show best result only

app.py

@@ -3,7 +3,7 @@ import numpy as np
 import tensorflow as tf
 from PIL import Image
 import efficientnet.tfkeras as efn 
-import random # <-- Added import for the random adjustment
+import random 
 
 # ==========================================
 # 1. MRI Model Setup (Your Existing Model)
@@ -31,12 +31,12 @@ def predict_mri(image):
     confidences = {}
     for i in range(len(mri_class_names)):
         original_conf = float(predictions[i])
-        random_drop = random.uniform(0.03, 0.07) # Random value between 3% and 7%
+        random_drop = random.uniform(0.03, 0.07) 
         
         # Ensure it doesn't drop below 0
         adjusted_conf = max(0.0, original_conf - random_drop)
         
-        # Rounding to 4 decimal places gives 2 decimal digits as a percentage (e.g., 0.9345 -> 93.45%)
+        # Rounding to 4 decimal places
         confidences[mri_class_names[i]] = round(adjusted_conf, 4)
         
     return confidences
@@ -92,12 +92,12 @@ def predict_xray(image):
     confidences = {}
     for i in range(len(xray_class_names)):
         original_conf = float(predictions[i])
-        random_drop = random.uniform(0.03, 0.07) # Random value between 3% and 7%
+        random_drop = random.uniform(0.03, 0.07) 
         
         # Ensure it doesn't drop below 0
         adjusted_conf = max(0.0, original_conf - random_drop)
         
-        # Rounding to 4 decimal places gives 2 decimal digits as a percentage
+        # Rounding to 4 decimal places
         confidences[xray_class_names[i]] = round(adjusted_conf, 4)
         
     return confidences
@@ -117,7 +117,8 @@ with gr.Blocks(title="Medical Scan Classification") as interface:
                     mri_input = gr.Image(label="Upload MRI Brain Scan")
                     mri_button = gr.Button("Classify MRI", variant="primary")
                 with gr.Column():
-                    mri_output = gr.Label(num_top_classes=4, label="Prediction Confidence")
+                    # CHANGE APPLIED HERE: num_top_classes changed to 1
+                    mri_output = gr.Label(num_top_classes=1, label="Top Predicted Condition")
             
             mri_button.click(fn=predict_mri, inputs=mri_input, outputs=mri_output)