Upload 4 files

app.py

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+import gradio as gr
+from inference import predict
+
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="filepath", label="Upload SAR Image"),
+    outputs="image",
+    title="SAR Image Colorization"
+)
+
+interface.launch()

inference.py

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+from tensorflow.keras.models import load_model
+import numpy as np
+from PIL import Image
+
+# Load model
+generator = load_model('generator_checkpoint.h5')
+
+# Example inference
+def predict(input_image_path, output_image_path):
+    img = Image.open(input_image_path).resize((256, 256))
+    img = np.array(img) / 255.0
+    img = np.expand_dims(img, axis=0)
+    
+    output = generator.predict(img)
+    output = (output[0] * 255).astype('uint8')
+    
+    Image.fromarray(output).save(output_image_path)
+
+# Example usage
+predict('sample_sar.png', 'predicted_colorized.png')

requirements.txt

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+tensorflow==2.12.0
+Pillow
+gradio
+numpy

sar_colorization_generator_final.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e015cd5be2afb027c24e99ba01b4099f9595cd898369b20471ff2538d197afb9
+size 5328448