Update app.py

app.py

@@ -228,44 +228,47 @@ desc = "Pipeline: UNet -> mask lungs -> two binary classifiers (Normal vs Bacter
        "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)")
-    ],
-    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"
-)
-gr.Markdown("### Example Images with True Labels")
-
-example_samples = [
-    ["images/NORMAL.jpeg", "NORMAL"],
-    ["images/VIRAL.jpeg", "VIRAL"],
-    ["images/BACT.jpeg", "BACTERIAL"],
-]
-
-gr.Dataset(
-    samples=example_samples,
-    components=[
-        gr.Image(type="filepath", label="Image"),
-        gr.Textbox(label="True Label")
-    ],
-    headers=["Image", "Label"],
-    label="Test Samples",
-    layout="table"
-)
+with gr.Blocks() as demo:
+
+    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)")
+        ],
+        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"
+    )
+
+
+    gr.Markdown("### Example Images with True Labels")
+
+    example_samples = [
+        ["images/NORMAL.jpg", "NORMAL"],
+        ["images/VIRAL.jpg", "VIRAL"],
+        ["images/BACT.jpg", "BACTERIAL"],
+    ]
+
+    gr.Dataset(
+        samples=example_samples,
+        components=[
+            gr.Image(type="filepath", label="Image"),
+            gr.Textbox(label="True Label")
+        ],
+        headers=["Image", "Label"],
+        label="Test Samples",
+        layout="table"
+    )
 
 if __name__ == "__main__":
-    iface.launch(share=False)
+    demo.launch(share=False)