adjust result screen

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 import numpy as np
 import tensorflow as tf
 from PIL import Image
-import efficientnet.tfkeras as efn # <-- Add this import
+import efficientnet.tfkeras as efn 
 
 # ==========================================
 # 1. MRI Model Setup (Your Existing Model)
@@ -28,7 +28,6 @@ def predict_mri(image):
     confidences = {mri_class_names[i]: float(predictions[i]) for i in range(len(mri_class_names))}
     return confidences
 
-
 # ==========================================
 # 2. X-Ray Model Setup (Using original EfficientNet library)
 # ==========================================
@@ -84,7 +83,6 @@ def predict_xray(image):
     confidences = {xray_class_names[i]: float(predictions[i]) for i in range(len(xray_class_names))}
     return confidences
 
-
 # ==========================================
 # 3. Define the Gradio Interface with Tabs
 # ==========================================
@@ -111,7 +109,8 @@ with gr.Blocks(title="Medical Scan Classification") as interface:
                     xray_input = gr.Image(label="Upload Chest X-Ray")
                     xray_button = gr.Button("Classify X-Ray", variant="primary")
                 with gr.Column():
-                    xray_output = gr.Label(num_top_classes=5, label="Top 5 Predicted Conditions")
+                    # CHANGE APPLIED HERE: num_top_classes changed to 2, and label updated
+                    xray_output = gr.Label(num_top_classes=2, label="Top 2 Predicted Conditions")
                     
             xray_button.click(fn=predict_xray, inputs=xray_input, outputs=xray_output)