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import gradio as gr
import tensorflow as tf
from PIL import Image
import numpy as np
# Load the model
model_path = "sentinel_classification_model.keras"
model = tf.keras.models.load_model(model_path)
# Define labels
labels = ['AnnualCrop', 'Forest', 'HerbaceousVegetation', 'Highway', 'Industrial', 'Pasture', 'PermanentCrop', 'Residential', 'River', 'SeaLake']
def predict_image(image):
image = Image.fromarray(image.astype('uint8'), 'RGB')
image = image.resize((64, 64))
image = np.array(image)
prediction = model.predict(np.expand_dims(image, axis=0))
confidences = {labels[i]: float(prediction[0][i]) for i in range(len(labels))}
return confidences
# Gradio interface
iface = gr.Interface(
fn=predict_image,
inputs=gr.Image(),
outputs=gr.Label(num_top_classes=10),
title="Sentinel Image Classifier",
description="Upload a satellite image and the classifier will predict the type of land cover or feature.",
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"]
)
iface.launch()
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