Update app.py

app.py

@@ -230,49 +230,45 @@ desc = (
     "If both classifiers fire, the stronger probability is chosen (fallback). Thresholds adjustable."
 )
 
-
-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(
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(
+                type="numpy",
+                label="Upload chest X-ray (RGB or grayscale)"
+            )
+            thresh_b = gr.Slider(0.1, 0.9, value=0.5, step=0.01, label="Bacterial threshold (thresh_b)")
+            thresh_v = gr.Slider(0.1, 0.9, value=0.5, step=0.01, label="Viral threshold (thresh_v)")
+            seg_thresh = gr.Slider(0.1, 0.9, value=0.5, step=0.01, label="Segmentation mask threshold (seg_thresh)")
+
+            submit_btn = gr.Button("Run Inference")
+
+        with gr.Column():
+            pred_label = gr.Label(num_top_classes=1, label="Prediction")
+            prob_b = gr.Number(label="Bacterial Probability")
+            prob_v = gr.Number(label="Viral Probability")
+            masked_img = gr.Image(type="pil", label="Masked Image (input × mask)")
+            seg_overlay = gr.Image(type="pil", label="Segmentation Overlay (red mask)")
+
+    submit_btn.click(
         fn=inference_pipeline,
-        inputs=[
-            gr.Image(type="numpy", label="Upload chest X-ray (RGB or grayscale)"),
-            gr.Slider(minimum=0.1, maximum=0.9, step=0.01, value=0.5,
-                      label="Bacterial threshold (thresh_b)"),
-            gr.Slider(minimum=0.1, maximum=0.9, step=0.01, value=0.5,
-                      label="Viral threshold (thresh_v)"),
-            gr.Slider(minimum=0.1, maximum=0.9, step=0.01, value=0.5,
-                      label="Segmentation mask threshold (seg_thresh)")
-        ],
-        outputs=[
-            gr.Label(num_top_classes=1, label="Prediction"),
-            gr.Number(label="Bacterial Probability"),
-            gr.Number(label="Viral Probability"),
-            gr.Image(type="pil", label="Masked Image (input × mask)"),
-            gr.Image(type="pil", label="Segmentation Overlay (red mask)")
-        ],
-        allow_flagging="never"
+        inputs=[image_input, thresh_b, thresh_v, seg_thresh],
+        outputs=[pred_label, prob_b, prob_v, masked_img, seg_overlay]
     )
 
-    gr.Markdown("### Test Samples")
-
-    gr.Dataframe(
-        headers=["Image", "Label"],
-        value=example_samples,
-        datatype=["image", "str"], 
-        interactive=False,
-        row_count=(len(example_samples), "fixed"),
-        col_count=(2, "fixed")
+    gr.Examples(
+        examples=[
+            ["images/NORMAL.jpeg"],
+            ["images/VIRAL.jpeg"],
+            ["images/BACT.jpeg"],
+        ],
+        inputs=image_input,
+        label="Try Examples"
     )
 
 if __name__ == "__main__":
     demo.launch(share=False)
+