mitai-jinkendo/backend/goal_utils.py

"""
Goal Utilities - Abstraction Layer for Focus Weights

This module provides an abstraction layer between goal modes and focus weights.
This allows Phase 0b placeholders to work with the current simple goal_mode system,
while enabling future v2.0 redesign (focus_areas table) without rewriting 120+ placeholders.

Version History:
- V1 (current): Maps goal_mode to predefined weights
- V2 (future): Reads from focus_areas table with custom user weights

Part of Phase 1: Quick Fixes + Abstraction Layer
"""

from typing import Dict
from db import get_cursor


def get_focus_weights(conn, profile_id: str) -> Dict[str, float]:
    """
    Get focus area weights for a profile.

    This is an abstraction layer that will evolve:
    - V1 (now): Maps goal_mode → predefined weights
    - V2 (later): Reads from focus_areas table → custom user weights

    Args:
        conn: Database connection
        profile_id: User's profile ID

    Returns:
        Dict with focus weights (sum = 1.0):
        {
            'weight_loss': 0.3,    # Fat loss priority
            'muscle_gain': 0.2,    # Muscle gain priority
            'strength': 0.25,      # Strength training priority
            'endurance': 0.25,     # Cardio/endurance priority
            'flexibility': 0.0,    # Mobility priority
            'health': 0.0          # General health maintenance
        }

    Example Usage in Phase 0b:
        weights = get_focus_weights(conn, profile_id)

        # Score calculation considers user's focus
        overall_score = (
            body_score * weights['weight_loss'] +
            strength_score * weights['strength'] +
            cardio_score * weights['endurance']
        )
    """
    cur = get_cursor(conn)

    # Fetch current goal_mode
    cur.execute(
        "SELECT goal_mode FROM profiles WHERE id = %s",
        (profile_id,)
    )
    row = cur.fetchone()

    if not row:
        # Fallback: balanced health focus
        return {
            'weight_loss': 0.0,
            'muscle_gain': 0.0,
            'strength': 0.0,
            'endurance': 0.0,
            'flexibility': 0.0,
            'health': 1.0
        }

    goal_mode = row['goal_mode'] or 'health'

    # V1: Predefined weight mappings per goal_mode
    # These represent "typical" focus distributions for each mode
    WEIGHT_MAPPINGS = {
        'weight_loss': {
            'weight_loss': 0.60,   # Primary: fat loss
            'endurance': 0.20,     # Support: cardio for calorie burn
            'muscle_gain': 0.0,    # Not compatible
            'strength': 0.10,      # Maintain muscle during deficit
            'flexibility': 0.05,   # Minor: mobility work
            'health': 0.05         # Minor: general wellness
        },
        'strength': {
            'strength': 0.50,      # Primary: strength gains
            'muscle_gain': 0.40,   # Support: hypertrophy
            'endurance': 0.10,     # Minor: work capacity
            'weight_loss': 0.0,    # Not compatible with strength focus
            'flexibility': 0.0,
            'health': 0.0
        },
        'endurance': {
            'endurance': 0.70,     # Primary: aerobic capacity
            'health': 0.20,        # Support: cardiovascular health
            'flexibility': 0.10,   # Support: mobility for running
            'weight_loss': 0.0,
            'muscle_gain': 0.0,
            'strength': 0.0
        },
        'recomposition': {
            'weight_loss': 0.30,   # Equal: lose fat
            'muscle_gain': 0.30,   # Equal: gain muscle
            'strength': 0.25,      # Support: progressive overload
            'endurance': 0.10,     # Minor: conditioning
            'flexibility': 0.05,   # Minor: mobility
            'health': 0.0
        },
        'health': {
            'health': 0.50,        # Primary: general wellness
            'endurance': 0.20,     # Support: cardio health
            'flexibility': 0.15,   # Support: mobility
            'strength': 0.10,      # Support: functional strength
            'weight_loss': 0.05,   # Minor: maintain healthy weight
            'muscle_gain': 0.0
        }
    }

    return WEIGHT_MAPPINGS.get(goal_mode, WEIGHT_MAPPINGS['health'])


def get_primary_focus(conn, profile_id: str) -> str:
    """
    Get the primary focus area for a profile.

    Returns the focus area with the highest weight.
    Useful for UI labels and simple decision logic.

    Args:
        conn: Database connection
        profile_id: User's profile ID

    Returns:
        Primary focus area name (e.g., 'weight_loss', 'strength')
    """
    weights = get_focus_weights(conn, profile_id)
    return max(weights.items(), key=lambda x: x[1])[0]


def get_focus_description(focus_area: str) -> str:
    """
    Get human-readable description for a focus area.

    Args:
        focus_area: Focus area key (e.g., 'weight_loss')

    Returns:
        German description for UI display
    """
    descriptions = {
        'weight_loss': 'Gewichtsreduktion & Fettabbau',
        'muscle_gain': 'Muskelaufbau & Hypertrophie',
        'strength': 'Kraftsteigerung & Performance',
        'endurance': 'Ausdauer & aerobe Kapazität',
        'flexibility': 'Beweglichkeit & Mobilität',
        'health': 'Allgemeine Gesundheit & Erhaltung'
    }
    return descriptions.get(focus_area, focus_area)


# Future V2 Implementation (commented out for reference):
"""
def get_focus_weights_v2(conn, profile_id: str) -> Dict[str, float]:
    '''V2: Read from focus_areas table with custom user weights'''
    cur = get_cursor(conn)

    cur.execute('''
        SELECT weight_loss_pct, muscle_gain_pct, endurance_pct,
               strength_pct, flexibility_pct, health_pct
        FROM focus_areas
        WHERE profile_id = %s AND active = true
        LIMIT 1
    ''', (profile_id,))

    row = cur.fetchone()

    if not row:
        # Fallback to V1 behavior
        return get_focus_weights(conn, profile_id)

    # Convert percentages to weights (0-1 range)
    return {
        'weight_loss': row['weight_loss_pct'] / 100.0,
        'muscle_gain': row['muscle_gain_pct'] / 100.0,
        'endurance': row['endurance_pct'] / 100.0,
        'strength': row['strength_pct'] / 100.0,
        'flexibility': row['flexibility_pct'] / 100.0,
        'health': row['health_pct'] / 100.0
    }
"""