feat: feedback-driven recipe discovery (auto-ingest via Spoonacular)

backend/tests/test_feedback_analyzer.py

@@ -0,0 +1,246 @@
+"""Tests for app.services.feedback_analyzer."""
+from datetime import date, timedelta
+from uuid import uuid4
+
+import pytest
+
+from app.models import (
+    DenialReason,
+    FamilyProfile,
+    FamilyMember,
+    FamilyMemberRole,
+    Feedback,
+    MealPlan,
+    MealPlanItem,
+    MealPlanItemStatus,
+    MealType,
+    NeverSuggest,
+    Recipe,
+    WeeklyRun,
+)
+from app.services.feedback_analyzer import FeedbackAnalyzer
+
+
+@pytest.fixture()
+def make_family(db):
+    def _make():
+        fp = FamilyProfile(
+            id=uuid4(),
+            name="TestFamily",
+            household_size=2,
+            adult_count=2,
+            child_count=0,
+            pending_approval_policy="approve",
+        )
+        db.add(fp)
+        db.flush()
+        return fp
+    return _make
+
+
+@pytest.fixture()
+def make_member(db, make_family):
+    def _make(family=None):
+        fp = family or make_family()
+        m = FamilyMember(
+            id=uuid4(),
+            family_profile_id=fp.id,
+            name="Alice",
+            role=FamilyMemberRole.ADULT,
+        )
+        db.add(m)
+        db.flush()
+        return m
+    return _make
+
+
+@pytest.fixture()
+def make_recipe(db):
+    def _make(**kw):
+        r = Recipe(
+            id=uuid4(),
+            name=kw.get("name", "Test Recipe"),
+            servings=kw.get("servings", 4),
+            ingredients=[{"name": "ing", "qty": 1, "unit": "cup"}],
+            instructions=["cook"],
+            cuisine_tags=kw.get("cuisine_tags", []),
+            protein_type=kw.get("protein_type", None),
+        )
+        db.add(r)
+        db.flush()
+        return r
+    return _make
+
+
+@pytest.fixture()
+def make_meal_plan(db, make_family):
+    def _make(family=None, week_start=None):
+        week = week_start or date.today()
+        mp = MealPlan(
+            id=uuid4(),
+            family_profile_id=(family or make_family()).id,
+            week_start_date=week,
+        )
+        db.add(mp)
+        db.flush()
+        return mp
+    return _make
+
+
+@pytest.fixture()
+def make_item(db, make_meal_plan, make_recipe):
+    def _make(meal_plan=None, recipe=None, status=MealPlanItemStatus.pending, day_of_week=1):
+        mp = meal_plan or make_meal_plan()
+        r = recipe or make_recipe()
+        item = MealPlanItem(
+            id=uuid4(),
+            meal_plan_id=mp.id,
+            recipe_id=r.id,
+            day_of_week=day_of_week,
+            meal_type=MealType.DINNER,
+            approval_status=status,
+        )
+        db.add(item)
+        db.flush()
+        return item
+    return _make
+
+
+class TestFeedbackAnalyzer:
+    def test_insufficient_feedback(self, db, make_family):
+        family = make_family()
+        analyzer = FeedbackAnalyzer(lookback_weeks=4)
+        today = date.today()
+        result = analyzer.analyze(db, family.id, today=today)
+        assert result.total_feedback_count == 0
+        assert result.confidence == 0.0
+        assert result.discovery_queries == []
+
+    def test_positive_cuisine_signal(self, db, make_family, make_member, make_recipe, make_meal_plan, make_item):
+        family = make_family()
+        member = make_member(family=family)
+        recipe = make_recipe(cuisine_tags=["mexican"], protein_type="chicken")
+        mp = make_meal_plan(family=family)
+        item = make_item(meal_plan=mp, recipe=recipe)
+
+        # 3 feedbacks, avg rating 4 (>= threshold, >= 2 samples)
+        for _ in range(3):
+            f = Feedback(
+                id=uuid4(),
+                family_profile_id=family.id,
+                meal_plan_item_id=item.id,
+                rating=4,
+            )
+            db.add(f)
+        db.flush()
+
+        analyzer = FeedbackAnalyzer(lookback_weeks=4)
+        result = analyzer.analyze(db, family.id)
+        assert result.total_feedback_count == 3
+        assert result.confidence == 1.0
+
+        pos = result.positive_signals
+        assert len(pos) == 2  # cuisine + protein
+        assert any(s.type == "prefer_cuisine" and s.value == "mexican" for s in pos)
+        assert any(s.type == "prefer_protein" and s.value == "chicken" for s in pos)
+
+    def test_never_suggest_blocks_recipe(self, db, make_family, make_member, make_recipe, make_meal_plan, make_item):
+        family = make_family()
+        member = make_member(family=family)
+        recipe = make_recipe(cuisine_tags=["indian"], protein_type="lamb")
+        mp = make_meal_plan(family=family)
+        item = make_item(meal_plan=mp, recipe=recipe)
+
+        # Deny with never-suggest
+        f = Feedback(
+            id=uuid4(),
+            family_profile_id=family.id,
+            meal_plan_item_id=item.id,
+            rating=1,
+            never_suggest=True,
+            denial_reason=DenialReason.DISLIKED_INGREDIENT,
+        )
+        db.add(f)
+        db.flush()
+
+        ns = NeverSuggest(
+            id=uuid4(),
+            family_profile_id=family.id,
+            recipe_id=recipe.id,
+        )
+        db.add(ns)
+        db.flush()
+
+        analyzer = FeedbackAnalyzer(lookback_weeks=4)
+        result = analyzer.analyze(db, family.id)
+        pos = result.positive_signals
+        # 1 feedback < min, therefore no positive signals
+        assert len(pos) == 0
+
+    def test_denial_reason_aggregated(self, db, make_family, make_member, make_recipe, make_meal_plan, make_item):
+        family = make_family()
+        member = make_member(family=family)
+        recipe = make_recipe()
+        mp = make_meal_plan(family=family)
+        item = make_item(meal_plan=mp, recipe=recipe)
+
+        for _ in range(3):
+            f = Feedback(
+                id=uuid4(),
+                family_profile_id=family.id,
+                meal_plan_item_id=item.id,
+                rating=2,
+                denial_reason=DenialReason.TOO_EXPENSIVE,
+            )
+            db.add(f)
+        db.flush()
+
+        analyzer = FeedbackAnalyzer(lookback_weeks=4)
+        result = analyzer.analyze(db, family.id)
+        negatives = result.negative_signals
+        assert any(n.type == "denial_too_expensive" for n in negatives)
+        assert negatives[0].count == 3
+
+    def test_discovery_queries_capped(self, db, make_family, make_member, make_recipe, make_meal_plan, make_item):
+        family = make_family()
+        member = make_member(family=family)
+        cuisines = ["mexican", "italian", "chinese"]
+        proteins = ["chicken", "beef", "shrimp"]
+        for i, (c, p) in enumerate(zip(cuisines, proteins)):
+            recipe = make_recipe(name=f"R{i}", cuisine_tags=[c], protein_type=p)
+            mp = make_meal_plan(family=family, week_start=date.today() - timedelta(weeks=i))
+            item = make_item(meal_plan=mp, recipe=recipe)
+            for _ in range(2):
+                db.add(Feedback(
+                    id=uuid4(),
+                    family_profile_id=family.id,
+                    meal_plan_item_id=item.id,
+                    rating=5,
+                ))
+        db.flush()
+
+        analyzer = FeedbackAnalyzer(lookback_weeks=4)
+        result = analyzer.analyze(db, family.id)
+        queries = result.discovery_queries
+        assert len(queries) <= 5
+        # At least one cuisine+protein cross query
+        assert any(" " in q for q in queries)
+
+    def test_top_rated_sorted(self, db, make_family, make_member, make_recipe, make_meal_plan, make_item):
+        family = make_family()
+        member = make_member(family=family)
+        r1 = make_recipe(name="Awesome Dish")
+        r2 = make_recipe(name="Meh Dish")
+        mp = make_meal_plan(family=family)
+        item1 = make_item(meal_plan=mp, recipe=r1)
+        item2 = make_item(meal_plan=mp, recipe=r2, day_of_week=2)
+
+        db.add(Feedback(id=uuid4(), family_profile_id=family.id, meal_plan_item_id=item1.id, rating=5))
+        db.add(Feedback(id=uuid4(), family_profile_id=family.id, meal_plan_item_id=item1.id, rating=5))
+        db.add(Feedback(id=uuid4(), family_profile_id=family.id, meal_plan_item_id=item2.id, rating=3))
+        db.flush()
+
+        analyzer = FeedbackAnalyzer(lookback_weeks=4)
+        result = analyzer.analyze(db, family.id)
+        assert result.top_rated_recipe_names[0] == "Awesome Dish"
+