Update app.py

app.py

@@ -228,7 +228,16 @@ desc = "Pipeline: UNet -> mask lungs -> two binary classifiers (Normal vs Bacter
        "If both classifiers fire, the stronger probability is chosen (fallback). Thresholds adjustable."
 
 
-with gr.Blocks() as demo:
+example_samples = [
+    ("images/NORMAL.jpeg", "NORMAL"),
+    ("images/VIRAL.jpeg", "VIRAL"),
+    ("images/BACT.jpeg", "BACTERIAL"),
+]
+
+with gr.Blocks(title=title) as demo:
+
+    gr.Markdown(f"### {title}")
+    gr.Markdown(desc)
 
     iface = gr.Interface(
         fn=inference_pipeline,
@@ -245,27 +254,21 @@ with gr.Blocks() as demo:
             gr.Image(type="pil", label="Masked Image (input × mask)"),
             gr.Image(type="pil", label="Segmentation Overlay (red mask)")
         ],
-        title=title,
-        description=desc,
         allow_flagging="never"
     )
 
-    example_samples = [
-        ["images/NORMAL.jpeg", "NORMAL"],
-        ["images/VIRAL.jpeg", "VIRAL"],
-        ["images/BACT.jpeg", "BACTERIAL"],
-    ]
-    gr.Dataset(
-    samples=example_samples,
-    components=[
-        # gr.Image(type="filepath")
-        gr.Markdown(label="True Label")
-    ],
-    headers=["Images", "Label"],
-    label="Test Samples",
-    layout="table"
-)
+    gr.Markdown("### Test Samples")
+
+    # Table Header
+    with gr.Row():
+        gr.Markdown("**Image**")
+        gr.Markdown("**Label**")
 
+    # Table Rows (Images + Labels)
+    for img_path, label in example_samples:
+        with gr.Row():
+            gr.Image(img_path, width=150, show_label=False)
+            gr.Markdown(f"<center>{label}</center>")
 
 if __name__ == "__main__":
     demo.launch(share=False)