{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Original Author: Jonathan Hudson\n",
    "#CPSC 501 F22"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DhZTTSlltFh2"
   },
   "source": [
    "Imports that are needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KsdVGfVCnQ4J"
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "huG1gu0KtG_a"
   },
   "source": [
    "Determine the arguments\n",
    "A weird way to do a notebook but lets code match non notebook code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ukCbw8ydnP3k"
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Dataset: notMNIST\n",
      "Model: notMNIST.h5\n",
      "Image: image.png\n",
      "Class index: 1\n"
     ]
    }
   ],
   "source": [
    "sys.argv = [\"\", input(\"Dataset:\"), input(\"Model:\"), input(\"Image:\"), input(\"Class index:\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6TD50Q9e_0ZE"
   },
   "outputs": [],
   "source": [
    "def check_args():\n",
    "     if(len(sys.argv) != 5):\n",
    "          print(\"Usage python predict.py <MNIST,notMNIST> <model.h5> <image.png> <prediction class index>\")\n",
    "          sys.exit(1)\n",
    "     if sys.argv[1] == \"MNIST\":\n",
    "          print(\"--Dataset MNIST--\")\n",
    "          class_names = list(range(10))\n",
    "     elif sys.argv[1] == \"notMNIST\":\n",
    "          print(\"--Dataset notMNIST--\")\n",
    "          class_names = [\"A\",\"B\",\"C\",\"D\",\"E\",\"F\",\"G\",\"H\",\"I\",\"J\"]\n",
    "     else:\n",
    "          print(f\"Choose MNIST or notMNIST, not {sys.argv[1]}\")\n",
    "          sys.exit(2)\n",
    "     if sys.argv[2][-3:] != \".h5\":\n",
    "          print(f\"{sys.argv[2]} is not a h5 extension\")\n",
    "          sys.exit(3)\n",
    "     if sys.argv[3][-4:] != \".png\":\n",
    "          print(f\"{sys.argv[3]} is not a png extension\")\n",
    "          sys.exit(3)\n",
    "     img = plt.imread(sys.argv[3])\n",
    "     if len(img.shape) != 2:\n",
    "          print(\"Image is not grey scale!\")\n",
    "          sys.exit(4)\n",
    "     if img.shape != (28,28):\n",
    "          print(\"Image is not 28 by 28!\")\n",
    "          sys.exit(4)\n",
    "     if not sys.argv[4].isdigit():\n",
    "          print(f\"{sys.argv[4]} is not an integer (0-9)\")\n",
    "          sys.exit(3)\n",
    "     if int(sys.argv[4]) < 0 or int(sys.argv[4]) > 9 :\n",
    "          print(f\"{sys.argv[4]} is not an integer (0-9)\")\n",
    "          sys.exit(3)\n",
    "     return class_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GFqxpnv4tjR6"
   },
   "source": [
    "Completed functions to plot for you"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "E75I4-RVeme5"
   },
   "outputs": [],
   "source": [
    "def plot(class_names, prediction, true_label, predicted_label, img):\n",
    "    plt.figure(figsize=(6,3))\n",
    "    plt.subplot(1,2,1)\n",
    "    plt.grid(False)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.imshow(img, cmap=plt.cm.binary)\n",
    "    predicted_label = np.argmax(prediction)\n",
    "    if predicted_label == true_label:\n",
    "        color = 'blue'\n",
    "    else:\n",
    "        color = 'red'\n",
    "    plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],100*np.max(prediction),class_names[true_label]),color=color)\n",
    "    plt.subplot(1,2,2)\n",
    "    plt.grid(False)\n",
    "    plt.xticks(range(10))\n",
    "    plt.yticks([])\n",
    "    thisplot = plt.bar(class_names, prediction, color=\"#777777\")\n",
    "    plt.ylim([0, 1])\n",
    "    thisplot[predicted_label].set_color('red')\n",
    "    thisplot[true_label].set_color('blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kL_b5WbYtonX"
   },
   "source": [
    "Finish the missing parts of function to predict output from input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GChTgYKGtoFN"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--Dataset notMNIST--\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'image.png'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m--------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                        Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [5], line 22\u001b[0m\n\u001b[0;32m     20\u001b[0m     plot(class_names, prediction, true_label, predicted_label, img[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m     21\u001b[0m     plt\u001b[38;5;241m.\u001b[39mshow()\n\u001b[1;32m---> 22\u001b[0m \u001b[43mmain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[1;32mIn [5], line 2\u001b[0m, in \u001b[0;36mmain\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmain\u001b[39m():\n\u001b[1;32m----> 2\u001b[0m      class_names \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      3\u001b[0m      \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m--Load Model \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msys\u001b[38;5;241m.\u001b[39margv[\u001b[38;5;241m2\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m--\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      4\u001b[0m      \u001b[38;5;66;03m#Load the model that should be in sys.argv[2]\u001b[39;00m\n",
      "Cell \u001b[1;32mIn [3], line 20\u001b[0m, in \u001b[0;36mcheck_args\u001b[1;34m()\u001b[0m\n\u001b[0;32m     18\u001b[0m      \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msys\u001b[38;5;241m.\u001b[39margv[\u001b[38;5;241m3\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m is not a png extension\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     19\u001b[0m      sys\u001b[38;5;241m.\u001b[39mexit(\u001b[38;5;241m3\u001b[39m)\n\u001b[1;32m---> 20\u001b[0m img \u001b[38;5;241m=\u001b[39m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimread\u001b[49m\u001b[43m(\u001b[49m\u001b[43msys\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margv\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     21\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(img\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[0;32m     22\u001b[0m      \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mImage is not grey scale!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32mD:\\env\\lib\\site-packages\\matplotlib\\pyplot.py:2113\u001b[0m, in \u001b[0;36mimread\u001b[1;34m(fname, format)\u001b[0m\n\u001b[0;32m   2111\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(matplotlib\u001b[38;5;241m.\u001b[39mimage\u001b[38;5;241m.\u001b[39mimread)\n\u001b[0;32m   2112\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mimread\u001b[39m(fname, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m-> 2113\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmatplotlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mD:\\env\\lib\\site-packages\\matplotlib\\image.py:1541\u001b[0m, in \u001b[0;36mimread\u001b[1;34m(fname, format)\u001b[0m\n\u001b[0;32m   1534\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fname, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(parse\u001b[38;5;241m.\u001b[39murlparse(fname)\u001b[38;5;241m.\u001b[39mscheme) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   1535\u001b[0m     \u001b[38;5;66;03m# Pillow doesn't handle URLs directly.\u001b[39;00m\n\u001b[0;32m   1536\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   1537\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease open the URL for reading and pass the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1538\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresult to Pillow, e.g. with \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1539\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m``np.array(PIL.Image.open(urllib.request.urlopen(url)))``.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1540\u001b[0m         )\n\u001b[1;32m-> 1541\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mimg_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m image:\n\u001b[0;32m   1542\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m (_pil_png_to_float_array(image)\n\u001b[0;32m   1543\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(image, PIL\u001b[38;5;241m.\u001b[39mPngImagePlugin\u001b[38;5;241m.\u001b[39mPngImageFile) \u001b[38;5;28;01melse\u001b[39;00m\n\u001b[0;32m   1544\u001b[0m             pil_to_array(image))\n",
      "File \u001b[1;32mD:\\env\\lib\\site-packages\\PIL\\ImageFile.py:104\u001b[0m, in \u001b[0;36mImageFile.__init__\u001b[1;34m(self, fp, filename)\u001b[0m\n\u001b[0;32m    100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdecodermaxblock \u001b[38;5;241m=\u001b[39m MAXBLOCK\n\u001b[0;32m    102\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_path(fp):\n\u001b[0;32m    103\u001b[0m     \u001b[38;5;66;03m# filename\u001b[39;00m\n\u001b[1;32m--> 104\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    105\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfilename \u001b[38;5;241m=\u001b[39m fp\n\u001b[0;32m    106\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exclusive_fp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'image.png'"
     ]
    }
   ],
   "source": [
    "def main():\n",
    "     class_names = check_args()\n",
    "     print(f\"--Load Model {sys.argv[2]}--\")\n",
    "     #Load the model that should be in sys.argv[2]\n",
    "     model = None\n",
    "     print(f\"--Load Image {sys.argv[3]}--\")\n",
    "     img = plt.imread(sys.argv[3])\n",
    "     if np.amax(img.flatten()) > 1:\n",
    "          img = img/255\n",
    "     img = 1 - img\n",
    "     print(f\"--Predict as Class {sys.argv[4]}--\")\n",
    "     predict(model, class_names, img, int(sys.argv[4]))\n",
    "\n",
    "def predict(model, class_names, img, true_label):\n",
    "    img = np.array([img])\n",
    "    #Replace these two lines with code to make a prediction\n",
    "    prediction = [1/10,1/10,1/10,1/10,1/10,1/10,1/10,1/10,1/10,1/10]\n",
    "    #Determine what the predicted label is\n",
    "    predicted_label = 0\n",
    "    plot(class_names, prediction, true_label, predicted_label, img[0])\n",
    "    plt.show()\n",
    "main()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "predict.ipynb",
   "provenance": []
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  "kernelspec": {
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  "language_info": {
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