Meal-Planner/backend/app/services/feedback_analyzer.py

"""Feedback Analyzer — turns family feedback signals into recipe discovery queries.

Reads the past N weeks of feedback (ratings, text, denial reasons, never-suggest)
and produces structured positive/negative signals plus external API search queries.
"""
from __future__ import annotations

import logging

from dataclasses import dataclass, field
from datetime import date, timedelta
from typing import List, Optional, Dict, Any, Set
from uuid import UUID

from sqlalchemy.orm import Session

from app.models import (
    FamilyProfile,
    Feedback,
    MealPlanItem,
    Recipe,
    DenialReason,
    NeverSuggest,
)

logger = logging.getLogger(__name__)

_DEFAULT_LOOKBACK_WEEKS = 4
_MIN_FEEDBACK_COUNT = 3
_MIN_AVG_RATING_FOR_POSITIVE = 4.0
_TOP_RATED_COUNT = 3


@dataclass
class NegativeSignal:
    type: str           # e.g. "avoid_ingredient", "avoid_tag", "too_spicy"
    value: str
    count: int
    sources: Set[str] = field(default_factory=set)


@dataclass
class PositiveSignal:
    type: str           # e.g. "prefer_cuisine", "prefer_protein"
    value: str
    count: int
    avg_rating: float


@dataclass
class FeedbackAnalysis:
    family_id: UUID
    lookback_start: date
    lookback_end: date
    total_feedback_count: int
    positive_signals: List[PositiveSignal]
    negative_signals: List[NegativeSignal]
    top_rated_recipe_ids: List[UUID]
    top_rated_recipe_names: List[str]
    discovery_queries: List[str]
    confidence: float = 0.0

    def to_dict(self) -> dict[str, Any]:
        return {
            "family_id": str(self.family_id),
            "lookback_start": self.lookback_start.isoformat(),
            "lookback_end": self.lookback_end.isoformat(),
            "total_feedback_count": self.total_feedback_count,
            "positive_signals": [
                {"type": s.type, "value": s.value, "count": s.count, "avg_rating": s.avg_rating}
                for s in self.positive_signals
            ],
            "negative_signals": [
                {"type": s.type, "value": s.value, "count": s.count}
                for s in self.negative_signals
            ],
            "top_rated_recipe_ids": [str(r) for r in self.top_rated_recipe_ids],
            "top_rated_recipe_names": self.top_rated_recipe_names,
            "discovery_queries": self.discovery_queries,
            "confidence": self.confidence,
        }


class FeedbackAnalyzer:
    """Analyze family feedback and produce recipe discovery signals."""

    def __init__(self, lookback_weeks: int = _DEFAULT_LOOKBACK_WEEKS) -> None:
        self.lookback_weeks = lookback_weeks

    def analyze(self, db: Session, family_id: UUID, today: date | None = None) -> FeedbackAnalysis:
        today = today or date.today()
        lookback_start = today - timedelta(weeks=self.lookback_weeks)

        # Pull feedback from lookback window with recipe context
        feedbacks = (
            db.query(Feedback, MealPlanItem, Recipe)
            .join(MealPlanItem, MealPlanItem.id == Feedback.meal_plan_item_id)
            .join(Recipe, Recipe.id == MealPlanItem.recipe_id)
            .filter(
                Feedback.family_profile_id == family_id,
                Feedback.created_at >= lookback_start,
            )
            .all()
        )

        total = len(feedbacks)

        if total < _MIN_FEEDBACK_COUNT:
            logger.info(
                "FeedbackAnalyzer: only %d feedbacks in last %d weeks (< %d minimum). "
                "Skipping discovery.",
                total, self.lookback_weeks, _MIN_FEEDBACK_COUNT,
            )
            return FeedbackAnalysis(
                family_id=family_id,
                lookback_start=lookback_start,
                lookback_end=today,
                total_feedback_count=total,
                positive_signals=[],
                negative_signals=[],
                top_rated_recipe_ids=[],
                top_rated_recipe_names=[],
                discovery_queries=[],
                confidence=0.0,
            )

        # Aggregate ratings per recipe
        recipe_ratings: dict[UUID, list[int]] = {}
        recipe_names: dict[UUID, str] = {}
        tag_ratings: dict[str, list[int]] = {}
        protein_ratings: dict[str, list[int]] = {}
        never_suggest_recipe_ids: set[UUID] = set()
        never_suggest_ingredient_ids: set[UUID] = set()

        for feedback, meal_item, recipe in feedbacks:
            rid = recipe.id
            recipe_names[rid] = recipe.name
            recipe_ratings.setdefault(rid, []).append(feedback.rating or 3)

            # Aggregate cuisine tags
            for tag in (recipe.cuisine_tags or []):
                tag.lower()
                tag_ratings.setdefault(tag.lower(), []).append(feedback.rating or 3)

            # Aggregate protein
            if recipe.protein_type:
                protein_ratings.setdefault(recipe.protein_type.lower(), []).append(feedback.rating or 3)

        # Pull never_suggest rules
        ns_rows = (
            db.query(NeverSuggest)
            .filter(NeverSuggest.family_profile_id == family_id)
            .all()
        )
        for ns in ns_rows:
            if ns.recipe_id:
                never_suggest_recipe_ids.add(ns.recipe_id)
            if ns.ingredient_id:
                never_suggest_ingredient_ids.add(ns.ingredient_id)

        # Build positive signals (high-rated cuisines/proteins)
        positive_signals: list[PositiveSignal] = []

        for tag, ratings in tag_ratings.items():
            avg = sum(ratings) / len(ratings)
            if avg >= _MIN_AVG_RATING_FOR_POSITIVE and len(ratings) >= 2:
                positive_signals.append(
                    PositiveSignal(
                        type="prefer_cuisine",
                        value=tag,
                        count=len(ratings),
                        avg_rating=round(avg, 2),
                    )
                )

        for protein, ratings in protein_ratings.items():
            avg = sum(ratings) / len(ratings)
            if avg >= _MIN_AVG_RATING_FOR_POSITIVE and len(ratings) >= 2:
                positive_signals.append(
                    PositiveSignal(
                        type="prefer_protein",
                        value=protein,
                        count=len(ratings),
                        avg_rating=round(avg, 2),
                    )
                )

        # Sort by avg_rating desc, then count desc
        positive_signals.sort(key=lambda s: (-s.avg_rating, -s.count))

        # Build negative signals from denial reasons + never_suggest
        negative_signals: list[NegativeSignal] = []

        denial_counts: dict[str, int] = {}
        for feedback, meal_item, recipe in feedbacks:
            if feedback.denial_reason:
                key = f"denial_{feedback.denial_reason.value}"
                denial_counts[key] = denial_counts.get(key, 0) + 1

        for reason, count in denial_counts.items():
            sig_type = reason.replace("denial_", "")
            negative_signals.append(
                NegativeSignal(
                    type=f"denial_{sig_type}",
                    value=sig_type,
                    count=count,
                )
            )

        # Top rated recipes (for "similar to X" queries)
        recipe_avgs = {
            rid: sum(ratings) / len(ratings)
            for rid, ratings in recipe_ratings.items()
        }
        top_rated = sorted(
            recipe_avgs.items(), key=lambda x: -x[1]
        )[:_TOP_RATED_COUNT]
        top_rated_ids = [rid for rid, _ in top_rated]
        top_rated_names = [recipe_names[rid] for rid in top_rated_ids]

        # Build discovery queries
        queries = _build_discovery_queries(
            positive_signals=positive_signals,
            top_rated_names=top_rated_names,
            negative_signals=negative_signals,
        )

        # Confidence = proportion of positive signals that are well-supported
        confidence = 0.0
        if positive_signals:
            well_supported = sum(1 for s in positive_signals if s.count >= 2)
            confidence = well_supported / len(positive_signals)

        logger.info(
            "FeedbackAnalyzer: family=%s feedbacks=%d positives=%d negatives=%d queries=%d confidence=%.2f",
            family_id, total, len(positive_signals), len(negative_signals), len(queries), confidence,
        )

        return FeedbackAnalysis(
            family_id=family_id,
            lookback_start=lookback_start,
            lookback_end=today,
            total_feedback_count=total,
            positive_signals=positive_signals,
            negative_signals=negative_signals,
            top_rated_recipe_ids=top_rated_ids,
            top_rated_recipe_names=top_rated_names,
            discovery_queries=queries,
            confidence=confidence,
        )


def _build_discovery_queries(
    positive_signals: List[PositiveSignal],
    top_rated_names: List[str],
    negative_signals: List[NegativeSignal],
) -> List[str]:
    """Convert signals into Spoonacular search queries.

    Strategy:
    1. Combine top cuisine + top protein → "mexican shrimp"
    2. High-rated specific recipes → search by name
    3. Cap at 5 queries to stay within free quota
    """
    queries = []
    seen = set()

    # Extract top cuisines and proteins
    cuisines = [s.value for s in positive_signals if s.type == "prefer_cuisine"]
    proteins = [s.value for s in positive_signals if s.type == "prefer_protein"]

    # Cross product of top cuisine + protein
    for cuisine in cuisines[:2]:
        for protein in proteins[:2]:
            q = f"{cuisine} {protein}"
            if q not in seen:
                queries.append(q)
                seen.add(q)
            if len(queries) >= 5:
                return queries

    # Single cuisine or protein queries
    for cuisine in cuisines[:2]:
        if cuisine not in seen:
            queries.append(cuisine)
            seen.add(cuisine)
        if len(queries) >= 5:
            return queries

    for protein in proteins[:2]:
        if protein not in seen:
            queries.append(protein)
            seen.add(protein)
        if len(queries) >= 5:
            return queries

    # Top-rated recipe names (people liked these, find similar)
    for name in top_rated_names[:2]:
        if name not in seen:
            queries.append(name)
            seen.add(name)
        if len(queries) >= 5:
            return queries

    return queries