{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "27c92395-9f07-4383-9526-58363a090f46",
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.vision.all import *\n",
    "import gradio as gr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a8969a2b-13c0-496d-918b-179bcd1ad7b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_cat(x): return x[0].isupper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "407054be-12ef-40f0-860f-856e8a835a5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = load_learner('model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "51f3903e-55c2-476e-96f9-ece20e49915c",
   "metadata": {},
   "outputs": [],
   "source": [
    "categories = ('Dog', 'Cat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3067fd5b-4872-4a0b-94bb-3a245129f340",
   "metadata": {},
   "outputs": [],
   "source": [
    "def classify_image(img):\n",
    "    pred, idx, probs = learn.predict(img)\n",
    "    return dict(zip(categories, map(float, probs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f285720c-a8d3-46ba-80e9-8e5ef79ec4ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "image = gr.Image(height=192, width=192)\n",
    "label = gr.Label()\n",
    "examples = ['dog.jpg', 'cat.jpg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9ae63726-4b8b-4d9c-9a95-a18e9ffe61ab",
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [],
      "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": [
    "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
    "intf.launch(inline=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (base)",
   "language": "python",
   "name": "base"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
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