Rename recommender.py to database_recommender.py

recommender.py → database_recommender.py

@@ -4,8 +4,6 @@ from sklearn.neighbors import KNeighborsClassifier
 from sklearn.preprocessing import LabelEncoder, StandardScaler
 import joblib
 import json
-import os
-import requests
 
 class CourseRecommender:
     def __init__(self):
@@ -281,47 +279,6 @@ class CourseRecommender:
         self.scaler = model_data['scaler']
         self.label_encoders = model_data['label_encoders']
 
-
-# ===== UI helper for Hugging Face integration =====
-def get_course_recommendations_ui(recommender: "CourseRecommender", stanine, gwa, strand, hobbies) -> str:
-    if recommender is None:
-        return "Sorry, the recommendation system is not available at the moment. Please try again later."
-    try:
-        try:
-            stanine = int(stanine.strip()) if isinstance(stanine, str) else int(stanine)
-        except (ValueError, TypeError, AttributeError):
-            return "❌ Stanine score must be a valid number between 1 and 9"
-        try:
-            gwa = float(gwa.strip()) if isinstance(gwa, str) else float(gwa)
-        except (ValueError, TypeError, AttributeError):
-            return "❌ GWA must be a valid number between 75 and 100"
-        if not (1 <= stanine <= 9):
-            return "❌ Stanine score must be between 1 and 9"
-        if not (75 <= gwa <= 100):
-            return "❌ GWA must be between 75 and 100"
-        if not strand:
-            return "❌ Please select a strand"
-        if not hobbies or not str(hobbies).strip():
-            return "❌ Please enter your hobbies/interests"
-
-        recommendations = recommender.recommend_courses(
-            stanine=stanine,
-            gwa=gwa,
-            strand=strand,
-            hobbies=str(hobbies)
-        )
-        if not recommendations:
-            return "No recommendations available at the moment."
-        response = f"## 🎯 Course Recommendations for You\n\n"
-        response += f"**Profile:** Stanine {stanine}, GWA {gwa}, {strand} Strand\n"
-        response += f"**Interests:** {hobbies}\n\n"
-        for i, rec in enumerate(recommendations, 1):
-            response += f"### {i}. {rec['code']} - {rec['name']}\n"
-            response += f"**Match Score:** {rec.get('rating', rec.get('probability', 0)):.1f}%\n\n"
-        return response
-    except Exception as e:
-        return f"❌ Error getting recommendations: {str(e)}"
-
 # Example usage
 if __name__ == "__main__":
     recommender = CourseRecommender()