Update course_recommender.py

course_recommender.py

@@ -21,6 +21,7 @@ class CourseRecommender:
         self._last_data_count = 0  # Track data count for auto-retraining
         self._auto_retrain_threshold = 5  # Retrain every 5 new feedbacks
         self._min_samples_for_training = 10  # Minimum samples needed to train
+        self._local_feedback = []  # Store feedback locally for learning
         
     def preprocess_data(self, df: pd.DataFrame) -> pd.DataFrame:
         """Preprocess the data for training"""
@@ -123,17 +124,18 @@ class CourseRecommender:
     
     def check_and_auto_retrain(self):
         """Check if enough new data exists and auto-retrain if needed"""
-        current_count = self.get_current_data_count()
+        # Use local feedback count for auto-retraining
+        local_feedback_count = len(self._local_feedback)
         
-        if current_count < self._min_samples_for_training:
-            print(f"Not enough data for training: {current_count} < {self._min_samples_for_training}")
+        if local_feedback_count < self._min_samples_for_training:
+            print(f"Not enough local feedback for training: {local_feedback_count} < {self._min_samples_for_training}")
             return False
         
-        if current_count - self._last_data_count >= self._auto_retrain_threshold:
-            print(f"Auto-retraining triggered: {current_count - self._last_data_count} new feedbacks")
+        if local_feedback_count - self._last_data_count >= self._auto_retrain_threshold:
+            print(f"Auto-retraining triggered: {local_feedback_count - self._last_data_count} new local feedbacks")
             try:
                 accuracy = self.train_model(use_database=True)
-                self._last_data_count = current_count
+                self._last_data_count = local_feedback_count
                 print(f"Auto-retraining completed with accuracy: {accuracy:.3f}")
                 return True
             except Exception as e:
@@ -143,13 +145,27 @@ class CourseRecommender:
         return False
     
     def add_feedback_with_learning(self, course: str, stanine: int, gwa: float, strand: str, 
-                                 rating: int, hobbies: str) -> bool:
+                                 rating: str, hobbies: str) -> bool:
         """Add feedback to database and trigger auto-learning if needed"""
         # Add feedback to database
         success = self.db_connection.add_feedback(course, stanine, gwa, strand, rating, hobbies)
         
         if success:
             print(f"Feedback added for course: {course}")
+            
+            # Store feedback locally for learning (since API has issues)
+            feedback_record = {
+                'course': course,
+                'stanine': stanine,
+                'gwa': gwa,
+                'strand': strand,
+                'rating': rating,
+                'hobbies': hobbies,
+                'count': 1
+            }
+            self._local_feedback.append(feedback_record)
+            print(f"Feedback stored locally for learning: {len(self._local_feedback)} total")
+            
             # Check if we should auto-retrain
             self.check_and_auto_retrain()
         
@@ -327,4 +343,4 @@ class CourseRecommender:
     def add_feedback(self, course: str, stanine: int, gwa: float, strand: str, 
                     rating: int, hobbies: str) -> bool:
         """Add user feedback to the database"""
-        return self.db_connection.add_feedback(course, stanine, gwa, strand, rating, hobbies)
+        return self.db_connection.add_feedback(course, stanine, gwa, strand, rating, hobbies)