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{
"cells": [
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7881\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7881/\" 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": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import gradio as gr\n",
"import tensorflow as tf\n",
"from PIL import Image\n",
"import numpy as np\n",
"\n",
"# Load the model\n",
"model_path = \"best_model.keras\"\n",
"model = tf.keras.models.load_model(model_path)\n",
"\n",
"# Define labels\n",
"labels = ['AnnualCrop', 'Forest', 'HerbaceousVegetation', 'Highway', 'Industrial', 'Pasture', 'PermanentCrop', 'Residential', 'River', 'SeaLake']\n",
"\n",
"def predict_image(image):\n",
" image = Image.fromarray(image.astype('uint8'), 'RGB')\n",
" image = image.resize((64, 64)) \n",
" image = np.array(image)\n",
"\n",
" prediction = model.predict(np.expand_dims(image, axis=0))\n",
" confidences = {labels[i]: float(prediction[0][i]) for i in range(len(labels))}\n",
" return confidences\n",
"\n",
"# Gradio interface\n",
"iface = gr.Interface(\n",
" fn=predict_image,\n",
" inputs=gr.Image(),\n",
" outputs=gr.Label(num_top_classes=10),\n",
" title=\"Sentinel Image Classifier\",\n",
" description=\"Upload a satellite image and the classifier will predict the type of land cover or feature.\",\n",
" examples = [\"images/annualcrop.jpg\", \"images/forest.jpg\", \"images/herbaceousvegetation.jpg\", \"images/highway.jpg\", \"images/industrial.jpg\", \"images/pasture.jpg\", \"images/permanentcrop.jpg\", \"images/residential.jpg\", \"images/river.jpg\", \"images/sealake.jpg\"]\n",
")\n",
"# Launch the interface\n",
"iface.launch(share=False)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 647ms/step\n",
"[[2.98899226e-06 3.38417292e-02 1.58750382e-08 1.03646407e-08\n",
" 3.04554437e-10 3.97204403e-08 7.68960629e-09 1.02308356e-10\n",
" 1.51210475e-06 9.66153681e-01]]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Min and max values: 0.16862745098039217 1.0\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Min and max values: 0.1411764705882353 1.0\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Min and max values: 0.16862745098039217 1.0\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n"
]
}
],
"source": [
"from PIL import Image\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"\n",
"# Load the trained model\n",
"model_path = 'sentinel_classificatiion_model.keras' # Adjust the path as necessary\n",
"model = tf.keras.models.load_model(model_path)\n",
"\n",
"# Load and process an example image\n",
"image_path = 'images/forest.jpg' # Replace with an example image from your dataset\n",
"image = Image.open(image_path)\n",
"\n",
"\n",
"# Predict using the model\n",
"prediction = model.predict(np.expand_dims(image, axis=0)) # Add batch dimension\n",
"print(prediction)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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