Converted 00_app.ipynb.\r\n",209      "Converted index.ipynb.\r\n"210     ]211    }212   ],213   "source": [214    "! nbdev_build_lib"215   ]216  },217  {218   "cell_type": "code",219   "execution_count": null,220   "metadata": {},221   "outputs": [],222   "source": []223  }224 ],225 "metadata": {226  "kernelspec": {227   "display_name": "Python 3 (ipykernel)",228   "language": "python",229   "name": "python3"230  },231  "language_info": {232   "codemirror_mode": {233    "name": "ipython",234    "version": 3235   },236   "file_extension": ".py",237   "mimetype": "text/x-python",238   "name": "python",239   "nbconvert_exporter": "python",240   "pygments_lexer": "ipython3",241   "version": "3.9.7"242  }243 },244 "nbformat": 4,245 "nbformat_minor": 2246}247