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import gradio as gr |
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import tensorflow as tf |
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import numpy as np |
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from pyrsgis import raster, convert |
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from sklearn.preprocessing import StandardScaler |
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from PIL import Image |
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import io |
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model = tf.keras.models.load_model('SGDNet.h5') |
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def predict(image_path): |
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ds, image_data = raster.read(image_path, bands='all') |
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image_data = convert.array_to_table(image_data) |
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scaler = StandardScaler() |
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image_data = scaler.fit_transform(image_data) |
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image_data = image_data.reshape((image_data.shape[0], 1, image_data.shape[1])) |
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predicted = model.predict(image_data) |
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predicted_prob = predicted[:, 1] |
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predicted_prob = np.reshape(predicted_prob, (ds.RasterYSize, ds.RasterXSize)) |
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im = Image.fromarray((predicted_prob * 255).astype(np.uint8)) |
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bio = io.BytesIO() |
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im.save(bio, format='PNG') |
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return bio.getvalue() |
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def save_uploaded_file(uploaded_file): |
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with open(uploaded_file.name, "wb") as f: |
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f.write(uploaded_file.getbuffer()) |
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return uploaded_file.name |
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with gr.Blocks() as app: |
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with gr.Row(): |
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with gr.Column(): |
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file_input = gr.File(label="Upload your satellite image") |
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submit_button = gr.Button("Predict") |
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with gr.Column(): |
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image_output = gr.Image(label="Predicted Glacier Boundaries") |
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submit_button.click( |
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fn=lambda x: predict(save_uploaded_file(x)), |
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inputs=file_input, |
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outputs=image_output |
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) |
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if __name__ == "__main__": |
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app.launch() |
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