Update app.py

app.py

@@ -223,96 +223,29 @@ def inference_pipeline(img, thresh_b=0.5, thresh_v=0.5, seg_thresh=0.5):
         blended            
     )
 
-
 title = "Chest X-ray: UNet segmentation + 2 binary classifiers"
-desc = (
-    "Pipeline: UNet -> mask lungs -> two binary classifiers (Normal vs Bacterial, Normal vs Viral). "
-    "If both classifiers fire, the stronger probability is chosen (fallback). Thresholds adjustable."
-)
-
-examples_df = pd.DataFrame({
-    "Image": [
-        "images/NORMAL.jpeg",
-        "images/VIRAL.jpeg",
-        "images/BACT.jpeg",
+desc = "Pipeline: UNet -> mask lungs -> two binary classifiers (Normal vs Bacterial, Normal vs Viral). " \
+       "If both classifiers fire, the stronger probability is chosen (fallback). Thresholds adjustable."
+
+iface = gr.Interface(
+    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)")
     ],
-    "Label": ["NORMAL", "VIRAL", "BACTERIAL"]
-})
-
-with gr.Blocks(title=title) as demo:
-
-    gr.Markdown(f"### {title}")
-    gr.Markdown(desc)
-
-    with gr.Row():
-        with gr.Column(scale=1):
-
-            image_input = gr.Image(
-                type="numpy",
-                label="Upload Chest X-ray"
-            )
-
-            thresh_b = gr.Slider(
-                minimum=0.1, maximum=0.9, step=0.01, value=0.5,
-                label="Bacterial Threshold"
-            )
-
-            thresh_v = gr.Slider(
-                minimum=0.1, maximum=0.9, step=0.01, value=0.5,
-                label="Viral Threshold"
-            )
-
-            seg_thresh = gr.Slider(
-                minimum=0.1, maximum=0.9, step=0.01, value=0.5,
-                label="Segmentation Mask Threshold"
-            )
-
-            with gr.Row():
-                clear_btn = gr.Button("Clear", variant="secondary")
-                submit_btn = gr.Button("Submit", variant="primary")
-
-
-        with gr.Column(scale=1):
-            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")
-            seg_overlay = gr.Image(type="pil", label="Segmentation Overlay")
-
-    submit_btn.click(
-        fn=inference_pipeline,
-        inputs=[image_input, thresh_b, thresh_v, seg_thresh],
-        outputs=[pred_label, prob_b, prob_v, masked_img, seg_overlay]
-    )
-
-    clear_btn.click(
-        fn=lambda: (None, None, None, None, None),
-        inputs=None,
-        outputs=[pred_label, prob_b, prob_v, masked_img, seg_overlay]
-    )
-
-    with gr.Accordion("Try Examples", open=False):
-
-        examples_table = gr.Dataframe(
-            value=examples_df,
-            headers=["Image", "Label"],
-            datatype=["str", "str"],
-            interactive=False,
-            wrap=True
-        )
-
-        load_btn = gr.Button("Load Selected Example")
-
-        def load_example(example_row):
-            if example_row is None or len(example_row) == 0:
-                return None
-            img_path = example_row[0]["Image"]
-            return img_path
+    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)")
+    ],
+    title=title,
+    description=desc,
+    allow_flagging="never"
+)
 
-        load_btn.click(
-            fn=load_example,
-            inputs=examples_table,
-            outputs=image_input
-        )
 if __name__ == "__main__":
-    demo.launch(share=False)
+    iface.launch(share=None)