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| import gradio as gr | |
| import cv2 | |
| import numpy as np | |
| from tensorflow.keras.models import load_model | |
| # Define the class names | |
| class_names = ['AnnualCrop', 'Forest', 'HerbaceousVegetation', 'Highway', 'Industrial', 'Pasture', 'PermanentCrop', 'Residential', 'River', 'SeaLake'] | |
| # Function to load the selected model | |
| def load_selected_model(model_name): | |
| if model_name == "ResNet50": | |
| return load_model('resnet.h5') | |
| elif model_name == "CNN": | |
| return load_model('cnn.h5') | |
| else: | |
| raise ValueError("Invalid model name") | |
| # Function to predict the image | |
| def predict_image(image, model_name): | |
| model = load_selected_model(model_name) | |
| img = cv2.resize(image, (64, 64)) | |
| img = img / 255.0 | |
| img = np.expand_dims(img, axis=0) | |
| predictions = model.predict(img)[0] | |
| predicted_class_index = np.argmax(predictions) | |
| predicted_class_name = class_names[predicted_class_index] | |
| confidence = predictions[predicted_class_index] | |
| # Get the top 3 predictions | |
| top_indices = predictions.argsort()[-3:][::-1] | |
| top_classes = [class_names[i] for i in top_indices] | |
| top_confidences = [predictions[i] for i in top_indices] | |
| # Format the output | |
| result = "Top Predictions:\n" | |
| for i in range(3): | |
| result += f"{top_classes[i]} (Confidence: {top_confidences[i]:.2f})\n" | |
| return result | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Satellite Image Classifier (CaseOh edition)") | |
| with gr.Row(): | |
| with gr.Column(): | |
| model_selector = gr.Dropdown(choices=["ResNet50", "CNN"], label="Select Model") | |
| image_input = gr.Image(type="numpy", label="Upload Image") | |
| predict_button = gr.Button("Predict") | |
| image_output = gr.Image(type="numpy", label="Uploaded Image") | |
| prediction_output = gr.Textbox(label="Prediction") | |
| predict_button.click(fn=predict_image, inputs=[image_input, model_selector], outputs=prediction_output) | |
| image_input.change(fn=lambda x: x, inputs=image_input, outputs=image_output) | |
| demo.launch(share=True) | |