Update app

app.py

@@ -4,7 +4,6 @@ from io import BytesIO
 from pathlib import Path
 
 import glob
-import spaces
 import numpy as np
 import gradio as gr
 import rasterio as rio
@@ -18,6 +17,11 @@ rcParams["font.size"] = 9
 rcParams["axes.titlesize"] = 9
 IMG_PX = 300
 
+import sys
+import csv
+
+csv.field_size_limit(sys.maxsize)
+
 EXAMPLES = {
     "EuroSAT": {
         "images": glob.glob("examples/eurosat/*.tif"),
@@ -47,18 +51,6 @@ EXAMPLES = {
 }
 
 
-def load_eurosat_example():
-    return EXAMPLES["EuroSAT"]["images"], ", ".join(EXAMPLES["EuroSAT"]["classes"])
-
-
-def load_meterml_example():
-    return EXAMPLES["Meter-ML"]["images"], ", ".join(EXAMPLES["Meter-ML"]["classes"])
-
-
-def load_terramesh_example():
-    return EXAMPLES["TerraMesh"]["images"], ", ".join(EXAMPLES["TerraMesh"]["classes"])
-
-
 pastel1_hex = [mpl.colors.to_hex(c) for c in mpl.colormaps["Pastel1"].colors]
 
 
@@ -164,8 +156,8 @@ def _bar_chart(top_scores, img_name, cmap) -> str:
     b64 = base64.b64encode(buf.getvalue()).decode()
     return f'<img src="data:image/png;base64,{b64}" style="display:block;margin:auto;width:{IMG_PX}px;" />'
 
-
-@spaces.GPU
+# import spaces
+# @spaces.GPU  # ZeroGPU does not seem to be working
 def classify(images, class_text):
     class_names = [c.strip() for c in class_text.split(",") if c.strip()]
     cards = []
@@ -192,8 +184,25 @@ def classify(images, class_text):
     )
 
 
-# UI
+# Cache examples
+terramesh_html = classify(EXAMPLES["TerraMesh"]["images"], ", ".join(EXAMPLES["TerraMesh"]["classes"]))
+eurosat_html = classify(EXAMPLES["EuroSAT"]["images"], ", ".join(EXAMPLES["EuroSAT"]["classes"]))
+meterml_html = classify(EXAMPLES["Meter-ML"]["images"], ", ".join(EXAMPLES["Meter-ML"]["classes"]))
+
+
+def load_eurosat_example():
+    return EXAMPLES["EuroSAT"]["images"], ", ".join(EXAMPLES["EuroSAT"]["classes"]), eurosat_html
+
+
+def load_meterml_example():
+    return EXAMPLES["Meter-ML"]["images"], ", ".join(EXAMPLES["Meter-ML"]["classes"]), meterml_html
+
+
+def load_terramesh_example():
+    return EXAMPLES["TerraMesh"]["images"], ", ".join(EXAMPLES["TerraMesh"]["classes"]), terramesh_html
+
 
+# UI
 with gr.Blocks(
         css="""
             .gradio-container
@@ -203,7 +212,8 @@ with gr.Blocks(
     gr.Markdown("## Zero‑shot Classification with Llama3-MS‑CLIP")
     gr.Markdown("Provide Sentinel-2 L2A tif files with all 12 bands and define the class names for running zero-shot classification. "
                 "You can also use S-2 L1C files with 13 bands but the model might not work as well (e.g., misclassifing forests as sea because of the differrently scaled values). "
-                "We provide three sets of example images with class names that you can modify. The examples are from [EuroSAT](https://arxiv.org/abs/1709.00029), [Meter-ML](https://arxiv.org/abs/2207.11166), and [TerraMesh](https://arxiv.org/abs/2504.11172) (We downloaded S-2 L2A images for the same locations). "
+                "We provide three sets of example images with class names and cached outputs. "
+                "The examples are from [EuroSAT](https://arxiv.org/abs/1709.00029), [Meter-ML](https://arxiv.org/abs/2207.11166), and [TerraMesh](https://arxiv.org/abs/2504.11172) (We downloaded S-2 L2A images for the same locations). "
                 "The images are classified based on the similarity between the images embeddings and text embeddings. "
                 "You find more information in the [model card](https://huggingface.co/ibm-esa-geospatial/Llama3-MS-CLIP-base) and the [paper](https://arxiv.org/abs/2503.15969). ")
     with gr.Row():
@@ -212,7 +222,6 @@ with gr.Blocks(
         )
         cls_in = gr.Textbox(
             value=", ".join(["Forest", "River", "Buildings", "Agriculture", "Mountain", "Snow"]),
-            # some default classes
             label="Class names (comma‑separated)",
         )
 
@@ -233,29 +242,17 @@ with gr.Blocks(
 
     btn_terramesh.click(
         load_terramesh_example,
-        outputs=[img_in, cls_in],
-    ).then(
-        classify,
-        inputs=[img_in, cls_in],
-        outputs=out_html,
+        outputs=[img_in, cls_in, out_html],
     )
 
     btn_eurosat.click(
         load_eurosat_example,
-        outputs=[img_in, cls_in],
-    ).then(
-        classify,
-        inputs=[img_in, cls_in],
-        outputs=out_html,
+        outputs=[img_in, cls_in, out_html],
     )
 
     btn_meterml.click(
         load_meterml_example,
-        outputs=[img_in, cls_in],
-    ).then(
-        classify,
-        inputs=[img_in, cls_in],
-        outputs=out_html,
+        outputs=[img_in, cls_in, out_html],
     )
 
 if __name__ == "__main__":

requirements.txt

@@ -1,5 +1,3 @@
---extra-index-url https://download.pytorch.org/whl/cu113
-torch
 gradio>=4.31.0
 plotly
 rasterio