added RAG, Multiuser, TG bot
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"""Weekly fitness chart data and least-squares trend lines."""
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from __future__ import annotations
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from collections import defaultdict
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from datetime import date, datetime, timedelta, timezone
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from typing import Any
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from sqlalchemy import select
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from sqlalchemy.orm import Session
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from app.db.models import BodyMetric, FoodLog, StepLog, WaterLog
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METRIC_DEFS: dict[str, dict[str, str]] = {
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"weight_kg": {"label": "Вес", "unit": "кг"},
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"body_fat_pct": {"label": "Жир", "unit": "%"},
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"calories": {"label": "Калории", "unit": "ккал/день"},
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"protein_g": {"label": "Белок", "unit": "г/день"},
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"water_l": {"label": "Вода", "unit": "л/день"},
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"steps": {"label": "Шаги", "unit": "шаг/день"},
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}
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def week_start(day: date) -> date:
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return day - timedelta(days=day.weekday())
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def linear_regression(points: list[tuple[float, float]]) -> dict[str, float] | None:
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"""Ordinary least squares y = slope * x + intercept."""
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n = len(points)
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if n < 2:
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return None
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sum_x = sum(x for x, _ in points)
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sum_y = sum(y for _, y in points)
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sum_xx = sum(x * x for x, _ in points)
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sum_xy = sum(x * y for x, y in points)
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denom = n * sum_xx - sum_x * sum_x
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if abs(denom) < 1e-12:
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return None
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slope = (n * sum_xy - sum_x * sum_y) / denom
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intercept = (sum_y - slope * sum_x) / n
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return {"slope": slope, "intercept": intercept}
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def _avg(values: list[float]) -> float | None:
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if not values:
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return None
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return sum(values) / len(values)
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def _last(values: list[tuple[date, float]]) -> float | None:
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if not values:
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return None
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values.sort(key=lambda item: item[0])
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return values[-1][1]
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def build_fitness_charts(
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db: Session,
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user_id: int,
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*,
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weeks: int = 52,
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trend: bool = True,
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end_day: date | None = None,
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) -> dict[str, Any]:
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weeks = max(4, min(int(weeks), 52))
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end = end_day or datetime.now(timezone.utc).date()
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last_week_start = week_start(end)
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first_week_start = last_week_start - timedelta(weeks=weeks - 1)
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range_start = datetime.combine(first_week_start, datetime.min.time(), tzinfo=timezone.utc)
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range_end = datetime.combine(end, datetime.max.time(), tzinfo=timezone.utc)
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daily: dict[date, dict[str, float]] = defaultdict(lambda: {
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"calories": 0.0,
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"protein_g": 0.0,
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"fat_g": 0.0,
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"carbs_g": 0.0,
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"water_ml": 0.0,
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"steps": 0.0,
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})
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daily_flags: dict[date, set[str]] = defaultdict(set)
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foods = db.scalars(
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select(FoodLog).where(
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FoodLog.user_id == user_id,
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FoodLog.logged_at >= range_start,
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FoodLog.logged_at <= range_end,
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)
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).all()
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for row in foods:
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d = row.logged_at.date()
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daily[d]["calories"] += row.calories
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daily[d]["protein_g"] += row.protein_g
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daily[d]["fat_g"] += row.fat_g
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daily[d]["carbs_g"] += row.carbs_g
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daily_flags[d].add("nutrition")
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waters = db.scalars(
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select(WaterLog).where(
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WaterLog.user_id == user_id,
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WaterLog.logged_at >= range_start,
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WaterLog.logged_at <= range_end,
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)
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).all()
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for row in waters:
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d = row.logged_at.date()
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daily[d]["water_ml"] += float(row.amount_ml)
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daily_flags[d].add("water")
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steps_rows = db.scalars(
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select(StepLog).where(
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StepLog.user_id == user_id,
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StepLog.logged_at >= range_start,
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StepLog.logged_at <= range_end,
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)
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).all()
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for row in steps_rows:
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d = row.logged_at.date()
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daily[d]["steps"] += float(row.steps)
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daily_flags[d].add("steps")
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body_rows = db.scalars(
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select(BodyMetric).where(
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BodyMetric.user_id == user_id,
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BodyMetric.recorded_at >= range_start,
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BodyMetric.recorded_at <= range_end,
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)
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).all()
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body_by_day: dict[date, list[tuple[date, float, float | None]]] = defaultdict(list)
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for row in body_rows:
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d = row.recorded_at.date()
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body_by_day[d].append((d, row.weight_kg, row.body_fat_pct))
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daily_flags[d].add("body")
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week_slots: list[dict[str, Any]] = []
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cursor = first_week_start
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while cursor <= last_week_start:
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week_slots.append(
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{
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"week_start": cursor.isoformat(),
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"week_end": (cursor + timedelta(days=6)).isoformat(),
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}
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)
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cursor += timedelta(weeks=1)
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days_with_data = len(daily_flags)
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weeks_with_data = 0
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def rollup_week(metric: str) -> list[dict[str, Any]]:
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nonlocal weeks_with_data
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points: list[dict[str, Any]] = []
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local_weeks_with_data = 0
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for idx, slot in enumerate(week_slots):
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ws = date.fromisoformat(slot["week_start"])
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we = date.fromisoformat(slot["week_end"])
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day_cursor = ws
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week_daily_values: list[float] = []
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week_body_weight: list[tuple[date, float]] = []
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week_body_fat: list[tuple[date, float]] = []
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while day_cursor <= we:
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if day_cursor > end:
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break
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flags = daily_flags.get(day_cursor, set())
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totals = daily.get(day_cursor)
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if metric == "weight_kg":
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for _, w, _ in body_by_day.get(day_cursor, []):
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week_body_weight.append((day_cursor, w))
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elif metric == "body_fat_pct":
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for _, _, bf in body_by_day.get(day_cursor, []):
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if bf is not None:
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week_body_fat.append((day_cursor, bf))
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elif metric == "calories" and totals and "nutrition" in flags:
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week_daily_values.append(totals["calories"])
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elif metric == "protein_g" and totals and "nutrition" in flags:
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week_daily_values.append(totals["protein_g"])
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elif metric == "water_l" and totals and "water" in flags:
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week_daily_values.append(totals["water_ml"] / 1000.0)
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elif metric == "steps" and totals and "steps" in flags:
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week_daily_values.append(totals["steps"])
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day_cursor += timedelta(days=1)
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value: float | None
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days_in_week = 0
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if metric == "weight_kg":
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value = _last(week_body_weight)
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days_in_week = len(week_body_weight)
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elif metric == "body_fat_pct":
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value = _last(week_body_fat)
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days_in_week = len(week_body_fat)
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else:
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value = _avg(week_daily_values)
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days_in_week = len(week_daily_values)
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has_data = value is not None
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if has_data:
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local_weeks_with_data += 1
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points.append(
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{
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"index": idx,
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"week_start": slot["week_start"],
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"week_end": slot["week_end"],
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"value": round(value, 2) if value is not None else None,
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"days_with_data": days_in_week,
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"has_data": has_data,
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}
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)
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weeks_with_data = max(weeks_with_data, local_weeks_with_data)
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return points
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series: dict[str, Any] = {}
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for key, meta in METRIC_DEFS.items():
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points = rollup_week(key)
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reg_points = [(float(p["index"]), float(p["value"])) for p in points if p["has_data"] and p["value"] is not None]
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trend_payload: dict[str, Any] | None = None
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if trend and len(reg_points) >= 2:
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fit = linear_regression(reg_points)
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if fit:
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line = [
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{
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"index": p["index"],
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"week_start": p["week_start"],
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"value": round(fit["slope"] * p["index"] + fit["intercept"], 2),
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}
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for p in points
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]
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trend_payload = {
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"slope_per_week": round(fit["slope"], 4),
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"intercept": round(fit["intercept"], 2),
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"points_with_data": len(reg_points),
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"line": line,
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}
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series[key] = {
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"key": key,
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"label": meta["label"],
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"unit": meta["unit"],
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"points": points,
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"trend": trend_payload,
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"data_points": sum(1 for p in points if p["has_data"]),
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}
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use_daily = days_with_data > 0 and days_with_data <= 14 and weeks_with_data <= 2
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daily_series: dict[str, Any] | None = None
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if use_daily:
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daily_series = _build_daily_series(
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daily,
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daily_flags,
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body_by_day,
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end,
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trend=trend,
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lookback_days=min(30, max(days_with_data, 7)),
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)
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return {
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"end_date": end.isoformat(),
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"weeks": weeks,
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"granularity": "day" if use_daily else "week",
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"first_week_start": first_week_start.isoformat(),
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"last_week_start": last_week_start.isoformat(),
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"days_with_data": days_with_data,
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"weeks_with_data": weeks_with_data,
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"series": series,
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"daily_series": daily_series,
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}
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def _build_daily_series(
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daily: dict[date, dict[str, float]],
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daily_flags: dict[date, set[str]],
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body_by_day: dict[date, list[tuple[date, float, float | None]]],
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end: date,
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*,
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trend: bool,
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lookback_days: int,
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) -> dict[str, Any]:
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start = end - timedelta(days=lookback_days - 1)
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day_points: list[date] = []
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cursor = start
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while cursor <= end:
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day_points.append(cursor)
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cursor += timedelta(days=1)
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result: dict[str, Any] = {}
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for key, meta in METRIC_DEFS.items():
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points: list[dict[str, Any]] = []
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for idx, d in enumerate(day_points):
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value: float | None = None
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has_data = False
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if key == "weight_kg":
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body = body_by_day.get(d, [])
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pairs = [(x, w) for x, w, _ in body]
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value = _last(pairs) if pairs else None
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has_data = value is not None
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elif key == "body_fat_pct":
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fat_vals = [(x, bf) for x, _, bf in body_by_day.get(d, []) if bf is not None]
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value = _last(fat_vals) if fat_vals else None
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has_data = value is not None
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else:
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flags = daily_flags.get(d, set())
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totals = daily.get(d)
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if key == "calories" and totals and "nutrition" in flags:
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value = totals["calories"]
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has_data = True
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elif key == "protein_g" and totals and "nutrition" in flags:
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value = totals["protein_g"]
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has_data = True
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elif key == "water_l" and totals and "water" in flags:
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value = totals["water_ml"] / 1000.0
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has_data = True
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elif key == "steps" and totals and "steps" in flags:
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value = totals["steps"]
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has_data = True
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points.append(
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{
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"index": idx,
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"date": d.isoformat(),
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"value": round(value, 2) if value is not None else None,
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"has_data": has_data,
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}
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)
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reg_points = [(float(p["index"]), float(p["value"])) for p in points if p["has_data"] and p["value"] is not None]
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trend_payload: dict[str, Any] | None = None
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if trend and len(reg_points) >= 2:
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fit = linear_regression(reg_points)
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if fit:
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trend_payload = {
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"slope_per_day": round(fit["slope"], 4),
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"intercept": round(fit["intercept"], 2),
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"points_with_data": len(reg_points),
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"line": [
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{
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"index": p["index"],
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"date": p["date"],
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"value": round(fit["slope"] * p["index"] + fit["intercept"], 2),
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}
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for p in points
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],
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}
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result[key] = {
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"key": key,
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"label": meta["label"],
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"unit": meta["unit"],
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"points": points,
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"trend": trend_payload,
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"data_points": sum(1 for p in points if p["has_data"]),
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}
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return result
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