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{
"cells": [
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"import gradio as gr\n",
"import tensorflow as tf\n",
"import numpy as np\n",
"from PIL import Image"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Defaulting to user installation because normal site-packages is not writeable\n",
"Requirement already satisfied: tensorflow in /Users/chiarazbinden/Library/Python/3.9/lib/python/site-packages (2.16.1)\n",
"Requirement already satisfied: tensorflow-datasets in /Users/chiarazbinden/Library/Python/3.9/lib/python/site-packages (4.9.3)\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install tensorflow tensorflow-datasets gradio pillow matplotlib"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"model_path = \"pokemon-model_transferlearning.keras\"\n",
"model = tf.keras.models.load_model(model_path)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"# Define the core prediction function\n",
"def predict_pokemon(image):\n",
" # Preprocess image\n",
" print(type(image))\n",
" image = Image.fromarray(image.astype('uint8')) # Convert numpy array to PIL image\n",
" image = image.resize((150, 150)) #resize the image to 28x28 and converts it to gray scale\n",
" image = np.array(image)\n",
" image = np.expand_dims(image, axis=0) # same as image[None, ...]\n",
" \n",
" # Predict\n",
" prediction = model.predict(image)\n",
" \n",
" # Because the output layer was dense(0) without an activation function, we need to apply sigmoid to get the probability\n",
" # we could also change the output layer to dense(1, activation='sigmoid')\n",
" prediction = np.round(float(tf.sigmoid(prediction)), 2)\n",
" p_cat = (1 - prediction)\n",
" p_dog = prediction\n",
" return {'cat': p_cat, 'dog': p_dog}"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"from PIL import Image\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"\n",
"# Define the core prediction function\n",
"def predict_pokemon(image):\n",
" # Preprocess image\n",
" image = Image.fromarray(image.astype('uint8')) # Convert numpy array to PIL image\n",
" image = image.resize((150, 150)) # Resize the image to 150x150\n",
" image = np.array(image)\n",
" image = np.expand_dims(image, axis=0) # Add batch dimension\n",
" \n",
" # Predict\n",
" prediction = model.predict(image)\n",
" \n",
" # Apply softmax to get probabilities for each class\n",
" probabilities = tf.nn.softmax(prediction)\n",
" \n",
" # Map probabilities to Pokemon classes\n",
" pokemon_classes = ['Articuno', 'Bulbasaur', 'Charmander'] \n",
" probabilities_dict = {pokemon_class: round(float(probability), 2) for pokemon_class, probability in zip(pokemon_classes, probabilities[0])}\n",
" \n",
" return probabilities_dict\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7863\n",
"IMPORTANT: You are using gradio version 4.27.0, however version 4.29.0 is available, please upgrade.\n",
"--------\n",
"Running on public URL: https://a1f42e2a7f46b68a92.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"https://a1f42e2a7f46b68a92.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step\n",
"\u001b[1m1/1\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step\n"
]
}
],
"source": [
"# Create the Gradio interface\n",
"input_image = gr.Image()\n",
"iface = gr.Interface(\n",
" fn=predict_pokemon,\n",
" inputs=input_image, \n",
" outputs=gr.Label(),\n",
" examples=[\"images/cat1.jpeg\", \"images/cat2.jpeg\", \"images/cat3.jpeg\", \"images/cat4.jpeg\", \"images/dog1.jpeg\", \"images/dog2.jpeg\", \"images/dog3.jpeg\"], \n",
" description=\"A simple mlp classification model for image classification using the mnist dataset.\")\n",
"iface.launch(share=True)"
]
}
],
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