Create performance_analyzer.py

performance_analyzer.py

@@ -0,0 +1,56 @@
+import logging
+from typing import Dict, List
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class PerformanceAnalyzer:
+    def __init__(self, db_connection):
+        self.conn = db_connection
+
+    def analyze_user_performance(self, user_id: str) -> Dict:
+        try:
+            cursor = self.conn.cursor()
+            cursor.execute('''
+                SELECT 
+                    topic,
+                    AVG(performance_score) as avg_score,
+                    COUNT(*) as total_sessions
+                FROM study_progress
+                WHERE user_id = ?
+                GROUP BY topic
+            ''', (user_id,))
+            
+            results = cursor.fetchall()
+            return {
+                'performance_by_topic': [dict(r) for r in results],
+                'overall_score': self._calculate_overall_score(results)
+            }
+        except Exception as e:
+            logger.error(f"Erro ao analisar performance: {e}")
+            return {}
+
+    def get_weak_areas(self, user_id: str) -> List[str]:
+        try:
+            cursor = self.conn.cursor()
+            cursor.execute('''
+                SELECT 
+                    topic,
+                    AVG(performance_score) as avg_score
+                FROM study_progress
+                WHERE user_id = ?
+                GROUP BY topic
+                HAVING avg_score < 70
+                ORDER BY avg_score ASC
+            ''', (user_id,))
+            
+            return [row['topic'] for row in cursor.fetchall()]
+        except Exception as e:
+            logger.error(f"Erro ao identificar áreas fracas: {e}")
+            return []
+
+    def _calculate_overall_score(self, results) -> float:
+        if not results:
+            return 0.0
+        total_score = sum(r['avg_score'] for r in results)
+        return round(total_score / len(results), 2)