feat: implement background LLM pattern analysis on Insights screen

Adds a prompt builder that serialises 7-day mood/activity data, wires InsightNotifier to call the on-device LLM directly (no user interaction), caches results by prompt hash, and renders the analysis as a passive card. Includes loading, empty (<2 entries), and error states with a force-refresh button.
2026-04-26 14:37:37 +10:00
parent d1b18c5296
commit ea412d6fe9
4 changed files with 322 additions and 48 deletions
@@ -0,0 +1,56 @@
+import '../database/daos/mood_dao.dart';
+
+abstract class PromptBuilder {
+  static String buildWeeklyPrompt(List<MoodEntryWithActivities> entries) {
+    final lines = entries.map(_entryLine).join('\n');
+    return '''
+You are a personal wellbeing assistant analysing a private mood journal.
+Analyse the following data and identify 2–3 patterns or correlations across mood, sleep, energy, positivity, and self-worth.
+End with one specific, actionable suggestion. Be concise (4–6 sentences total). Do not greet the user.
+
+$lines''';
+  }
+
+  static String _entryLine(MoodEntryWithActivities e) {
+    final dt = e.entry.timestamp;
+    final date =
+        '${dt.year}-${dt.month.toString().padLeft(2, '0')}-${dt.day.toString().padLeft(2, '0')}';
+    final time =
+        '${dt.hour.toString().padLeft(2, '0')}:${dt.minute.toString().padLeft(2, '0')}';
+
+    final parts = <String>[
+      '$date $time',
+      'Mood ${e.entry.moodLevel}/5',
+    ];
+
+    if (e.entry.sleepMinutes != null) {
+      final h = e.entry.sleepMinutes! ~/ 60;
+      final m = (e.entry.sleepMinutes! % 60).toString().padLeft(2, '0');
+      parts.add('Sleep ${h}h$m');
+    }
+    if (e.entry.energyLevel != null) {
+      parts.add('Energy ${_scale(e.entry.energyLevel!, _energyLabels)}');
+    }
+    if (e.entry.positivityLevel != null) {
+      parts.add('Positivity ${_scale(e.entry.positivityLevel!, _positivityLabels)}');
+    }
+    if (e.entry.selfWorthLevel != null) {
+      parts.add('Self-worth ${_scale(e.entry.selfWorthLevel!, _selfWorthLabels)}');
+    }
+    if (e.activities.isNotEmpty) {
+      parts.add('Activities: ${e.activities.map((a) => a.name).join(', ')}');
+    }
+    if (e.entry.note != null && e.entry.note!.isNotEmpty) {
+      parts.add('Note: ${e.entry.note}');
+    }
+
+    return parts.join(' | ');
+  }
+
+  static String _scale(int level, List<String> labels) =>
+      labels[(level - 1).clamp(0, labels.length - 1)];
+
+  static const _energyLabels = ['very low', 'low', 'ok', 'high', 'very high'];
+  static const _positivityLabels = ['very negative', 'negative', 'neutral', 'positive', 'very positive'];
+  static const _selfWorthLabels = ['very low', 'low', 'moderate', 'high', 'very high'];
+}