{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "250fcb86",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|default_exp app"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea967051",
   "metadata": {},
   "source": [
    "# Powerline app"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3f54ef95",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'gradio'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[12], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m#| export\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastai\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvision\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mall\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mgradio\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mgr\u001b[39;00m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'gradio'"
     ]
    }
   ],
   "source": [
    "#| export\n",
    "from fastai.vision.all import *\n",
    "import gradio as gr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d25179ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| export\n",
    "learn = load_learner('model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d752f05b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| export\n",
    "labels = learn.dls.vocab\n",
    "\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": 10,
   "id": "80d55893",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'gr' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mgr\u001b[49m\u001b[38;5;241m.\u001b[39mInterface(fn\u001b[38;5;241m=\u001b[39mpredict, inputs\u001b[38;5;241m=\u001b[39mgr\u001b[38;5;241m.\u001b[39minputs\u001b[38;5;241m.\u001b[39mImage(shape\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m512\u001b[39m, \u001b[38;5;241m512\u001b[39m)), outputs\u001b[38;5;241m=\u001b[39mgr\u001b[38;5;241m.\u001b[39moutputs\u001b[38;5;241m.\u001b[39mLabel(num_top_classes\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m))\u001b[38;5;241m.\u001b[39mlaunch(share\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'gr' is not defined"
     ]
    }
   ],
   "source": [
    "#| export\n",
    "gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=3)).launch(share=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e304d06",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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