{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "db4fccaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|default_exp app\n",
    "\n",
    "!pip install -Uqq fastai\n",
    "!pip install -Uqq gradio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7f067622",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "\n",
    "from fastai.vision.all import *\n",
    "import gradio as gr\n",
    "\n",
    "def is_cat(x): return x[0].isupper() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2a2f5376",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "\n",
    "learn = load_learner('model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2ee57395",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "\n",
    "labels = learn.dls.vocab\n",
    "def predict(img):\n",
    "    img = PILImage.create(img)\n",
    "    pred,pred_idx,probs = learn.predict(img)\n",
    "    return {labels[i]: float(probs[i]) for i in range(len(labels))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7e0abb03",
   "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/html": [
       "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#|export\n",
    "\n",
    "gr.Interface(fn=predict, inputs=\"image\", outputs=\"label\").launch(share=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "22dc9b3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Export successful\n"
     ]
    }
   ],
   "source": [
    "import nbdev\n",
    "nbdev.export.nb_export('is_a_cat.ipynb', './')\n",
    "print('Export successful')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "baca5791",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}