Update script

fast_dash_app.py

@@ -1,25 +1,41 @@
+import os
 from fast_dash import FastDash, UploadImage, Image, Upload
 import PIL
 import numpy as np
+
 from app import func
 import tempfile
 import base64
 
-def generate_image(input_tiff_image: Upload) -> (Image, Image):
+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"))
 
-    with tempfile.NamedTemporaryFile(delete=False) as f:
-        contents = input_tiff_image.encode("utf8").split(b";base64,")[1]
-        f.write(base64.decodebytes(contents))
+    # 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_image, output_image = func(f)
+        input_satellite_image, water_prediction_mask = func(f)
 
-    # Convert numpy arrays to PIL images
-    input_image = PIL.Image.fromarray(np.uint8(input_image)).convert('RGB')
-    output_image = PIL.Image.fromarray(np.uint8(output_image)).convert('RGB')
+        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_image, output_image
+    return input_satellite_image, water_prediction_mask
 
-app = FastDash(generate_image, port=8000)
+app = FastDash(detect_water, theme="cosmo", port=7860)
 server = app.server
 
 if __name__ == "__main__":

fastapp.py

@@ -1,42 +0,0 @@
-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()