antigravity

src/utils/model_utils.py

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+"""
+Model loading and data preprocessing utilities
+"""
+
+import streamlit as st
+import pandas as pd
+import pickle
+from typing import Dict, Any, List
+
+@st.cache_data
+def load_model_and_info():
+    """Load the trained model and preprocessing information"""
+    try:
+        with open('random_forest_model.pkl', 'rb') as f:
+            model = pickle.load(f)
+        
+        with open('preprocessing_info.pkl', 'rb') as f:
+            preprocessing_info = pickle.load(f)
+            
+        with open('feature_names.pkl', 'rb') as f:
+            feature_names = pickle.load(f)
+            
+        return model, preprocessing_info, feature_names
+    except FileNotFoundError:
+        st.error("❌ Model files not found! Please run 'train_and_save_model.py' first.")
+        st.stop()
+
+def preprocess_input(user_input: Dict[str, Any], preprocessing_info: Dict[str, Any]) -> pd.DataFrame:
+    """Preprocess user input to match the training data format"""
+    
+    # Create a DataFrame with the user input
+    input_df = pd.DataFrame([user_input])
+    
+    # Get categorical columns that need one-hot encoding
+    categorical_columns = [
+        'Marital status', 'Application mode', 'Course', 'Daytime/evening attendance',
+        'Previous qualification', 'Nationality', 'Mothers qualification', 
+        'Fathers qualification', 'Mothers occupation', 'Fathers occupation',
+        'Gender', 'Displaced', 'Educational special needs', 'Debtor', 
+        'Tuition fees up to date', 'Scholarship holder', 'International',
+        'Application order'
+    ]
+    
+    # Perform one-hot encoding for categorical columns
+    for column in categorical_columns:
+        if column in input_df.columns:
+            # Get dummies with prefix
+            dummies = pd.get_dummies(input_df[column], prefix=column)
+            # Concatenate with original data
+            input_df = pd.concat([input_df, dummies], axis=1)
+            # Drop original column
+            input_df.drop(columns=[column], inplace=True)
+    
+    # Ensure all training features are present
+    feature_names = preprocessing_info['feature_names']
+    
+    # Add missing columns with 0 values
+    for feature in feature_names:
+        if feature not in input_df.columns:
+            input_df[feature] = 0
+    
+    # Select only the features used in training and in the same order
+    input_df = input_df[feature_names]
+    
+    return input_df
+
+def validate_input(user_input: Dict[str, Any]) -> List[str]:
+    """Validate user input and return list of issues"""
+    validation_issues = []
+    
+    # Check for unrealistic combinations
+    if user_input['Age at enrollment'] < 17:
+        validation_issues.append("⚠️ Age seems too low for enrollment")
+    
+    if user_input['Curricular units 1st sem (approved)'] > user_input['Curricular units 1st sem (enrolled)']:
+        validation_issues.append("⚠️ Approved units exceed enrolled units (1st sem)")
+    
+    if user_input['Curricular units 2nd sem (approved)'] > user_input['Curricular units 2nd sem (enrolled)']:
+        validation_issues.append("⚠️ Approved units exceed enrolled units (2nd sem)")
+    
+    return validation_issues
+
+def generate_recommendations(predicted_outcome: str, user_input: Dict[str, Any]) -> List[str]:
+    """Generate personalized recommendations based on prediction and input"""
+    recommendations = []
+    
+    if predicted_outcome == "Dropout":
+        if user_input['Curricular units 1st sem (approved)'] == 0:
+            recommendations.append("📚 **Academic Support:** Provide tutoring for struggling courses")
+        
+        if user_input['Scholarship holder'] == 0:
+            recommendations.append("💰 **Financial Aid:** Consider scholarship opportunities")
+        
+        if user_input['Age at enrollment'] > 25:
+            recommendations.append("🕐 **Flexible Scheduling:** Offer evening or part-time options")
+        
+        if user_input['Debtor'] == 1:
+            recommendations.append("💳 **Financial Counseling:** Address outstanding debts")
+    
+    elif predicted_outcome == "Graduate":
+        recommendations.append("🎯 **Success Indicators:** This student shows strong potential for graduation")
+        recommendations.append("🔄 **Continue Support:** Maintain current academic support systems")
+    
+    else:  # Enrolled
+        recommendations.append("📈 **Regular Monitoring:** Schedule periodic academic check-ins")
+        recommendations.append("🎯 **Goal Setting:** Help establish clear academic objectives")
+    
+    return recommendations