| import gradio as gr | |
| import numpy as np | |
| from tensorflow.keras.models import load_model | |
| from PIL import Image | |
| model = load_model("static_cnn_colab_final.keras") | |
| class_names = ['cloudy', 'desert', 'green_area', 'water'] | |
| def predict_image(img): | |
| img = img.resize((128,128)) | |
| img_array = np.array(img)/255.0 | |
| img_array = np.expand_dims(img_array, axis=0) | |
| predictions = model.predict(img_array) | |
| predicted_class = class_names[np.argmax(predictions)] | |
| confidence = np.max(predictions) | |
| return f"{predicted_class} ({confidence*100:.2f}%)" | |
| iface = gr.Interface( | |
| fn=predict_image, | |
| inputs=gr.Image(type="pil"), | |
| outputs="text", | |
| title="Satellite Image Classifier", | |
| description="Upload a satellite image and the model will predict its class (cloudy, desert, green_area, water)." | |
| ) | |
| iface.launch(share=False) |