Eaglemodel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h1><center>The Eagle Classifier</center></h1>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h2><center>The Potential of Classifiers</center></h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML('<h1><center>The Eagle Classifier</center></h1>'))\n",
    "display(HTML('<h2><center>The Potential of Classifiers</center></h2>'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import tensorflow as tf\n",
    "\n",
    "CATEGORIES = [\"Eagle\", \"NonEagle\"]\n",
    "\n",
    "def prepare(filepath):\n",
    "    IMG_SIZE = 70  # 50 in txt-based\n",
    "    img_array = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE)\n",
    "    new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))\n",
    "    return new_array.reshape(-1, IMG_SIZE, IMG_SIZE, 1)\n",
    "\n",
    "\n",
    "model = tf.keras.models.load_model(\"Eaglev1.model\")\n",
    "prediction = model.predict([prepare(r'C:\\Users\\chris\\Downloads\\EaglesMain\\vulture2.jpg')])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import numpy as np\n",
    "\n",
    "X = pickle.load(open(\"X.pickle\",\"rb\"))\n",
    "y = pickle.load(open(\"y.pickle\",\"rb\"))\n",
    "\n",
    "X = np.array(X/255.0)\n",
    "y = np.array(y)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "model.compile(loss=\"binary_crossentropy\",\n",
    "             optimizer=\"adam\",\n",
    "             metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3><center>Current Model Accuracy</center></h3>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 [==============================] - 0s 17ms/step - loss: 0.2532 - accuracy: 0.9126\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.253150075674057, 0.9125683307647705]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LOSS, ACCURACY]\n"
     ]
    }
   ],
   "source": [
    "display(HTML('<h3><center>Current Model Accuracy</center></h3>'))\n",
    "\n",
    "display(model.evaluate(X, y))\n",
    "print(\"[LOSS, ACCURACY]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><h3><center>Upload a file to be Classified Here:</center></h3></p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML('<p><h3><center>Upload a file to be Classified Here:</center></h3></p>'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "27c195736d194d4cb023bfebd5b53248",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FileUpload(value={}, description='Upload')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ptero.jpg\n",
      "ptero.jpg\n",
      "vulture2.jpg\n",
      "vulture2.jpg\n",
      "testeagle.jpg\n"
     ]
    }
   ],
   "source": [
    "from ipywidgets import FileUpload\n",
    "\n",
    "def on_upload_change(change):\n",
    "    if not change.new:\n",
    "        return\n",
    "    up = change.owner\n",
    "    for filename,data in up.value.items():\n",
    "        print(f'{filename}')\n",
    "        with open(filename, 'wb') as f:\n",
    "            f.write(data['content'])\n",
    "    up.value.clear()\n",
    "    up._counter = 0\n",
    "\n",
    "upload_btn = FileUpload()\n",
    "upload_btn.observe(on_upload_change, names='_counter')\n",
    "upload_btn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter your value: testeagle.jpg\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "val = input(\"Enter your value: \") \n",
    "\n",
    "Image(filename=val)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eagle\n"
     ]
    }
   ],
   "source": [
    "prediction = model.predict([prepare(val)])\n",
    "print(CATEGORIES[int(prediction[0][0])] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": 4
}