Update app.py

app.py

@@ -3,15 +3,13 @@ import pandas as pd
 import numpy as np
 import tensorflow as tf
 from tensorflow import keras
-from sklearn.preprocessing import StandardScaler, LabelEncoder
-import pickle  # Missing import
+from sklearn.preprocessing import StandardScaler
+import pickle
 
-# Load model and preprocessing objects (replace paths if needed)
-model = keras.models.load_model('model.h5')
+# 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)
-with open('label_encoder.pkl', 'rb') as f:
-    label_encoder = pickle.load(f)
 
 def predict_eligibility(theory_score, practice_score):
     """Predicts eligibility given theory and practice scores."""
@@ -26,7 +24,7 @@ def predict_eligibility(theory_score, practice_score):
         # Predict
         proba = model.predict(scaled_input, verbose=0)[0][0]
         prediction = int(proba > 0.5)
-        class_name = label_encoder.inverse_transform([prediction])[0]
+        class_name = "Eligible" if prediction else "Not eligible"
         
         # Confidence score
         confidence = proba if prediction else (1 - proba)
@@ -35,7 +33,7 @@ def predict_eligibility(theory_score, practice_score):
         return (
             class_name,  # First output: Prediction label
             f"{confidence * 100:.2f}%",  # Second output: Confidence
-            "❌ No" if prediction else "✅  Yes"  # Third output: Eligibility status
+            "✅ Yes" if prediction else "❌ No"  # Third output: Eligibility status
         )
         
     except Exception as e:
@@ -51,7 +49,7 @@ demo = gr.Interface(
         gr.Slider(0, 30, value=0, step=1, label="Practice Score")
     ],
     outputs=[
-        gr.Textbox(label="Prediction"),  # Changed from gr.Label to gr.Textbox
+        gr.Textbox(label="Prediction"),
         gr.Textbox(label="Confidence"),
         gr.Textbox(label="Eligibility Status")
     ],