less miimal dummy example

app.py

@@ -1,13 +1,46 @@
 import gradio as gr
+import numpy as np
+import pandas as pd
+from PIL import Image, ImageDraw
 
-def f(x):
-    return x
+IMAGE_PATH = "output_image.png"
+CSV_PATH = "damage_list.csv"
+
+def dummy_damage_classification(img, threshold):
+    if img is None:
+        return None, None, None
+
+    # Convert to numpy array
+    image_np = np.array(img)
+
+    # Dummy: draw a red square on the image
+    image_pil = Image.fromarray(image_np).convert("RGB")
+    draw = ImageDraw.Draw(image_pil)
+    draw.rectangle([10, 10, 50, 50], outline="red", width=3)
+    image_pil.save(IMAGE_PATH)
+
+    # Dummy CSV content
+    df = pd.DataFrame({"x": [20], "y": [30], "damage_type": ["Dummy"]})
+    df.to_csv(CSV_PATH, index=False)
+
+    return image_pil, IMAGE_PATH, CSV_PATH
 
 with gr.Blocks() as app:
-    inp = gr.Textbox(label="Input")
-    btn = gr.Button("Go")
-    btn.click(fn=f, inputs=inp, outputs=gr.Textbox(label="Out"))
+    gr.Markdown("# Minimal Damage Classifier Demo")
 
-if __name__=="__main__":
-    app.launch()
+    image_input = gr.Image(label="Upload SEM Image")
+    threshold_input = gr.Number(value=20, label="Threshold")
+
+    output_image = gr.Image(label="Classified Image")
+    download_image = gr.File(label="Download Image")
+    download_csv = gr.File(label="Download CSV")
 
+    btn = gr.Button("Run Classification")
+    btn.click(
+        dummy_damage_classification,
+        inputs=[image_input, threshold_input],
+        outputs=[output_image, download_image, download_csv]
+    )
+
+if __name__ == "__main__":
+    app.launch()