HTTP Get Request.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "comparable-williams",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ongoing-things",
   "metadata": {},
   "outputs": [],
   "source": [
    "from django.urls import path\n",
    "from PIL import Image\n",
    "from skimage import transform\n",
    "\n",
    "from tensorflow.compat.v1 import ConfigProto\n",
    "from tensorflow.compat.v1 import InteractiveSession\n",
    "\n",
    "config = ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "session = InteractiveSession(config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "underlying-baker",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "#change directory to where you want image to be written\n",
    "dire = r'C:\\Users\\jaych\\Documents\\Senior Design\\test_images\\image5.jpg'\n",
    "recieve = requests.get('https://www.statnews.com/wp-content/uploads/2020/07/1-s2.0-S0735675720302746-gr1_lrg.jpg')\n",
    "with open(dire,'wb') as f:\n",
    "    f.write(recieve.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "amended-vector",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.load_model(r'C:\\Users\\jaych\\Documents\\Senior Design\\covid_fine_tuned.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "lovely-platform",
   "metadata": {},
   "outputs": [],
   "source": [
    "#preprocess the image so it can be evaluated by the model with the proper dimensions, includes rescaling\n",
    "\n",
    "def load(filename):\n",
    "   np_image = Image.open(filename)\n",
    "   np_image = np.array(np_image).astype('float32')/255\n",
    "   np_image = transform.resize(np_image, (218, 178, 3))\n",
    "   np_image = np.expand_dims(np_image, axis=0)\n",
    "   return np_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "hired-poison",
   "metadata": {},
   "outputs": [],
   "source": [
    "img = load(dire)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "extensive-payday",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 218, 178, 3)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "grand-employee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.83280176\n"
     ]
    }
   ],
   "source": [
    "pred = model.predict(img)[0, 0]\n",
    "print(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "important-search",
   "metadata": {},
   "outputs": [],
   "source": [
    "def result(num):\n",
    "    if round(num) == 1:\n",
    "        print(\"Image registers as positive for COVID-19\")\n",
    "    elif round(num) == 0:\n",
    "        print(\"Image registers as negative for COVID-19\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "sophisticated-fancy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image registers as positive for COVID-19\n"
     ]
    }
   ],
   "source": [
    "result(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "amber-albany",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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