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xds
2026-03-16 14:46:20 +03:00
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backend/app/services/coaching.py Normal file
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"""AI Coaching service — onboarding, plan generation, chat."""
import json
import re
from datetime import date, datetime, timedelta
from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession
from backend.app.models.activity import Activity, ActivityMetrics
from backend.app.models.coaching import CoachingChat
from backend.app.models.fitness import FitnessHistory, PowerCurve
from backend.app.models.rider import Rider
from backend.app.models.training import TrainingPlan
from backend.app.services.gemini_client import chat_async
ONBOARDING_SYSTEM = """You are VeloBrain AI Coach — a professional cycling coach.
You are conducting an onboarding interview with a new athlete.
Ask questions ONE AT A TIME, in a friendly conversational tone.
Keep responses short (2-3 sentences + question).
Questions to cover (in rough order):
1. Main cycling goal (fitness, racing, gran fondo, weight loss, etc.)
2. Target event/race (if any) and its date
3. Current weekly training volume (hours/week)
4. How many days per week they can train
5. Which days are available for training
6. Indoor trainer availability (smart trainer, basic, none)
7. Power meter availability
8. Any injuries or health concerns
9. Previous coaching or structured training experience
10. What they enjoy most about cycling
When ALL questions are answered, respond with your summary and then output EXACTLY this marker on a new line:
[ONBOARDING_COMPLETE]
Followed by a JSON block with the structured data:
```json
{
"goal": "...",
"target_event": "...",
"target_event_date": "...",
"hours_per_week": N,
"days_per_week": N,
"available_days": ["monday", ...],
"has_indoor_trainer": true/false,
"trainer_type": "smart/basic/none",
"has_power_meter": true/false,
"injuries": "...",
"coaching_experience": "...",
"enjoys": "..."
}
```
Respond in Russian."""
PLAN_GENERATION_SYSTEM = """You are VeloBrain AI Coach generating a structured training plan.
Based on the rider's profile, current fitness, and goals, create a detailed multi-week training plan.
Output ONLY a valid JSON block with this structure:
```json
{
"goal": "short goal description",
"description": "plan overview in 2-3 sentences",
"phase": "base/build/peak/recovery",
"duration_weeks": N,
"weeks": [
{
"week_number": 1,
"focus": "week focus description",
"target_tss": 300,
"target_hours": 8,
"days": [
{
"day": "monday",
"workout_type": "rest",
"title": "Rest Day",
"description": "",
"duration_minutes": 0,
"target_tss": 0,
"target_if": 0
}
]
}
]
}
```
workout_type options: rest, endurance, tempo, sweetspot, threshold, vo2max, sprint, recovery, race
Plan duration: 4-8 weeks based on goal.
Include progressive overload with recovery weeks every 3-4 weeks.
Adjust intensity based on rider's FTP and experience level.
All descriptions in Russian."""
ADJUSTMENT_SYSTEM = """You are VeloBrain AI Coach reviewing a training plan.
The rider's plan needs adjustment based on their recent performance, compliance, and fatigue.
Analyze the data provided and suggest specific changes to upcoming weeks.
When you've decided on adjustments, output:
[PLAN_ADJUSTED]
Followed by the updated weeks JSON.
Respond in Russian."""
GENERAL_CHAT_SYSTEM = """You are VeloBrain AI Coach — a knowledgeable and supportive cycling coach.
You have access to the rider's training data and can answer questions about:
- Training methodology and periodization
- Nutrition and recovery
- Equipment and bike fit
- Race strategy and pacing
- Interpreting their power/HR data
Be concise, specific, and actionable. Use the rider's actual data when relevant.
Respond in Russian."""
async def build_rider_context(rider: Rider, session: AsyncSession) -> str:
"""Build a concise context string with rider's current state."""
lines = [
f"Rider: {rider.name}",
f"FTP: {rider.ftp or 'not set'} W",
f"Weight: {rider.weight or 'not set'} kg",
f"LTHR: {rider.lthr or 'not set'} bpm",
f"Experience: {rider.experience_level or 'not set'}",
f"Goals: {rider.goals or 'not set'}",
]
if rider.ftp and rider.weight:
lines.append(f"W/kg: {rider.ftp / rider.weight:.2f}")
# Coaching profile
if rider.coaching_profile:
cp = rider.coaching_profile
lines.append(f"\nCoaching Profile:")
for k, v in cp.items():
lines.append(f" {k}: {v}")
# Fitness (latest CTL/ATL/TSB)
fh_query = (
select(FitnessHistory)
.where(FitnessHistory.rider_id == rider.id)
.order_by(FitnessHistory.date.desc())
.limit(1)
)
fh_result = await session.execute(fh_query)
fh = fh_result.scalar_one_or_none()
if fh:
lines.append(f"\nFitness: CTL={fh.ctl:.0f} ATL={fh.atl:.0f} TSB={fh.tsb:.0f}")
# Recent 4 weeks volume
four_weeks_ago = date.today() - timedelta(weeks=4)
vol_query = (
select(
Activity.date,
Activity.duration,
ActivityMetrics.tss,
)
.outerjoin(ActivityMetrics, ActivityMetrics.activity_id == Activity.id)
.where(Activity.rider_id == rider.id)
.where(Activity.date >= four_weeks_ago)
.order_by(Activity.date.desc())
)
vol_result = await session.execute(vol_query)
rides = list(vol_result.all())
if rides:
total_hours = sum(r.duration for r in rides) / 3600
total_tss = sum(float(r.tss or 0) for r in rides)
lines.append(f"\nLast 4 weeks: {len(rides)} rides, {total_hours:.1f}h, TSS={total_tss:.0f}")
lines.append(f"Avg/week: {total_hours / 4:.1f}h, TSS={total_tss / 4:.0f}")
# Personal records (from power curves)
pc_query = (
select(PowerCurve.curve_data)
.join(Activity, Activity.id == PowerCurve.activity_id)
.where(Activity.rider_id == rider.id)
)
pc_result = await session.execute(pc_query)
best: dict[int, int] = {}
for row in pc_result:
for dur_str, power in row.curve_data.items():
dur = int(dur_str)
if dur not in best or power > best[dur]:
best[dur] = power
if best:
pr_strs = []
for dur in sorted(best.keys()):
if dur < 60:
pr_strs.append(f"{dur}s={best[dur]}W")
elif dur < 3600:
pr_strs.append(f"{dur // 60}m={best[dur]}W")
else:
pr_strs.append(f"{dur // 3600}h={best[dur]}W")
lines.append(f"\nPower PRs: {', '.join(pr_strs)}")
# Active plan status
plan_query = (
select(TrainingPlan)
.where(TrainingPlan.rider_id == rider.id)
.where(TrainingPlan.status == "active")
.order_by(TrainingPlan.created_at.desc())
.limit(1)
)
plan_result = await session.execute(plan_query)
plan = plan_result.scalar_one_or_none()
if plan:
lines.append(f"\nActive plan: '{plan.goal}' ({plan.start_date} to {plan.end_date}), phase: {plan.phase}")
return "\n".join(lines)
async def process_chat_message(
rider: Rider,
chat_id,
user_message: str,
session: AsyncSession,
) -> str:
"""Process a user message in a coaching chat and return AI response."""
chat = await session.get(CoachingChat, chat_id)
if not chat or chat.rider_id != rider.id:
raise ValueError("Chat not found")
# Build context
rider_context = await build_rider_context(rider, session)
# Select system prompt
system_prompts = {
"onboarding": ONBOARDING_SYSTEM,
"general": GENERAL_CHAT_SYSTEM,
"adjustment": ADJUSTMENT_SYSTEM,
}
system = system_prompts.get(chat.chat_type, GENERAL_CHAT_SYSTEM)
system = f"{system}\n\n--- Rider Data ---\n{rider_context}"
# Build message history
messages = list(chat.messages_json or [])
messages.append({"role": "user", "text": user_message})
# Call Gemini
gemini_messages = [{"role": m["role"], "text": m["text"]} for m in messages]
response = await chat_async(gemini_messages, system_instruction=system, temperature=0.7)
# Save messages
now = datetime.utcnow().isoformat()
messages_to_save = list(chat.messages_json or [])
messages_to_save.append({"role": "user", "text": user_message, "timestamp": now})
messages_to_save.append({"role": "model", "text": response, "timestamp": now})
chat.messages_json = messages_to_save
# Check for onboarding completion
if chat.chat_type == "onboarding" and "[ONBOARDING_COMPLETE]" in response:
chat.status = "completed"
profile_data = _extract_json(response)
if profile_data:
rider.coaching_profile = profile_data
rider.onboarding_completed = True
if profile_data.get("goal"):
rider.goals = profile_data["goal"]
# Check for plan adjustment
if chat.chat_type == "adjustment" and "[PLAN_ADJUSTED]" in response:
chat.status = "completed"
plan_data = _extract_json(response)
if plan_data:
plan_query = (
select(TrainingPlan)
.where(TrainingPlan.rider_id == rider.id)
.where(TrainingPlan.status == "active")
.order_by(TrainingPlan.created_at.desc())
.limit(1)
)
plan_result = await session.execute(plan_query)
plan = plan_result.scalar_one_or_none()
if plan and "weeks" in plan_data:
current = plan.weeks_json or {}
current["weeks"] = plan_data["weeks"]
plan.weeks_json = current
await session.commit()
return response
async def generate_plan(rider: Rider, session: AsyncSession) -> TrainingPlan:
"""Generate a new training plan using AI."""
rider_context = await build_rider_context(rider, session)
prompt = f"Generate a training plan for this rider.\n\n{rider_context}"
messages = [{"role": "user", "text": prompt}]
response = await chat_async(
messages,
system_instruction=PLAN_GENERATION_SYSTEM,
temperature=0.5,
)
plan_data = _extract_json(response)
if not plan_data or "weeks" not in plan_data:
raise ValueError("Failed to parse plan from AI response")
# Cancel existing active plans
existing_query = (
select(TrainingPlan)
.where(TrainingPlan.rider_id == rider.id)
.where(TrainingPlan.status == "active")
)
existing_result = await session.execute(existing_query)
for old_plan in existing_result.scalars().all():
old_plan.status = "cancelled"
duration_weeks = plan_data.get("duration_weeks", len(plan_data["weeks"]))
start = date.today()
# Align to next Monday
days_until_monday = (7 - start.weekday()) % 7
if days_until_monday == 0:
days_until_monday = 0
start = start + timedelta(days=days_until_monday)
plan = TrainingPlan(
rider_id=rider.id,
goal=plan_data.get("goal", rider.goals or "General fitness"),
start_date=start,
end_date=start + timedelta(weeks=duration_weeks),
phase=plan_data.get("phase", "base"),
weeks_json=plan_data,
description=plan_data.get("description", ""),
status="active",
onboarding_data=rider.coaching_profile,
)
session.add(plan)
await session.commit()
await session.refresh(plan)
return plan
async def get_today_workout(rider: Rider, session: AsyncSession) -> dict | None:
"""Get today's planned workout from the active plan."""
plan_query = (
select(TrainingPlan)
.where(TrainingPlan.rider_id == rider.id)
.where(TrainingPlan.status == "active")
.order_by(TrainingPlan.created_at.desc())
.limit(1)
)
result = await session.execute(plan_query)
plan = result.scalar_one_or_none()
if not plan or not plan.weeks_json:
return None
today = date.today()
if today < plan.start_date or today > plan.end_date:
return None
week_num = (today - plan.start_date).days // 7 + 1
day_name = today.strftime("%A").lower()
weeks = plan.weeks_json.get("weeks", [])
for week in weeks:
if week.get("week_number") == week_num:
for day in week.get("days", []):
if day.get("day") == day_name:
return {
"plan_id": str(plan.id),
"plan_goal": plan.goal,
"week_number": week_num,
"week_focus": week.get("focus", ""),
**day,
}
return None
async def calculate_compliance(plan: TrainingPlan, session: AsyncSession) -> list[dict]:
"""Compare planned vs actual per week."""
if not plan.weeks_json:
return []
weeks = plan.weeks_json.get("weeks", [])
results = []
for week in weeks:
week_num = week.get("week_number", 0)
week_start = plan.start_date + timedelta(weeks=week_num - 1)
week_end = week_start + timedelta(days=7)
# Skip future weeks
if week_start > date.today():
results.append({
"week_number": week_num,
"focus": week.get("focus", ""),
"planned_tss": week.get("target_tss", 0),
"actual_tss": 0,
"planned_hours": week.get("target_hours", 0),
"actual_hours": 0,
"planned_rides": sum(1 for d in week.get("days", []) if d.get("workout_type") != "rest"),
"actual_rides": 0,
"adherence_pct": 0,
"status": "upcoming",
})
continue
# Get actual activities in this week
act_query = (
select(Activity, ActivityMetrics.tss)
.outerjoin(ActivityMetrics, ActivityMetrics.activity_id == Activity.id)
.where(Activity.rider_id == plan.rider_id)
.where(Activity.date >= week_start)
.where(Activity.date < week_end)
)
act_result = await session.execute(act_query)
acts = list(act_result.all())
actual_tss = sum(float(r.tss or 0) for r in acts)
actual_hours = sum(r[0].duration for r in acts) / 3600
actual_rides = len(acts)
planned_rides = sum(1 for d in week.get("days", []) if d.get("workout_type") != "rest")
planned_tss = week.get("target_tss", 0)
adherence = 0
if planned_rides > 0:
adherence = min(100, round(actual_rides / planned_rides * 100))
is_current = week_start <= date.today() < week_end
results.append({
"week_number": week_num,
"focus": week.get("focus", ""),
"planned_tss": planned_tss,
"actual_tss": round(actual_tss, 0),
"planned_hours": week.get("target_hours", 0),
"actual_hours": round(actual_hours, 1),
"planned_rides": planned_rides,
"actual_rides": actual_rides,
"adherence_pct": adherence,
"status": "current" if is_current else "completed",
})
return results
def _extract_json(text: str) -> dict | None:
"""Extract JSON from AI response text."""
# Try to find JSON in code blocks
match = re.search(r"```(?:json)?\s*\n?(.*?)\n?```", text, re.DOTALL)
if match:
try:
return json.loads(match.group(1))
except json.JSONDecodeError:
pass
# Try to find raw JSON object
match = re.search(r"\{[\s\S]*\}", text)
if match:
try:
return json.loads(match.group(0))
except json.JSONDecodeError:
pass
return None