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{
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7865\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7865/\" 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"
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{
"data": {
"text/plain": []
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"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
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"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:6 out of the last 7 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x315091670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 608ms/step\n",
"[[0.49759743 0.50240254]]\n"
]
}
],
"source": [
"import gradio as gr\n",
"import tensorflow as tf\n",
"from PIL import Image\n",
"import numpy as np\n",
"\n",
"# Load the pre-trained model\n",
"model = tf.keras.models.load_model('dogs-vs-cats-model_transferlearning.keras')\n",
"\n",
"def classify_image(img):\n",
" \n",
" if isinstance(img, np.ndarray):\n",
" img = Image.fromarray(img.astype('uint8'), 'RGB')\n",
"\n",
" # Preprocess the image to fit the model's input requirements\n",
" img = img.resize((150, 150)) # Resize the image using PIL, which is intended here\n",
" img_array = np.array(img) # Convert the resized PIL image to a numpy array\n",
" img_array = img_array / 255.0 # Normalize pixel values to [0, 1]\n",
" img_array = np.expand_dims(img_array, axis=0) # Expand dimensions to fit model input shape\n",
"\n",
" # Make prediction\n",
" prediction = model.predict(img_array)\n",
"\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}\n",
"\n",
" print(prediction)\n",
"\n",
" probabilities = tf.sigmoid(prediction).numpy() # Convert tensor to numpy array if using \n",
"\n",
" # Formatting the probabilities\n",
" class_names = ['Cat', 'Dog']\n",
" results = {class_names[i]: float(prediction[0][i]) for i in range(2)} # Convert each probability to float\n",
" \n",
" return results\n",
"\n",
"# Create Gradio interface\n",
"iface = gr.Interface(fn=classify_image,\n",
" inputs=gr.Image(),\n",
" outputs=gr.Label(num_top_classes=2),\n",
" title=\"Cat vs Dog Classifier\",\n",
" description=\"Upload an image of a cat or dog to classify.\")\n",
"\n",
"# Launch the application\n",
"iface.launch()\n"
]
}
],
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"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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