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{
"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
}
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