From 40f9a353bbad7b9274ff8d7b90482eef4a8c0cd6 Mon Sep 17 00:00:00 2001 From: Piotr Oleszczyk Date: Mon, 2 Mar 2026 22:38:08 +0100 Subject: [PATCH] feat(products): add shopping suggestions feature MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Add POST /api/products/suggest endpoint that analyzes skin condition and inventory to suggest product types (e.g., 'Salicylic Acid 2% Masque') - Add MCP tool get_shopping_suggestions() for MCP clients - Add 'Suggest' button to Products page in frontend - Add /products/suggest page with suggestion cards - Include product type, key ingredients, target concerns, why_needed, recommended_time, and frequency in suggestions - Fix stock logic: sealed products now count as available inventory - Add legend to clarify ✓ (in stock) vs ✗ (not in stock) markers --- backend/innercontext/api/products.py | 172 +++++++++++++- backend/innercontext/mcp_server.py | 217 ++++++++++++++++++ frontend/messages/en.json | 11 + frontend/messages/pl.json | 11 + frontend/src/lib/types.ts | 17 ++ frontend/src/routes/products/+page.svelte | 5 +- .../routes/products/suggest/+page.server.ts | 26 +++ .../src/routes/products/suggest/+page.svelte | 126 ++++++++++ 8 files changed, 583 insertions(+), 2 deletions(-) create mode 100644 frontend/src/routes/products/suggest/+page.server.ts create mode 100644 frontend/src/routes/products/suggest/+page.svelte diff --git a/backend/innercontext/api/products.py b/backend/innercontext/api/products.py index 4e19352..90a9e21 100644 --- a/backend/innercontext/api/products.py +++ b/backend/innercontext/api/products.py @@ -5,6 +5,7 @@ from uuid import UUID, uuid4 from fastapi import APIRouter, Depends, HTTPException, Query from google.genai import types as genai_types +from pydantic import BaseModel as PydanticBase from pydantic import ValidationError from sqlmodel import Session, SQLModel, col, select @@ -19,6 +20,7 @@ from innercontext.models import ( ProductPublic, ProductWithInventory, SkinConcern, + SkinConditionSnapshot, ) from innercontext.models.enums import ( AbsorptionSpeed, @@ -176,6 +178,41 @@ class InventoryUpdate(SQLModel): notes: Optional[str] = None +# --------------------------------------------------------------------------- +# Shopping suggestion schemas +# --------------------------------------------------------------------------- + + +class ProductSuggestion(PydanticBase): + category: str + product_type: str + key_ingredients: list[str] + target_concerns: list[str] + why_needed: str + recommended_time: str + frequency: str + + +class ShoppingSuggestionResponse(PydanticBase): + suggestions: list[ProductSuggestion] + reasoning: str + + +class _ProductSuggestionOut(PydanticBase): + category: str + product_type: str + key_ingredients: list[str] + target_concerns: list[str] + why_needed: str + recommended_time: str + frequency: str + + +class _ShoppingSuggestionsOut(PydanticBase): + suggestions: list[_ProductSuggestionOut] + reasoning: str + + # --------------------------------------------------------------------------- # Product routes # --------------------------------------------------------------------------- @@ -218,7 +255,9 @@ def list_products( product_ids = [p.id for p in products] inventory_rows = ( session.exec( - select(ProductInventory).where(col(ProductInventory.product_id).in_(product_ids)) + select(ProductInventory).where( + col(ProductInventory.product_id).in_(product_ids) + ) ).all() if product_ids else [] @@ -467,3 +506,134 @@ def create_product_inventory( session.commit() session.refresh(entry) return entry + + +# --------------------------------------------------------------------------- +# Shopping suggestion +# --------------------------------------------------------------------------- + + +def _ev(v: object) -> str: + if v is None: + return "" + value = getattr(v, "value", None) + if isinstance(value, str): + return value + return str(v) + + +def _build_shopping_context(session: Session) -> str: + snapshot = session.exec( + select(SkinConditionSnapshot).order_by( + col(SkinConditionSnapshot.snapshot_date).desc() + ) + ).first() + + skin_lines = ["STAN SKÓRY:"] + if snapshot: + skin_lines.append(f" Data: {snapshot.snapshot_date}") + skin_lines.append(f" Ogólny stan: {_ev(snapshot.overall_state)}") + skin_lines.append(f" Typ skóry: {_ev(snapshot.skin_type)}") + skin_lines.append(f" Nawilżenie: {snapshot.hydration_level}/5") + skin_lines.append(f" Wrażliwość: {snapshot.sensitivity_level}/5") + skin_lines.append(f" Bariera: {_ev(snapshot.barrier_state)}") + concerns = ", ".join(_ev(c) for c in (snapshot.active_concerns or [])) + skin_lines.append(f" Aktywne problemy: {concerns or 'brak'}") + if snapshot.priorities: + skin_lines.append(f" Priorytety: {', '.join(snapshot.priorities)}") + else: + skin_lines.append(" (brak danych)") + + stmt = select(Product).where(col(Product.is_tool).is_(False)) + products = session.exec(stmt).all() + + product_ids = [p.id for p in products] + inventory_rows = ( + session.exec( + select(ProductInventory).where( + col(ProductInventory.product_id).in_(product_ids) + ) + ).all() + if product_ids + else [] + ) + inv_by_product: dict = {} + for inv in inventory_rows: + inv_by_product.setdefault(inv.product_id, []).append(inv) + + products_lines = ["POSIADANE PRODUKTY:"] + products_lines.append( + " Legenda: [✓] = produkt dostępny (w magazynie), [✗] = brak w magazynie" + ) + for p in products: + if p.is_medication: + continue + active_inv = [i for i in inv_by_product.get(p.id, []) if i.finished_at is None] + has_stock = len(active_inv) > 0 # any unfinished inventory = in stock + stock = "✓" if has_stock else "✗" + products_lines.append( + f" [{stock}] {p.name} ({p.brand or ''}) - {_ev(p.category)}, " + f"targets: {p.targets or []}" + ) + + return "\n".join(skin_lines) + "\n\n" + "\n".join(products_lines) + + +_SHOPPING_SYSTEM_PROMPT = """Jesteś asystentem zakupowym w dziedzinie pielęgnacji skóry. +Twoim zadaniem jest przeanalizować stan skóry użytkownika oraz produkty, które już posiada, +a następnie zasugerować TYPY produktów (bez marek), które mogłyby uzupełnić ich rutynę. + +LEGENDA: +- [✓] = produkt dostępny w magazynie (nawet jeśli jest zapieczętowany) +- [✗] = produkt niedostępny (brak w magazynie, wszystkie opakowania zużyte) + +ZASADY: +1. Sugeruj TYLKO typy produktów, NIGDY konkretne marki (np. "Salicylic Acid 2% Masque", nie "La Roche-Posay") +2. Produkty oznaczone [✗] to te, których NIE MA w magazynie - możesz je zasugerować +3. Produkty oznaczone [✓] są już dostępne - nie sugeruj ich ponownie +4. Bierz pod uwagę aktywne problemy skóry (acne, hyperpigmentacja, aging, etc.) +5. Sugeruj realistyczną częstotliwość użycia (dzienna, 2-3x tygodniowo, etc.) +6. Zachowaj kolejność warstw: cleanse → toner → serum → moisturizer → SPF +7. Jeśli użytkownik ma uszkodzoną barierę, unikaj silnych eksfoliantów i retinoidów +8. Odpowiadaj w języku polskim + +Format odpowiedzi - zwróć wyłącznie JSON zgodny z podanym schematem.""" + + +@router.post("/suggest", response_model=ShoppingSuggestionResponse) +def suggest_shopping(session: Session = Depends(get_session)): + context = _build_shopping_context(session) + + prompt = ( + f"Na podstawie poniższych danych przeanalizuj, jakie TYPY produktów " + f"mogłyby uzupełnić rutynę pielęgnacyjną użytkownika.\n\n" + f"{context}\n\n" + f"Zwróć wyłącznie JSON zgodny ze schematem." + ) + + response = call_gemini( + endpoint="products/suggest", + contents=prompt, + config=genai_types.GenerateContentConfig( + system_instruction=_SHOPPING_SYSTEM_PROMPT, + response_mime_type="application/json", + response_schema=_ShoppingSuggestionsOut, + max_output_tokens=4096, + temperature=0.4, + ), + user_input=prompt, + ) + + raw = response.text + if not raw: + raise HTTPException(status_code=502, detail="LLM returned an empty response") + + try: + parsed = json.loads(raw) + except json.JSONDecodeError as e: + raise HTTPException(status_code=502, detail=f"LLM returned invalid JSON: {e}") + + return ShoppingSuggestionResponse( + suggestions=[ProductSuggestion(**s) for s in parsed.get("suggestions", [])], + reasoning=parsed.get("reasoning", ""), + ) diff --git a/backend/innercontext/mcp_server.py b/backend/innercontext/mcp_server.py index 5844551..9537148 100644 --- a/backend/innercontext/mcp_server.py +++ b/backend/innercontext/mcp_server.py @@ -1,13 +1,20 @@ from __future__ import annotations +import json +import os from datetime import date, datetime, timedelta from typing import Optional from uuid import UUID +import httpx +from fastapi import HTTPException from fastmcp import FastMCP +from google.genai import types as genai_types +from pydantic import BaseModel from sqlmodel import Session, col, select from db import engine +from innercontext.llm import call_gemini from innercontext.models import ( GroomingSchedule, LabResult, @@ -20,6 +27,36 @@ from innercontext.models import ( SkinConditionSnapshot, ) +# --------------------------------------------------------------------------- +# Pydantic schemas for structured output +# --------------------------------------------------------------------------- + + +class ProductSuggestionOut(BaseModel): + category: str + product_type: str + key_ingredients: list[str] + target_concerns: list[str] + why_needed: str + recommended_time: str + frequency: str + + +class ShoppingSuggestionsOut(BaseModel): + suggestions: list[ProductSuggestionOut] + reasoning: str + + +class MarketProduct(BaseModel): + name: str + brand: str + price: Optional[float] = None + currency: str = "PLN" + store: str + url: str + in_stock: Optional[bool] = None + + mcp = FastMCP("innercontext") @@ -435,3 +472,183 @@ def get_lab_results_for_test(test_code: str) -> list[dict]: .order_by(col(LabResult.collected_at).asc()) ).all() return [_lab_result_to_dict(r) for r in results] + + +# ── Shopping assistant ──────────────────────────────────────────────────────── + + +def _ev(v: object) -> str: + if v is None: + return "" + value = getattr(v, "value", None) + if isinstance(value, str): + return value + return str(v) + + +def _build_shopping_context(session: Session) -> tuple[str, list[dict]]: + snapshot = session.exec( + select(SkinConditionSnapshot).order_by( + col(SkinConditionSnapshot.snapshot_date).desc() + ) + ).first() + + skin_lines = ["STAN SKÓRY:"] + if snapshot: + skin_lines.append(f" Data: {snapshot.snapshot_date}") + skin_lines.append(f" Ogólny stan: {_ev(snapshot.overall_state)}") + skin_lines.append(f" Typ skóry: {_ev(snapshot.skin_type)}") + skin_lines.append(f" Nawilżenie: {snapshot.hydration_level}/5") + skin_lines.append(f" Wrażliwość: {snapshot.sensitivity_level}/5") + skin_lines.append(f" Bariera: {_ev(snapshot.barrier_state)}") + concerns = ", ".join(_ev(c) for c in (snapshot.active_concerns or [])) + skin_lines.append(f" Aktywne problemy: {concerns or 'brak'}") + if snapshot.priorities: + skin_lines.append(f" Priorytety: {', '.join(snapshot.priorities)}") + else: + skin_lines.append(" (brak danych)") + + stmt = select(Product).where(col(Product.is_tool).is_(False)) + products = session.exec(stmt).all() + + product_ids = [p.id for p in products] + inventory_rows = ( + session.exec( + select(ProductInventory).where( + col(ProductInventory.product_id).in_(product_ids) + ) + ).all() + if product_ids + else [] + ) + inv_by_product: dict[UUID, list[ProductInventory]] = {} + for inv in inventory_rows: + inv_by_product.setdefault(inv.product_id, []).append(inv) + + products_data = [] + for p in products: + if p.is_medication: + continue + active_inv = [i for i in inv_by_product.get(p.id, []) if i.finished_at is None] + has_stock = any(i.is_opened and i.finished_at is None for i in active_inv) + products_data.append( + { + "id": str(p.id), + "name": p.name, + "brand": p.brand or "", + "category": _ev(p.category), + "targets": p.targets or [], + "actives": [ + { + "name": a.get("name") if isinstance(a, dict) else a.name, + "percent": a.get("percent") if isinstance(a, dict) else None, + } + for a in (p.actives or []) + ], + "has_inventory": has_stock, + } + ) + + products_lines = ["POSIADANE PRODUKTY:"] + for p in products_data: + stock = "✓" if p["has_inventory"] else "✗" + products_lines.append( + f" [{stock}] {p['name']} ({p['brand']}) - {p['category']}, " + f"targets: {p['targets']}" + ) + + return "\n".join(skin_lines) + "\n\n" + "\n".join(products_lines), products_data + + +_SHOPPING_SYSTEM_PROMPT = """Jesteś asystentem zakupowym w dziedzinie pielęgnacji skóry. +Twoim zadaniem jest przeanalizować stan skóry użytkownika oraz produkty, które już posiada, +a następnie zasugerować TYPY produktów (bez marek), które mogłyby uzupełnić ich rutynę. + +ZASADY: +1. Sugeruj TYLKO typy produktów, NIGDY konkretne marki (np. "Salicylic Acid 2% Masque", nie "La Roche-Posay") +2. Koncentruj się na produktach, których użytkownik NIE posiada w swoim inventarzu +3. Bierz pod uwagę aktywne problemy skóry (acne, hyperpigmentacja, aging, etc.) +4. Sugeruj realistyczną częstotliwość użycia (dzienna, 2-3x tygodniowo, etc.) +5. Zachowaj kolejność warstw: cleanse → toner → serum → moisturizer → SPF +6. Jeśli użytkownik ma uszkodzoną barierę, unikaj silnych eksfoliantów i retinoidów +7. Odpowiadaj w języku polskim + +Format odpowiedzi - zwróć wyłącznie JSON zgodny z podanym schematem.""" + + +@mcp.tool() +def get_shopping_suggestions() -> dict: + """Analyze skin condition and inventory to suggest product types that could fill gaps. + Returns generic product suggestions (e.g., 'Salicylic Acid 2% Masque'), not specific brands. + """ + with Session(engine) as session: + context, products = _build_shopping_context(session) + + prompt = ( + f"Na podstawie poniższych danych przeanalizuj, jakie TYPY produktów " + f"mogłyby uzupełnić rutynę pielęgnacyjną użytkownika.\n\n" + f"{context}\n\n" + f"Zwróć wyłącznie JSON zgodny ze schematem." + ) + + response = call_gemini( + endpoint="shopping/suggest", + contents=prompt, + config=genai_types.GenerateContentConfig( + system_instruction=_SHOPPING_SYSTEM_PROMPT, + response_mime_type="application/json", + response_schema=ShoppingSuggestionsOut, + max_output_tokens=4096, + temperature=0.4, + ), + user_input=prompt, + ) + + raw = response.text + if not raw: + raise HTTPException(status_code=502, detail="LLM returned empty response") + + try: + parsed = json.loads(raw) + except json.JSONDecodeError as e: + raise HTTPException(status_code=502, detail=f"Invalid JSON from LLM: {e}") + + return { + "suggestions": parsed.get("suggestions", []), + "reasoning": parsed.get("reasoning", ""), + } + + +def _call_market_service( + query: str, stores: Optional[list[str]] = None +) -> list[MarketProduct]: + market_url = os.environ.get("MARKET_SERVICE_URL") + if not market_url: + return [] + + try: + with httpx.Client(timeout=10.0) as client: + resp = client.get( + f"{market_url}/search", + params={"q": query, "stores": stores or ["rossmann", "dm", "hebe"]}, + ) + resp.raise_for_status() + data = resp.json() + return [MarketProduct(**item) for item in data.get("products", [])] + except Exception: + return [] + + +@mcp.tool() +def search_market_products( + query: str, + stores: Optional[list[str]] = None, +) -> list[dict]: + """Search drug store catalogs for products matching the query. + Uses external market service to query Rossmann, DM, Hebe, etc. + + Examples: + - query: "salicylic acid serum acne" + - stores: ["rossmann", "dm", "hebe"] (optional, queries all by default)""" + products = _call_market_service(query, stores) + return [p.model_dump(mode="json") for p in products] diff --git a/frontend/messages/en.json b/frontend/messages/en.json index ec26efb..f3129da 100644 --- a/frontend/messages/en.json +++ b/frontend/messages/en.json @@ -33,6 +33,17 @@ "products_title": "Products", "products_count": "{count} products", "products_addNew": "+ Add product", + "products_suggest": "Suggest", + "products_suggestTitle": "Shopping suggestions", + "products_suggestSubtitle": "What to buy?", + "products_suggestDescription": "Based on your skin condition and products you own, I'll suggest product types that could complement your routine.", + "products_suggestGenerating": "Analyzing...", + "products_suggestBtn": "Generate suggestions", + "products_suggestResults": "Suggestions", + "products_suggestTime": "Time", + "products_suggestFrequency": "Frequency", + "products_suggestRegenerate": "Regenerate", + "products_suggestNoResults": "No suggestions.", "products_noProducts": "No products found.", "products_filterAll": "All", "products_filterOwned": "Owned", diff --git a/frontend/messages/pl.json b/frontend/messages/pl.json index a69932b..b3b8cb9 100644 --- a/frontend/messages/pl.json +++ b/frontend/messages/pl.json @@ -33,6 +33,17 @@ "products_title": "Produkty", "products_count": "{count} produktów", "products_addNew": "+ Dodaj produkt", + "products_suggest": "Sugeruj", + "products_suggestTitle": "Sugestie zakupowe", + "products_suggestSubtitle": "Co warto kupić?", + "products_suggestDescription": "Na podstawie Twojego stanu skóry i posiadanych produktów zasugeruję typy produktów, które mogłyby uzupełnić Twoją rutynę.", + "products_suggestGenerating": "Analizuję...", + "products_suggestBtn": "Generuj sugestie", + "products_suggestResults": "Propozycje", + "products_suggestTime": "Pora", + "products_suggestFrequency": "Częstotliwość", + "products_suggestRegenerate": "Wygeneruj ponownie", + "products_suggestNoResults": "Brak propozycji.", "products_noProducts": "Nie znaleziono produktów.", "products_filterAll": "Wszystkie", "products_filterOwned": "Posiadane", diff --git a/frontend/src/lib/types.ts b/frontend/src/lib/types.ts index 16aed33..472d3f0 100644 --- a/frontend/src/lib/types.ts +++ b/frontend/src/lib/types.ts @@ -216,6 +216,23 @@ export interface BatchSuggestion { overall_reasoning: string; } +// ─── Shopping suggestion types ─────────────────────────────────────────────── + +export interface ProductSuggestion { + category: string; + product_type: string; + key_ingredients: string[]; + target_concerns: string[]; + why_needed: string; + recommended_time: string; + frequency: string; +} + +export interface ShoppingSuggestionResponse { + suggestions: ProductSuggestion[]; + reasoning: string; +} + // ─── Health types ──────────────────────────────────────────────────────────── export interface MedicationUsage { diff --git a/frontend/src/routes/products/+page.svelte b/frontend/src/routes/products/+page.svelte index 47c5d10..a160e84 100644 --- a/frontend/src/routes/products/+page.svelte +++ b/frontend/src/routes/products/+page.svelte @@ -63,7 +63,10 @@

{m.products_title()}

{m.products_count({ count: totalCount })}

- +
+ + +
diff --git a/frontend/src/routes/products/suggest/+page.server.ts b/frontend/src/routes/products/suggest/+page.server.ts new file mode 100644 index 0000000..e5904f4 --- /dev/null +++ b/frontend/src/routes/products/suggest/+page.server.ts @@ -0,0 +1,26 @@ +import type { ActionData } from './$types'; +import { fail } from '@sveltejs/kit'; +import type { Actions } from './$types'; + +export const actions: Actions = { + suggest: async ({ fetch }) => { + try { + const res = await fetch('/api/products/suggest', { + method: 'POST', + }); + + if (!res.ok) { + const err = await res.text(); + return fail(res.status, { error: err || 'Failed to get suggestions' }); + } + + const data = await res.json(); + return { + suggestions: data.suggestions, + reasoning: data.reasoning, + }; + } catch (e) { + return fail(500, { error: String(e) }); + } + }, +} satisfies Actions; diff --git a/frontend/src/routes/products/suggest/+page.svelte b/frontend/src/routes/products/suggest/+page.svelte new file mode 100644 index 0000000..ee80398 --- /dev/null +++ b/frontend/src/routes/products/suggest/+page.svelte @@ -0,0 +1,126 @@ + + +{m["products_suggestTitle"]()} — innercontext + +
+
+ {m["products_backToList"]()} +

{m["products_suggestTitle"]()}

+
+ + {#if errorMsg} +
{errorMsg}
+ {/if} + + + {m["products_suggestSubtitle"]()} + +
+

+ {m["products_suggestDescription"]()} +

+ +
+ + + + {#if suggestions && suggestions.length > 0} + {#if reasoning} + + +

{reasoning}

+ + + {/if} + +
+

{m["products_suggestResults"]()}

+ {#each suggestions as s (s.product_type)} + + +
+
+

{s.product_type}

+ {s.category} +
+ + {#if s.key_ingredients.length > 0} +
+ {#each s.key_ingredients as ing (ing)} + {ing} + {/each} +
+ {/if} + + {#if s.target_concerns.length > 0} +
+ {#each s.target_concerns as concern (concern)} + {concern.replace(/_/g, ' ')} + {/each} +
+ {/if} + +

{s.why_needed}

+ +
+ {m["products_suggestTime"]()}: {s.recommended_time.toUpperCase()} + {m["products_suggestFrequency"]()}: {s.frequency} +
+
+ + + {/each} +
+ +
+ +
+ {:else if suggestions && suggestions.length === 0} + + + {m["products_suggestNoResults"]()} + + + {/if} +