Update database_recommender.py

database_recommender.py

@@ -4,6 +4,8 @@ from sklearn.neighbors import KNeighborsClassifier
 from sklearn.preprocessing import LabelEncoder, StandardScaler
 import joblib
 import json
+import os
+import requests
 
 class CourseRecommender:
     def __init__(self):
@@ -279,6 +281,47 @@ 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()