{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow_datasets as tfds\n",
    "import tensorflow as tf\n",
    "import tensorflow_hub as hub\n",
    "import sklearn\n",
    "import random\n",
    "from glob import glob\n",
    "import matplotlib.pyplot as plt\n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TF version: 2.9.2\n",
      "Hub version: 0.12.0\n",
      "GPU is available\n"
     ]
    }
   ],
   "source": [
    "print(\"TF version:\", tf.__version__)\n",
    "print(\"Hub version:\", hub.__version__)\n",
    "print(\"GPU is\", \"available\" if tf.config.list_physical_devices('GPU') else \"NOT AVAILABLE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb7.h5\n",
      "268326632/268326632 [==============================] - 13s 0us/step\n"
     ]
    }
   ],
   "source": [
    "\n",
    "inception_net = tf.keras.applications.EfficientNetB7()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "response = requests.get(\"https://git.io/JJkYN\")\n",
    "labels = response.text.split(\"\\n\")\n",
    "\n",
    "def classify_image(inp):\n",
    "  inp = inp.reshape((-1, 600, 600, 3))\n",
    "  inp = tf.keras.applications.efficientnet_v2.preprocess_input(inp)\n",
    "  prediction = inception_net.predict(inp).flatten()\n",
    "  confidences = {labels[i]: float(prediction[i]) for i in range(1000)}\n",
    "  return confidences\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gradio as gr\n",
    "\n",
    "gr.Interface(fn=classify_image, \n",
    "             inputs=gr.Image(shape=(600, 600)),\n",
    "             outputs=gr.Label(num_top_classes=3),\n",
    "             examples=[\"data/animals/animals/antelope/0a37838e99.jpg\", \"data/animals/animals/starfish/0a63e965c2.jpg\"]).launch(share=True)\n"
   ]
  }
 ],
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   "language": "python",
   "name": "python3"
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