feat: add Phase 5 Progression & Analytics — smart suggestions, dashboard, schedule

Add auto-progression engine (ProgressionService) with rep increase, weight
increase, deload, and felt-easy acceleration rules. Add AnalyticsService for
user stats, exercise progress charts, and volume-by-day data. New dashboard
and schedule routes with Chart.js visualizations. Progression badges shown
inline on workout day view. Navigation updated with Dashboard and Schedule links.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
2026-02-24 12:26:23 -06:00
parent e35b78ae87
commit 134542b66f
19 changed files with 1209 additions and 0 deletions

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"""Analytics service for progress dashboard and chart data.
Aggregates workout log data into stats, trends, and chart-ready formats.
"""
import re
from datetime import date, timedelta
import structlog
from sqlmodel import Session, select
from app.models.workout_day import WorkoutDay
from app.models.workout_log import WorkoutLog
from app.models.workout_session import WorkoutSession
logger = structlog.get_logger(__name__)
def _weight_to_float(weight_str: str) -> float:
"""Convert weight string to float for volume calculations.
Args:
weight_str: Weight like '30 lbs' or 'BW'.
Returns:
Numeric weight, or 0.0 for bodyweight.
"""
if not weight_str or weight_str.upper() == "BW":
return 0.0
match = re.search(r"(\d+(?:\.\d+)?)", weight_str)
return float(match.group(1)) if match else 0.0
class AnalyticsService:
"""Aggregates workout data for dashboards and charts.
Args:
session: An active SQLModel Session.
"""
def __init__(self, session: Session) -> None:
self._session = session
def get_user_stats(self, user_id: int) -> dict:
"""Get summary statistics for a user.
Returns:
Dict with keys: total_sessions, total_volume, total_sets,
current_streak, last_workout_date.
"""
sessions = self._session.exec(
select(WorkoutSession)
.where(WorkoutSession.user_id == user_id)
.order_by(WorkoutSession.date.desc())
).all()
total_sessions = len(sessions)
total_volume = 0.0
total_sets = 0
for ws in sessions:
logs = self._session.exec(
select(WorkoutLog).where(WorkoutLog.session_id == ws.id)
).all()
for log_entry in logs:
total_sets += 1
weight = _weight_to_float(log_entry.weight_used)
total_volume += log_entry.reps_completed * weight
current_streak = 0
if sessions:
week_start = date.today() - timedelta(days=date.today().weekday())
for week_offset in range(52):
week_check = week_start - timedelta(weeks=week_offset)
week_end = week_check + timedelta(days=6)
has_session = any(
week_check <= ws.date <= week_end for ws in sessions
)
if has_session:
current_streak += 1
else:
break
last_workout = sessions[0].date if sessions else None
return {
"total_sessions": total_sessions,
"total_volume": round(total_volume),
"total_sets": total_sets,
"current_streak": current_streak,
"last_workout_date": last_workout,
}
def get_exercise_progress(
self, user_id: int, exercise_id: int,
) -> dict:
"""Get chart-ready progress data for a specific exercise.
Returns:
Dict with keys: dates, reps, weights, volumes.
"""
sessions = self._session.exec(
select(WorkoutSession)
.where(WorkoutSession.user_id == user_id)
.order_by(WorkoutSession.date.asc())
).all()
dates = []
reps = []
weights = []
volumes = []
for ws in sessions:
logs = self._session.exec(
select(WorkoutLog).where(
WorkoutLog.session_id == ws.id,
WorkoutLog.exercise_id == exercise_id,
)
).all()
if not logs:
continue
avg_reps = sum(
log_entry.reps_completed for log_entry in logs
) / len(logs)
weight = _weight_to_float(logs[0].weight_used)
session_volume = sum(
log_entry.reps_completed * _weight_to_float(log_entry.weight_used)
for log_entry in logs
)
dates.append(ws.date.isoformat())
reps.append(round(avg_reps, 1))
weights.append(weight)
volumes.append(round(session_volume))
return {
"dates": dates,
"reps": reps,
"weights": weights,
"volumes": volumes,
}
def get_volume_by_day(self, user_id: int) -> dict:
"""Get total volume broken down by workout day.
Returns:
Dict mapping workout day name to total volume.
"""
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)
).all()
volume_by_day = {}
for ws in sessions:
day_name = day_map.get(ws.workout_day_id, "Unknown")
logs = self._session.exec(
select(WorkoutLog).where(WorkoutLog.session_id == ws.id)
).all()
day_volume = sum(
log_entry.reps_completed * _weight_to_float(log_entry.weight_used)
for log_entry in logs
)
volume_by_day[day_name] = (
volume_by_day.get(day_name, 0) + round(day_volume)
)
return volume_by_day

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"""Auto-progression engine for workout programming.
Analyzes workout log history and applies the progression model:
- +1-2 reps/week until wk4 rep target
- +5 lbs every 2 weeks once at rep target
- Deload at week 5 (-20% weight, reset to wk1 reps)
- Accelerated weight increase when all sets felt easy
"""
import re
from datetime import date
from typing import Optional
import structlog
from sqlmodel import Session, select
from app.models.progress_log import ProgressLog
from app.models.user_exercise_program import UserExerciseProgram
from app.models.workout_log import WorkoutLog
from app.models.workout_session import WorkoutSession
logger = structlog.get_logger(__name__)
def _parse_weight(weight_str: str) -> Optional[float]:
"""Extract numeric weight from a string like '30 lbs' or 'BW'.
Args:
weight_str: Weight as a string.
Returns:
Numeric weight in lbs, or None for bodyweight.
"""
if not weight_str or weight_str.upper() == "BW":
return None
match = re.search(r"(\d+(?:\.\d+)?)", weight_str)
return float(match.group(1)) if match else None
def _format_weight(weight_lbs: Optional[float]) -> str:
"""Format a numeric weight back to a display string.
Args:
weight_lbs: Weight in lbs, or None for bodyweight.
Returns:
Formatted string like '35 lbs' or 'BW'.
"""
if weight_lbs is None:
return "BW"
if weight_lbs == int(weight_lbs):
return f"{int(weight_lbs)} lbs"
return f"{weight_lbs:.1f} lbs"
class ProgressionService:
"""Implements the auto-progression engine.
Args:
session: An active SQLModel Session.
"""
def __init__(self, session: Session) -> None:
self._session = session
def _get_program(
self, user_id: int, exercise_id: int,
) -> Optional[UserExerciseProgram]:
"""Look up the user's program for a specific exercise."""
statement = select(UserExerciseProgram).where(
UserExerciseProgram.user_id == user_id,
UserExerciseProgram.exercise_id == exercise_id,
)
return self._session.exec(statement).first()
def _get_recent_sessions(
self, user_id: int, exercise_id: int, limit: int = 5,
) -> list[dict]:
"""Get recent session summaries for an exercise.
Returns a list of dicts with: date, avg_reps, weight, all_felt_easy.
"""
statement = (
select(WorkoutSession)
.where(WorkoutSession.user_id == user_id)
.order_by(WorkoutSession.date.desc())
.limit(limit * 2)
)
sessions = self._session.exec(statement).all()
results = []
for ws in sessions:
logs = self._session.exec(
select(WorkoutLog).where(
WorkoutLog.session_id == ws.id,
WorkoutLog.exercise_id == exercise_id,
)
).all()
if not logs:
continue
avg_reps = sum(log.reps_completed for log in logs) / len(logs)
weight = logs[0].weight_used
all_felt_easy = all(log.felt_easy for log in logs)
results.append({
"date": ws.date,
"avg_reps": avg_reps,
"weight": weight,
"all_felt_easy": all_felt_easy,
"set_count": len(logs),
})
if len(results) >= limit:
break
return results
def get_suggestion(
self, user_id: int, exercise_id: int,
) -> dict:
"""Generate a progression suggestion for the next workout.
Analyzes recent log history against the user's program targets
and applies progression rules.
Returns:
Dict with keys: suggested_reps, suggested_weight,
progression_type, message.
"""
program = self._get_program(user_id, exercise_id)
if program is None:
return {
"suggested_reps": 0,
"suggested_weight": "",
"progression_type": "no_program",
"message": "No program found for this exercise.",
}
try:
wk1_reps = int(program.wk1_reps)
wk4_reps = int(program.wk4_reps)
except (ValueError, TypeError):
wk1_reps = 0
wk4_reps = 0
wk1_weight = program.wk1_weight
recent = self._get_recent_sessions(user_id, exercise_id, limit=5)
if not recent:
return {
"suggested_reps": wk1_reps,
"suggested_weight": wk1_weight,
"progression_type": "baseline",
"message": f"Start with {wk1_reps} reps @ {wk1_weight}.",
}
latest = recent[0]
current_reps = int(round(latest["avg_reps"]))
current_weight = latest["weight"]
current_weight_num = _parse_weight(current_weight)
consecutive_sessions = len(recent)
# Rule: Deload at week 5 (4 consecutive sessions completed)
if consecutive_sessions >= 4:
if current_weight_num is not None:
deload_weight = current_weight_num * 0.8
return {
"suggested_reps": wk1_reps,
"suggested_weight": _format_weight(deload_weight),
"progression_type": "deload",
"message": (
f"Deload week: {wk1_reps} reps @ "
f"{_format_weight(deload_weight)} (-20%)."
),
}
return {
"suggested_reps": wk1_reps,
"suggested_weight": current_weight,
"progression_type": "deload",
"message": f"Deload week: reset to {wk1_reps} reps.",
}
# Rule: Weight increase if at rep target and felt easy
if current_reps >= wk4_reps and latest["all_felt_easy"]:
if current_weight_num is not None:
new_weight = current_weight_num + 5
return {
"suggested_reps": wk1_reps,
"suggested_weight": _format_weight(new_weight),
"progression_type": "weight_increase",
"message": (
f"Weight up: {wk1_reps} reps @ "
f"{_format_weight(new_weight)} (+5 lbs)."
),
}
# Rule: Weight increase after 2 weeks at rep target
if (
current_reps >= wk4_reps
and len(recent) >= 2
and int(round(recent[1]["avg_reps"])) >= wk4_reps
):
if current_weight_num is not None:
new_weight = current_weight_num + 5
return {
"suggested_reps": wk1_reps,
"suggested_weight": _format_weight(new_weight),
"progression_type": "weight_increase",
"message": (
f"2 weeks at target: {wk1_reps} reps @ "
f"{_format_weight(new_weight)} (+5 lbs)."
),
}
# Rule: Rep increase (+1-2 reps)
if current_reps < wk4_reps:
increment = 2 if latest["all_felt_easy"] else 1
new_reps = min(current_reps + increment, wk4_reps)
return {
"suggested_reps": new_reps,
"suggested_weight": current_weight,
"progression_type": "reps_increase",
"message": (
f"Reps up: {new_reps} reps @ {current_weight} "
f"(+{increment})."
),
}
# Hold: at target, waiting for biweekly weight increase
return {
"suggested_reps": current_reps,
"suggested_weight": current_weight,
"progression_type": "hold",
"message": f"Hold at {current_reps} reps @ {current_weight}.",
}
def record_progression(
self,
user_id: int,
exercise_id: int,
suggested_reps: int,
suggested_weight: str,
actual_reps: int,
actual_weight: str,
progression_type: str,
) -> ProgressLog:
"""Record a progression entry in the progress_log table."""
progress_log = ProgressLog(
user_id=user_id,
exercise_id=exercise_id,
date=date.today(),
suggested_reps=suggested_reps,
suggested_weight=suggested_weight,
actual_reps=actual_reps,
actual_weight=actual_weight,
progression_applied=progression_type,
)
self._session.add(progress_log)
self._session.commit()
self._session.refresh(progress_log)
logger.info(
"progression_recorded",
user_id=user_id,
exercise_id=exercise_id,
type=progression_type,
)
return progress_log