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HTTP Get Request.ipynb

@@ -0,0 +1,189 @@
+{
+ "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": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}