diff --git a/backend/innercontext/api/products.py b/backend/innercontext/api/products.py index f9e0323..6c13ac9 100644 --- a/backend/innercontext/api/products.py +++ b/backend/innercontext/api/products.py @@ -257,11 +257,16 @@ class InventoryUpdate(SQLModel): class ProductSuggestion(PydanticBase): category: ProductCategory product_type: str + priority: Literal["high", "medium", "low"] key_ingredients: list[str] target_concerns: list[str] - why_needed: str recommended_time: DayTime frequency: str + short_reason: str + reason_to_buy_now: str + reason_not_needed_if_budget_tight: str | None = None + fit_with_current_routine: str + usage_cautions: list[str] class ShoppingSuggestionResponse(PydanticBase): @@ -276,11 +281,16 @@ class ShoppingSuggestionResponse(PydanticBase): class _ProductSuggestionOut(PydanticBase): category: ProductCategory product_type: str + priority: Literal["high", "medium", "low"] key_ingredients: list[str] target_concerns: list[str] - why_needed: str recommended_time: DayTime frequency: str + short_reason: str + reason_to_buy_now: str + reason_not_needed_if_budget_tight: str | None = None + fit_with_current_routine: str + usage_cautions: list[str] class _ShoppingSuggestionsOut(PydanticBase): @@ -982,6 +992,13 @@ ZASADY: 8. Zwracaj uwagę na ewentualne konflikty polecanych składników z tymi, które użytkownik już posiada (np. nie polecaj peptydów miedziowych jeśli użytkownik nadużywa kwasów) 9. Odpowiadaj w języku polskim 10. Używaj wyłącznie dozwolonych wartości enumów poniżej - nie twórz synonimów typu "night", "evening" ani "treatment" +11. Możesz zwrócić pustą listę suggestions, jeśli nie widzisz realnej potrzeby zakupowej +12. Każda sugestia ma mieć charakter decision-support: konkretnie wyjaśnij, dlaczego warto kupić ją teraz, jak wpisuje się w obecną rutynę i jakie są ograniczenia +13. `short_reason` ma być krótkim, 1-zdaniowym skrótem decyzji zakupowej +14. `reason_to_buy_now` ma być konkretne i praktyczne, bez lania wody +15. `reason_not_needed_if_budget_tight` jest opcjonalne - uzupełniaj tylko wtedy, gdy zakup nie jest pilny lub istnieje rozsądny kompromis +16. `usage_cautions` ma być krótką listą praktycznych uwag; gdy brak istotnych zastrzeżeń zwróć pustą listę +17. `priority` ustawiaj jako: high = wyraźna luka lub pilna potrzeba, medium = sensowne uzupełnienie, low = opcjonalny upgrade DOZWOLONE WARTOŚCI ENUMÓW: - category: "cleanser" | "toner" | "essence" | "serum" | "moisturizer" | "spf" | "mask" | "exfoliant" | "hair_treatment" | "tool" | "spot_treatment" | "oil" @@ -1086,6 +1103,20 @@ def suggest_shopping(session: Session = Depends(get_session)): except json.JSONDecodeError as e: raise HTTPException(status_code=502, detail=f"LLM returned invalid JSON: {e}") + try: + parsed_response = _ShoppingSuggestionsOut.model_validate(parsed) + except ValidationError as exc: + formatted_errors = "; ".join( + f"{'/'.join(str(part) for part in err['loc'])}: {err['msg']}" + for err in exc.errors() + ) + raise HTTPException( + status_code=502, + detail=( + f"LLM returned invalid shopping suggestion schema: {formatted_errors}" + ), + ) + # Get products with inventory (those user already owns) products_with_inventory_ids = session.exec( select(ProductInventory.product_id).distinct() @@ -1102,8 +1133,11 @@ def suggest_shopping(session: Session = Depends(get_session)): # Build initial shopping response without metadata shopping_response = ShoppingSuggestionResponse( - suggestions=[ProductSuggestion(**s) for s in parsed.get("suggestions", [])], - reasoning=parsed.get("reasoning", ""), + suggestions=[ + ProductSuggestion.model_validate(s.model_dump()) + for s in parsed_response.suggestions + ], + reasoning=parsed_response.reasoning, ) validation_result = validator.validate(shopping_response, shopping_context) diff --git a/backend/innercontext/validators/shopping_validator.py b/backend/innercontext/validators/shopping_validator.py index 4f21360..bbd4c17 100644 --- a/backend/innercontext/validators/shopping_validator.py +++ b/backend/innercontext/validators/shopping_validator.py @@ -22,48 +22,9 @@ class ShoppingValidationContext: class ShoppingValidator(BaseValidator): - """Validates shopping suggestions for product types.""" + """Validates shopping suggestion schema and copy quality.""" - # Realistic product type patterns (not exhaustive, just sanity checks) - VALID_PRODUCT_TYPE_PATTERNS = { - "serum", - "cream", - "cleanser", - "toner", - "essence", - "moisturizer", - "spf", - "sunscreen", - "oil", - "balm", - "mask", - "exfoliant", - "acid", - "retinoid", - "vitamin", - "niacinamide", - "hyaluronic", - "ceramide", - "peptide", - "antioxidant", - "aha", - "bha", - "pha", - } - - VALID_FREQUENCIES = { - "daily", - "twice daily", - "am", - "pm", - "both", - "2x weekly", - "3x weekly", - "2-3x weekly", - "weekly", - "as needed", - "occasional", - } + VALID_PRIORITIES = {"high", "medium", "low"} def validate( self, response: Any, context: ShoppingValidationContext @@ -73,19 +34,17 @@ class ShoppingValidator(BaseValidator): Checks: 1. suggestions field present - 2. Product types are realistic (contain known keywords) - 3. Not suggesting products user already owns (should mark as [✗]) - 4. Recommended frequencies are valid - 5. Categories are valid - 6. Targets are valid - 7. Each suggestion has required fields + 2. Categories are valid + 3. Targets are valid + 4. Each suggestion has required fields + 5. Decision-support fields are well formed Args: response: Parsed shopping suggestion response context: Validation context Returns: - ValidationResult with any errors/warnings + ValidationResult with schema errors and lightweight quality warnings """ result = ValidationResult() @@ -112,15 +71,8 @@ class ShoppingValidator(BaseValidator): f"Suggestion {sug_num}: invalid category '{suggestion.category}'" ) - # Check product type is realistic - if hasattr(suggestion, "product_type") and suggestion.product_type: - self._check_product_type_realistic( - suggestion.product_type, sug_num, result - ) - - # Check frequency is valid - if hasattr(suggestion, "frequency") and suggestion.frequency: - self._check_frequency_valid(suggestion.frequency, sug_num, result) + if hasattr(suggestion, "priority") and suggestion.priority: + self._check_priority_valid(suggestion.priority, sug_num, result) # Check targets are valid if hasattr(suggestion, "target_concerns") and suggestion.target_concerns: @@ -128,6 +80,11 @@ class ShoppingValidator(BaseValidator): suggestion.target_concerns, sug_num, context, result ) + if hasattr(suggestion, "usage_cautions"): + self._check_usage_cautions(suggestion.usage_cautions, sug_num, result) + + self._check_text_quality(suggestion, sug_num, result) + # Check recommended_time is valid if hasattr(suggestion, "recommended_time") and suggestion.recommended_time: if suggestion.recommended_time not in ("am", "pm", "both"): @@ -142,7 +99,15 @@ class ShoppingValidator(BaseValidator): self, suggestion: Any, sug_num: int, result: ValidationResult ) -> None: """Check suggestion has required fields.""" - required = ["category", "product_type", "why_needed"] + required = [ + "category", + "product_type", + "priority", + "short_reason", + "reason_to_buy_now", + "fit_with_current_routine", + "usage_cautions", + ] for field in required: if not hasattr(suggestion, field) or getattr(suggestion, field) is None: @@ -150,64 +115,14 @@ class ShoppingValidator(BaseValidator): f"Suggestion {sug_num}: missing required field '{field}'" ) - def _check_product_type_realistic( - self, product_type: str, sug_num: int, result: ValidationResult + def _check_priority_valid( + self, priority: str, sug_num: int, result: ValidationResult ) -> None: - """Check product type contains realistic keywords.""" - product_type_lower = product_type.lower() - - # Check if any valid pattern appears in the product type - has_valid_keyword = any( - pattern in product_type_lower - for pattern in self.VALID_PRODUCT_TYPE_PATTERNS - ) - - if not has_valid_keyword: - result.add_warning( - f"Suggestion {sug_num}: product type '{product_type}' looks unusual - " - "verify it's a real skincare product category" - ) - - # Check for brand names (shouldn't suggest specific brands) - suspicious_brands = [ - "la roche", - "cerave", - "paula", - "ordinary", - "skinceuticals", - "drunk elephant", - "versed", - "inkey", - "cosrx", - "pixi", - ] - - if any(brand in product_type_lower for brand in suspicious_brands): + """Check priority uses supported enum values.""" + if priority not in self.VALID_PRIORITIES: result.add_error( - f"Suggestion {sug_num}: product type contains brand name - " - "should suggest product TYPES only, not specific brands" - ) - - def _check_frequency_valid( - self, frequency: str, sug_num: int, result: ValidationResult - ) -> None: - """Check frequency is a recognized pattern.""" - frequency_lower = frequency.lower() - - # Check for exact matches or common patterns - is_valid = ( - frequency_lower in self.VALID_FREQUENCIES - or "daily" in frequency_lower - or "weekly" in frequency_lower - or "am" in frequency_lower - or "pm" in frequency_lower - or "x" in frequency_lower # e.g. "2x weekly" - ) - - if not is_valid: - result.add_warning( - f"Suggestion {sug_num}: unusual frequency '{frequency}' - " - "verify it's a realistic usage pattern" + f"Suggestion {sug_num}: invalid priority '{priority}' " + "(must be 'high', 'medium', or 'low')" ) def _check_targets_valid( @@ -227,3 +142,64 @@ class ShoppingValidator(BaseValidator): result.add_error( f"Suggestion {sug_num}: invalid target concern '{target}'" ) + + def _check_usage_cautions( + self, usage_cautions: Any, sug_num: int, result: ValidationResult + ) -> None: + """Check usage cautions are a list of short strings.""" + if not isinstance(usage_cautions, list): + result.add_error(f"Suggestion {sug_num}: usage_cautions must be a list") + return + + for caution in usage_cautions: + if not isinstance(caution, str): + result.add_error( + f"Suggestion {sug_num}: usage_cautions entries must be strings" + ) + continue + if len(caution.strip()) > 180: + result.add_warning( + f"Suggestion {sug_num}: usage caution is too long - keep it concise" + ) + + def _check_text_quality( + self, suggestion: Any, sug_num: int, result: ValidationResult + ) -> None: + """Warn when decision-support copy is too generic or empty-ish.""" + generic_phrases = { + "wspiera skore", + "pomaga skorze", + "moze pomoc", + "dobry wybor", + "uzupelnia rutyne", + "supports the skin", + "may help", + "good option", + "complements the routine", + } + + text_fields = [ + ("short_reason", getattr(suggestion, "short_reason", None), 12), + ("reason_to_buy_now", getattr(suggestion, "reason_to_buy_now", None), 18), + ( + "fit_with_current_routine", + getattr(suggestion, "fit_with_current_routine", None), + 18, + ), + ] + + for field_name, value, min_length in text_fields: + if not isinstance(value, str): + continue + stripped = value.strip() + if len(stripped) < min_length: + result.add_warning( + f"Suggestion {sug_num}: {field_name} is very short - add more decision context" + ) + continue + + lowered = stripped.lower() + if lowered in generic_phrases: + result.add_warning( + f"Suggestion {sug_num}: {field_name} is too generic - make it more specific" + ) diff --git a/backend/tests/test_products_helpers.py b/backend/tests/test_products_helpers.py index db9b210..9ce3573 100644 --- a/backend/tests/test_products_helpers.py +++ b/backend/tests/test_products_helpers.py @@ -5,17 +5,25 @@ from unittest.mock import patch from sqlmodel import Session from innercontext.api.products import ( + ProductSuggestion, + ShoppingSuggestionResponse, _build_shopping_context, _extract_requested_product_ids, build_product_details_tool_handler, ) from innercontext.models import ( Product, + ProductCategory, ProductInventory, SexAtBirth, + SkinConcern, SkinConditionSnapshot, ) from innercontext.models.profile import UserProfile +from innercontext.validators.shopping_validator import ( + ShoppingValidationContext, + ShoppingValidator, +) def test_build_shopping_context(session: Session): @@ -46,6 +54,7 @@ def test_build_shopping_context(session: Session): # Add product p = Product( id=uuid.uuid4(), + short_id=str(uuid.uuid4())[:8], name="Soothing Serum", brand="BrandX", category="serum", @@ -108,7 +117,7 @@ def test_suggest_shopping(client, session): "Response", (), { - "text": '{"suggestions": [{"category": "cleanser", "product_type": "cleanser", "priority": "high", "key_ingredients": [], "target_concerns": [], "why_needed": "reason", "recommended_time": "am", "frequency": "daily"}], "reasoning": "Test shopping"}' + "text": '{"suggestions": [{"category": "cleanser", "product_type": "cleanser", "priority": "high", "key_ingredients": ["glycerin"], "target_concerns": ["dehydration"], "recommended_time": "am", "frequency": "daily", "short_reason": "Brakuje lagodnego kroku myjacego rano.", "reason_to_buy_now": "Obecnie nie masz delikatnego produktu do porannego oczyszczania i wsparcia bariery.", "reason_not_needed_if_budget_tight": "Mozesz tymczasowo ograniczyc sie do samego splukania twarzy rano, jesli skora jest spokojna.", "fit_with_current_routine": "To domknie podstawowy krok cleanse bez dokladania agresywnych aktywow.", "usage_cautions": ["unikaj mocnego domywania przy podraznieniu"]}], "reasoning": "Test shopping"}' }, ) mock_gemini.return_value = (mock_response, None) @@ -118,6 +127,11 @@ def test_suggest_shopping(client, session): data = r.json() assert len(data["suggestions"]) == 1 assert data["suggestions"][0]["product_type"] == "cleanser" + assert data["suggestions"][0]["priority"] == "high" + assert data["suggestions"][0]["short_reason"] + assert data["suggestions"][0]["usage_cautions"] == [ + "unikaj mocnego domywania przy podraznieniu" + ] assert data["reasoning"] == "Test shopping" kwargs = mock_gemini.call_args.kwargs assert "USER PROFILE:" in kwargs["contents"] @@ -133,9 +147,43 @@ def test_suggest_shopping(client, session): assert "get_product_details" in kwargs["function_handlers"] +def test_suggest_shopping_invalid_json_returns_502(client): + with patch( + "innercontext.api.products.call_gemini_with_function_tools" + ) as mock_gemini: + mock_response = type("Response", (), {"text": "{"}) + mock_gemini.return_value = (mock_response, None) + + r = client.post("/products/suggest") + + assert r.status_code == 502 + assert "LLM returned invalid JSON" in r.json()["detail"] + + +def test_suggest_shopping_invalid_schema_returns_502(client): + with patch( + "innercontext.api.products.call_gemini_with_function_tools" + ) as mock_gemini: + mock_response = type( + "Response", + (), + { + "text": '{"suggestions": [{"category": "cleanser", "product_type": "cleanser", "priority": "urgent", "key_ingredients": [], "target_concerns": [], "recommended_time": "am", "frequency": "daily", "short_reason": "x", "reason_to_buy_now": "y", "fit_with_current_routine": "z", "usage_cautions": []}], "reasoning": "Test shopping"}' + }, + ) + mock_gemini.return_value = (mock_response, None) + + r = client.post("/products/suggest") + + assert r.status_code == 502 + assert "LLM returned invalid shopping suggestion schema" in r.json()["detail"] + assert "suggestions/0/priority" in r.json()["detail"] + + def test_shopping_context_medication_skip(session: Session): p = Product( id=uuid.uuid4(), + short_id=str(uuid.uuid4())[:8], name="Epiduo", brand="Galderma", category="serum", @@ -162,6 +210,7 @@ def test_extract_requested_product_ids_dedupes_and_limits(): def test_shopping_tool_handlers_return_payloads(session: Session): product = Product( id=uuid.uuid4(), + short_id=str(uuid.uuid4())[:8], name="Test Product", brand="Brand", category="serum", @@ -176,10 +225,10 @@ def test_shopping_tool_handlers_return_payloads(session: Session): payload = {"product_ids": [str(product.id)]} details = build_product_details_tool_handler([product])(payload) - assert details["products"][0]["inci"] == ["Water", "Niacinamide"] assert details["products"][0]["actives"][0]["name"] == "Niacinamide" assert "context_rules" in details["products"][0] assert details["products"][0]["last_used_on"] is None + assert "inci" not in details["products"][0] def test_shopping_tool_handler_includes_last_used_on_from_mapping(session: Session): @@ -200,3 +249,48 @@ def test_shopping_tool_handler_includes_last_used_on_from_mapping(session: Sessi )(payload) assert details["products"][0]["last_used_on"] == "2026-03-01" + + +def test_shopping_validator_accepts_freeform_product_type_and_frequency(): + response = ShoppingSuggestionResponse( + suggestions=[ + ProductSuggestion( + category="spot_treatment", + product_type="Punktowy preparat na wypryski z ichtiolem lub cynkiem", + priority="high", + key_ingredients=["ichtiol", "cynk"], + target_concerns=["acne"], + recommended_time="pm", + frequency="Codziennie (punktowo na zmiany)", + short_reason="Pomaga opanowac aktywne zmiany bez dokladania pelnego aktywu na cala twarz.", + reason_to_buy_now="Brakuje Ci dedykowanego produktu punktowego na pojedyncze wypryski.", + fit_with_current_routine="Mozesz dolozyc go tylko na zmiany po serum lub zamiast mocniejszego aktywu.", + usage_cautions=["stosuj tylko miejscowo"], + ), + ProductSuggestion( + category="mask", + product_type="Lagodna maska oczyszczajaca", + priority="low", + key_ingredients=["glinka"], + target_concerns=["sebum_excess"], + recommended_time="pm", + frequency="1 raz w tygodniu", + short_reason="To opcjonalne wsparcie przy nadmiarze sebum.", + reason_to_buy_now="Moze pomoc domknac sporadyczne oczyszczanie, gdy skora jest bardziej przetluszczona.", + fit_with_current_routine="Najlepiej traktowac to jako dodatkowy krok, nie zamiennik podstaw rutyny.", + usage_cautions=[], + ), + ], + reasoning="Test", + ) + + result = ShoppingValidator().validate( + response, + ShoppingValidationContext( + owned_product_ids=set(), + valid_categories=set(ProductCategory), + valid_targets=set(SkinConcern), + ), + ) + + assert not any("unusual frequency" in warning for warning in result.warnings) diff --git a/frontend/messages/en.json b/frontend/messages/en.json index b7e2447..df9d006 100644 --- a/frontend/messages/en.json +++ b/frontend/messages/en.json @@ -58,6 +58,15 @@ "products_suggestResults": "Suggestions", "products_suggestTime": "Time", "products_suggestFrequency": "Frequency", + "products_suggestPriorityHigh": "High priority", + "products_suggestPriorityMedium": "Medium priority", + "products_suggestPriorityLow": "Low priority", + "products_suggestBuyNow": "Buy now because", + "products_suggestRoutineFit": "How it fits your routine", + "products_suggestBudgetSkip": "If you're cutting the budget", + "products_suggestKeyIngredients": "Key ingredients", + "products_suggestTargets": "Targets", + "products_suggestCautions": "Cautions", "products_suggestRegenerate": "Regenerate", "products_suggestNoResults": "No suggestions.", "products_noProducts": "No products found.", diff --git a/frontend/messages/pl.json b/frontend/messages/pl.json index f4852b8..c1c8d56 100644 --- a/frontend/messages/pl.json +++ b/frontend/messages/pl.json @@ -60,6 +60,15 @@ "products_suggestResults": "Propozycje", "products_suggestTime": "Pora", "products_suggestFrequency": "Częstotliwość", + "products_suggestPriorityHigh": "Wysoki priorytet", + "products_suggestPriorityMedium": "Średni priorytet", + "products_suggestPriorityLow": "Niski priorytet", + "products_suggestBuyNow": "Kup teraz, bo", + "products_suggestRoutineFit": "Jak wpisuje się w rutynę", + "products_suggestBudgetSkip": "Jeśli tniesz budżet", + "products_suggestKeyIngredients": "Kluczowe składniki", + "products_suggestTargets": "Cele", + "products_suggestCautions": "Uwagi", "products_suggestRegenerate": "Wygeneruj ponownie", "products_suggestNoResults": "Brak propozycji.", "products_noProducts": "Nie znaleziono produktów.", diff --git a/frontend/src/lib/types.ts b/frontend/src/lib/types.ts index 2fd00f6..752c29f 100644 --- a/frontend/src/lib/types.ts +++ b/frontend/src/lib/types.ts @@ -284,14 +284,21 @@ export interface BatchSuggestion { // ─── Shopping suggestion types ─────────────────────────────────────────────── +export type ShoppingPriority = 'high' | 'medium' | 'low'; + export interface ProductSuggestion { category: string; product_type: string; + priority: ShoppingPriority; key_ingredients: string[]; target_concerns: string[]; - why_needed: string; recommended_time: string; frequency: string; + short_reason: string; + reason_to_buy_now: string; + reason_not_needed_if_budget_tight?: string; + fit_with_current_routine: string; + usage_cautions: string[]; } export interface ShoppingSuggestionResponse { diff --git a/frontend/src/routes/products/suggest/+page.server.ts b/frontend/src/routes/products/suggest/+page.server.ts index 5f8b5b5..8f48644 100644 --- a/frontend/src/routes/products/suggest/+page.server.ts +++ b/frontend/src/routes/products/suggest/+page.server.ts @@ -17,6 +17,9 @@ export const actions: Actions = { return { suggestions: data.suggestions, reasoning: data.reasoning, + validation_warnings: data.validation_warnings, + auto_fixes_applied: data.auto_fixes_applied, + response_metadata: data.response_metadata, }; } catch (e) { return fail(500, { error: String(e) }); diff --git a/frontend/src/routes/products/suggest/+page.svelte b/frontend/src/routes/products/suggest/+page.svelte index a6617af..79b21bd 100644 --- a/frontend/src/routes/products/suggest/+page.svelte +++ b/frontend/src/routes/products/suggest/+page.svelte @@ -1,7 +1,7 @@