Add min/max filter filter for exercise sparkline graph

db.py

@@ -494,7 +494,7 @@ class DataBase():
         start_dates = [t['start_date'] for t in topsets]
         messages = [f'{t["repetitions"]} x {t["weight"]}kg ({t["estimated_1rm"]}kg E1RM) on {t["start_date"].strftime("%d %b %y")}' for t in topsets]
 
-        exercise_progress = get_exercise_graph_model(topsets[0]['exercise_name'], estimated_1rm, repetitions, weight, start_dates, messages, epoch, person_id, exercise_id)
+        exercise_progress = get_exercise_graph_model(topsets[0]['exercise_name'], estimated_1rm, repetitions, weight, start_dates, messages, epoch, person_id, exercise_id, min_date, max_date)
 
         return exercise_progress
     
@@ -546,4 +546,30 @@ class DataBase():
 
         # Return a tuple containing the person's name and their workout notes
         return (person_name, workout_notes_list)
+    
+    def get_exercise_earliest_and_latest_dates(self, person_id, exercise_id):
+        sql_query = """
+        SELECT 
+            w.start_date 
+        FROM workout w
+        INNER JOIN topset t on w.workout_id = t.workout_id
+        INNER JOIN exercise e on t.exercise_id = e.exercise_id
+        WHERE w.person_id = %s AND e.exercise_id = %s
+        ORDER BY w.start_date DESC;
+        """
+
+        # Execute the SQL query
+        workout_exercise_dates = self.execute(sql_query, [person_id, exercise_id])
+
+        if not workout_exercise_dates:
+            return None, None
+
+        latest_date = workout_exercise_dates[0]['start_date']
+        earliest_date = workout_exercise_dates[-1]['start_date']
+
+        return earliest_date, latest_date
+
+
+
+