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| import tensorflow as tf | |
| print("TensorFlow is installed:", tf.__version__) | |
| import gradio as gr | |
| import tensorflow as tf | |
| import numpy as np | |
| from PIL import Image | |
| # Load trained model | |
| model = tf.keras.models.load_model("eurosat_cnn_model.h5") | |
| # Define class labels | |
| class_labels = [ | |
| "AnnualCrop", "Forest", "Highway", "Industrial", "Pasture", | |
| "PermanentCrop", "Residential", "River", "SeaLake", "HerbaceousVegetation" | |
| ] | |
| # Function to process image and make predictions | |
| def predict_image(image): | |
| image = image.resize((64, 64)) # Resize image | |
| img_array = np.array(image) / 255.0 # Normalize | |
| img_array = np.expand_dims(img_array, axis=0) # Add batch dimension | |
| prediction = model.predict(img_array) # Predict | |
| return {class_labels[i]: float(prediction[0][i]) for i in range(len(class_labels))} | |
| # Create Gradio Interface | |
| iface = gr.Interface(fn=predict_image, inputs="image", outputs="label") | |
| iface.launch() | |