Create app.py

app.py

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+import gradio as gr
+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
+
+# Load model and preprocessing objects (replace paths if needed)
+model = keras.models.load_model('model.h5')
+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."""
+    try:
+        # Preprocess input - create DataFrame to avoid feature name warning
+        input_df = pd.DataFrame({
+            'theory': [theory_score],
+            'practice': [practice_score]
+        })
+        scaled_input = scaler.transform(input_df)
+        
+        # Predict
+        proba = model.predict(scaled_input, verbose=0)[0][0]
+        prediction = int(proba > 0.5)
+        class_name = label_encoder.inverse_transform([prediction])[0]
+        
+        # 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
+            "❌ No" if prediction else "✅  Yes"  # Third output: Eligibility status
+        )
+        
+    except Exception as e:
+        # Return error message for all outputs
+        error_msg = f"Error: {str(e)}"
+        return error_msg, "0%", "❌ Error"
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=predict_eligibility,
+    inputs=[
+        gr.Slider(0, 100, value=50, step=1, label="Theory Score"),
+        gr.Slider(0, 100, value=60, step=1, label="Practice Score")
+    ],
+    outputs=[
+        gr.Textbox(label="Prediction"),  # Changed from gr.Label to gr.Textbox
+        gr.Textbox(label="Confidence"),
+        gr.Textbox(label="Eligibility Status")
+    ],
+    title="🎓 Eligibility Predictor",
+    description="Enter theory and practice scores to check eligibility.",
+    examples=[[85, 90], [45, 60], [70, 75]],
+    theme="soft"
+)
+
+# Launch
+demo.launch(share=True)