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import os
import tensorflow as tf
# Disable all GPUS
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
# Load your pre-trained model
def load_model():
model = tf.keras.models.load_model('depi-graduation-project.h5') # Replace with your model's path
return model
model = load_model()
# Define the labels (categories)
labels = ['Water', 'Cloudy', 'Desert', 'Green Area']
# Function to preprocess the image and predict the class
def classify_image(image):
img = image.resize((128, 128)) # Resize the image
img = np.array(img) / 255.0 # Normalize the image
img = np.expand_dims(img, axis=0) # Add batch dimension
prediction = model.predict(img)
predicted_class = labels[np.argmax(prediction)]
# Prepare output with probabilities
return {labels[i]: float(prediction[0][i]) for i in range(len(labels))}
# Define the Gradio interface
image_input = gr.inputs.Image(shape=(128, 128))
label_output = gr.outputs.Label(num_top_classes=4)
# Launch the interface
gr.Interface(fn=classify_image,
inputs=image_input,
outputs=label_output,
title="Satellite Image Classification",
description="Classify satellite images into four types: Water, Cloudy, Desert, Green Area").launch() |