Update app.py

app.py

@@ -1,6 +1,5 @@
 import gradio as gr
 import torch
-gr.HTML('<div style="text-align: center; margin-bottom: 20px;"><img src="insightray_icon.svg" width="100" style="display: inline-block;"/></div>')
 from PIL import Image
 import torch.nn.functional as F
 from monai.networks.nets import DenseNet121
@@ -85,14 +84,21 @@ def predict(image_input, modality):
         generic_report = get_generic_report(body_part, conf_body_percent)
         return f"**Selected modality:** {modality}\n{generic_report}"
 
-gr.Interface(
-    fn=predict,
-    inputs=[
-        gr.Image(type="pil", label="Upload Medical Image"),
-        gr.Radio(choices=["Chest X‑ray", "Head CT", "Hand X‑ray", "Abdomen CT", "Breast MRI", "Chest CT"],
-                 label="Select Modality", value="Chest X‑ray")
-    ],
-    outputs="text",
-    title="InsightRay – Multi‑Modality Medical AI",
-    description="Choose the image type and upload."
-).launch()
+# ---------- Gradio interface with logo using Blocks ----------
+with gr.Blocks(title="InsightRay – Multi‑Modality Medical AI") as demo:
+    gr.HTML('<div style="text-align: center; margin-bottom: 20px;"><img src="insightray_icon.svg" width="100" style="display: inline-block;"/></div>')
+    gr.Markdown("## InsightRay – Multi‑Modality Medical AI")
+    gr.Markdown("Choose the image type and upload a medical image. For chest X‑ray and head CT, detailed AI reports are generated.")
+    
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="Upload Medical Image")
+            modality = gr.Radio(choices=["Chest X‑ray", "Head CT", "Hand X‑ray", "Abdomen CT", "Breast MRI", "Chest CT"],
+                                label="Select Modality", value="Chest X‑ray")
+            submit_btn = gr.Button("Analyse")
+        with gr.Column():
+            output = gr.Textbox(label="AI Report", lines=20)
+    
+    submit_btn.click(fn=predict, inputs=[image_input, modality], outputs=output)
+
+demo.launch()