Update app.py

app.py

@@ -4,17 +4,27 @@ import numpy as np
 import tensorflow as tf
 from tensorflow import keras
 from sklearn.preprocessing import StandardScaler
-import pickle
+import joblib  # Changed from pickle to joblib
+import os
+import warnings
 
-# Load model and scaler (replace paths if needed)
-model = keras.models.load_model('eligibility_predictor.h5')  # Changed to match your saved model name
-with open('scaler.pkl', 'rb') as f:
-    scaler = pickle.load(f)
+# Suppress TensorFlow and CUDA warnings
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+warnings.filterwarnings('ignore')
+tf.get_logger().setLevel('ERROR')
+
+# Load model and scaler
+try:
+    model = keras.models.load_model('eligibility_predictor.h5')
+    scaler = joblib.load('scaler.pkl')  # Using joblib instead of pickle
+except Exception as e:
+    print(f"Error loading model/scaler: {e}")
+    raise
 
 def predict_eligibility(theory_score, practice_score):
     """Predicts eligibility given theory and practice scores."""
     try:
-        # Preprocess input - create DataFrame to avoid feature name warning
+        # Preprocess input
         input_df = pd.DataFrame({
             'theory': [theory_score],
             'practice': [practice_score]
@@ -29,15 +39,13 @@ def predict_eligibility(theory_score, practice_score):
         # Confidence score
         confidence = proba if prediction else (1 - proba)
         
-        # Return 3 separate values (not a dictionary)
         return (
-            class_name,  # First output: Prediction label
-            f"{confidence * 100:.2f}%",  # Second output: Confidence
-            "✅ Yes" if prediction else "❌ No"  # Third output: Eligibility status
+            class_name,
+            f"{confidence * 100:.2f}%",
+            "✅ Yes" if prediction else "❌ No"
         )
         
     except Exception as e:
-        # Return error message for all outputs
         error_msg = f"Error: {str(e)}"
         return error_msg, "0%", "❌ Error"
 
@@ -55,9 +63,9 @@ demo = gr.Interface(
     ],
     title="🎓 Eligibility Predictor",
     description="Enter theory and practice scores to check eligibility.",
-    examples=[[85, 90], [45, 60], [70, 75]],
+    examples=[[10, 15], [5, 10], [15, 25]],  # Adjusted examples to match your slider ranges
     theme="soft"
 )
 
 # Launch
-demo.launch(share=True)
+demo.launch()