import os from fast_dash import FastDash, UploadImage, Image, Upload import PIL import numpy as np from app import func import tempfile import base64 examples = ["India_900498_S2Hand", "Spain_7370579_S2Hand", "USA_430764_S2Hand"] def detect_water(select_an_example: str = examples, input_tiff_image: Upload = None) -> (Image, Image): """ NASA and IBM recently uploaded their foundation model on Hugging Face, Pritivi, at https://huggingface.co/ibm-nasa-geospatial. This demo, built with Fast Dash, showcases a version of Prithvi that they finetuned to detect water from satellite images. Select an example or upload your own TIFF image. If uploading your own image, read the format details at https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M-sen1floods11. """ # If example is selected if input_tiff_image is None: input_satellite_image = PIL.Image.open(os.path.join("examples", f"{select_an_example}.png")) water_prediction_mask = PIL.Image.open(os.path.join("examples", f"{select_an_example}_result.png")) # If file is uploaded, run inference else: with tempfile.NamedTemporaryFile(delete=False) as f: contents = input_tiff_image.encode("utf8").split(b";base64,")[1] f.write(base64.decodebytes(contents)) input_satellite_image, water_prediction_mask = func(f) input_satellite_image = PIL.Image.fromarray(np.uint8(input_satellite_image)).convert('RGB') water_prediction_mask = PIL.Image.fromarray(np.uint8(water_prediction_mask)).convert('RGB') return input_satellite_image, water_prediction_mask app = FastDash(detect_water, theme="cosmo", port=7860) server = app.server if __name__ == "__main__": app.run()