feat: enhance dashboard with PRs, adherence, activity, progression chart, and muscle heatmap

Add 3 new stat cards (Last Workout, Personal Records, Adherence Rate),
recent activity table, progression timeline chart, and muscle group
recency heatmap to the dashboard. Remove Total Volume card.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

app/services/analytics_service.py

@@ -9,6 +9,8 @@ from datetime import date, timedelta
 import structlog
 from sqlmodel import Session, select
 
+from app.models.exercise import Exercise
+from app.models.progress_log import ProgressLog
 from app.models.workout_day import WorkoutDay
 from app.models.workout_log import WorkoutLog
 from app.models.workout_session import WorkoutSession
@@ -179,3 +181,197 @@ class AnalyticsService:
             )
 
         return volume_by_day
+
+    def get_personal_records(self, user_id: int) -> list[dict]:
+        """Get per-exercise max weight records for a user.
+
+        Returns:
+            List of dicts with exercise_name, weight, weight_display, date.
+            Sorted by weight descending. BW-only exercises excluded.
+        """
+        sessions = self._session.exec(
+            select(WorkoutSession)
+            .where(WorkoutSession.user_id == user_id)
+        ).all()
+
+        # Map exercise_id -> {max_weight, weight_str, date, exercise_name}
+        records: dict[int, dict] = {}
+        for ws in sessions:
+            logs = self._session.exec(
+                select(WorkoutLog).where(WorkoutLog.session_id == ws.id)
+            ).all()
+            for log_entry in logs:
+                weight = _weight_to_float(log_entry.weight_used)
+                if weight == 0.0:
+                    continue
+                existing = records.get(log_entry.exercise_id)
+                if existing is None or weight > existing["weight"]:
+                    records[log_entry.exercise_id] = {
+                        "exercise_id": log_entry.exercise_id,
+                        "weight": weight,
+                        "weight_display": log_entry.weight_used,
+                        "date": ws.date,
+                    }
+
+        # Resolve exercise names
+        result = []
+        for exercise_id, rec in records.items():
+            exercise = self._session.get(Exercise, exercise_id)
+            if exercise:
+                rec["exercise_name"] = exercise.name
+                result.append(rec)
+
+        result.sort(key=lambda r: r["weight"], reverse=True)
+        return result
+
+    def get_adherence_rate(self, user_id: int, weeks: int = 8) -> dict:
+        """Calculate workout adherence rate over the past N weeks.
+
+        Returns:
+            Dict with rate (0-100), completed, expected, weeks.
+        """
+        cutoff = date.today() - timedelta(weeks=weeks)
+        sessions = self._session.exec(
+            select(WorkoutSession)
+            .where(
+                WorkoutSession.user_id == user_id,
+                WorkoutSession.date >= cutoff,
+            )
+        ).all()
+
+        # Only count sessions with logs
+        completed = 0
+        for ws in sessions:
+            logs = self._session.exec(
+                select(WorkoutLog).where(WorkoutLog.session_id == ws.id)
+            ).all()
+            if logs:
+                completed += 1
+
+        expected = weeks * 4
+        rate = round((completed / expected) * 100) if expected > 0 else 0
+        return {
+            "rate": min(rate, 100),
+            "completed": completed,
+            "expected": expected,
+            "weeks": weeks,
+        }
+
+    def get_muscle_group_recency(self, user_id: int) -> list[dict]:
+        """Get the most recent workout date for each muscle group.
+
+        Returns:
+            List of dicts with muscle_group, last_worked, days_ago.
+            Sorted by days_ago descending (most stale first).
+        """
+        exercises = self._session.exec(select(Exercise)).all()
+        muscle_groups = {e.muscle_group for e in exercises if e.muscle_group}
+
+        # Map exercise_id -> muscle_group for fast lookup
+        ex_muscle = {e.id: e.muscle_group for e in exercises}
+
+        sessions = self._session.exec(
+            select(WorkoutSession)
+            .where(WorkoutSession.user_id == user_id)
+            .order_by(WorkoutSession.date.desc())
+        ).all()
+
+        recency: dict[str, date] = {}
+        for ws in sessions:
+            logs = self._session.exec(
+                select(WorkoutLog).where(WorkoutLog.session_id == ws.id)
+            ).all()
+            for log_entry in logs:
+                mg = ex_muscle.get(log_entry.exercise_id, "")
+                if mg and (mg not in recency or ws.date > recency[mg]):
+                    recency[mg] = ws.date
+
+        today = date.today()
+        result = []
+        for mg in sorted(muscle_groups):
+            last_worked = recency.get(mg)
+            days_ago = (today - last_worked).days if last_worked else None
+            result.append({
+                "muscle_group": mg,
+                "last_worked": last_worked,
+                "days_ago": days_ago,
+            })
+
+        # Sort: never-worked first, then most stale
+        result.sort(
+            key=lambda r: (r["days_ago"] is None, -(r["days_ago"] or 0)),
+            reverse=True,
+        )
+        return result
+
+    def get_recent_activity(self, user_id: int, limit: int = 5) -> list[dict]:
+        """Get the last N workout sessions with summary data.
+
+        Returns:
+            List of dicts with date, workout_day_name, total_volume, total_sets.
+        """
+        days = self._session.exec(select(WorkoutDay)).all()
+        day_map = {d.id: d.name for d in days}
+
+        sessions = self._session.exec(
+            select(WorkoutSession)
+            .where(WorkoutSession.user_id == user_id)
+            .order_by(WorkoutSession.date.desc())
+        ).all()
+
+        result = []
+        for ws in sessions:
+            logs = self._session.exec(
+                select(WorkoutLog).where(WorkoutLog.session_id == ws.id)
+            ).all()
+            if not logs:
+                continue
+
+            total_volume = sum(
+                log_entry.reps_completed * _weight_to_float(log_entry.weight_used)
+                for log_entry in logs
+            )
+            result.append({
+                "date": ws.date,
+                "workout_day_name": day_map.get(ws.workout_day_id, "Unknown"),
+                "total_volume": round(total_volume),
+                "total_sets": len(logs),
+            })
+            if len(result) >= limit:
+                break
+
+        return result
+
+    def get_progression_timeline(self, user_id: int) -> dict:
+        """Get progression history for Chart.js multi-line chart.
+
+        Returns:
+            Dict with 'exercises' key mapping exercise names to
+            {dates, weights, events} lists.
+        """
+        logs = self._session.exec(
+            select(ProgressLog)
+            .where(ProgressLog.user_id == user_id)
+            .order_by(ProgressLog.date.asc())
+        ).all()
+
+        exercises: dict[int, list] = {}
+        for pl in logs:
+            exercises.setdefault(pl.exercise_id, []).append(pl)
+
+        result = {}
+        for exercise_id, entries in exercises.items():
+            exercise = self._session.get(Exercise, exercise_id)
+            if not exercise:
+                continue
+            name = exercise.name
+            result[name] = {
+                "dates": [e.date.isoformat() for e in entries],
+                "weights": [
+                    _weight_to_float(e.actual_weight or e.suggested_weight or "0")
+                    for e in entries
+                ],
+                "events": [e.progression_applied or "" for e in entries],
+            }
+
+        return {"exercises": result}