model file

model.ipynb

@@ -0,0 +1,121 @@
+{
+ "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"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 ('work')",
+   "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": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "59f0528c0641d303038c15eb2f7ee076b3157354b9138799665619ae8b3de89f"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}