Updated demo

app.py

@@ -10,7 +10,7 @@ from functools import partial
 from huggingface_hub import hf_hub_download
 from terratorch.cli_tools import LightningInferenceModel
 
-# pull files from hub
+# pull files from hub-
 token = os.environ.get("HF_TOKEN", None)
 config_path = hf_hub_download(repo_id="ibm-nasa-geospatial/Prithvi-EO-2.0-300M-BurnScars",
                                  filename="burn_scars_config.yaml", token=token)
@@ -97,10 +97,9 @@ def predict_on_images(data_file: str | Path, config_path: str, checkpoint: str):
 
     lightning_model.model.eval()
 
-    channels = [config_dict['data']['init_args']['bands'].index(b) for b in ["RED", "GREEN", "BLUE"]]  # BGR -> RGB
+    channels = [config_dict['data']['init_args']['output_bands'].index(b) for b in ["RED", "GREEN", "BLUE"]]  # BGR -> RGB
 
-    pred = run_model(input_data, temporal_coords, location_coords,
-                     lightning_model.model, lightning_model.datamodule, img_size)
+    pred = run_model(input_data, lightning_model.model, lightning_model.datamodule, img_size)
 
     if input_data.mean() < 1:
         input_data = input_data * 10000  # Scale to 0-10000
@@ -153,7 +152,7 @@ Some example images are provided at the end of this page.
         predicted = gr.Image(image_mode='RGB', show_label=False, show_fullscreen_button=False)
         overlay = gr.Image(image_mode='RGB', show_label=False, show_fullscreen_button=False)
 
-    gr.Markdown(value='\* White = burned; Black = no burned')
+    gr.Markdown(value='\* White = burned; Black = not burned')
 
     btn.click(fn=run_inference,
               inputs=inp_file,
@@ -162,7 +161,7 @@ Some example images are provided at the end of this page.
     with gr.Row():
         gr.Examples(examples=[
             os.path.join(os.path.dirname(__file__), "examples/subsetted_512x512_HLS.S30.T10SEH.2018190.v1.4_merged.tif"),
-            os.path.join(os.path.dirname(__file__), "examples/subsetted_512x512_HLS.S30.T10SFF.2018190.v1.4_merged.tif"),
+            os.path.join(os.path.dirname(__file__), "examples/subsetted_512x512_HLS.S30.T10SFH.2018185.v1.4_merged.tif"),
             os.path.join(os.path.dirname(__file__), "examples/subsetted_512x512_HLS.S30.T10SGF.2020217.v1.4_merged.tif")],
             inputs=inp_file,
                     outputs=[original] + [predicted] + [overlay],