Keep /products/suggest lean by exposing product UUIDs and fetching INCI, safety rules, actives, and usage notes on demand through Gemini function tools. Add conservative fallback behavior for tool roundtrip limits and expand helper tests to cover tool wiring and payload handlers.
Keep the /routines/suggest base context lean by sending only active names and fetching detailed safety, actives, usage notes, and INCI on demand. Add a conservative fallback when tool roundtrip limits are hit to preserve safe outputs instead of failing the request.
Enable on-demand INCI retrieval in /routines/suggest through Gemini function calling so detailed ingredient data is fetched only when needed. Persist and normalize tool_trace data in AI logs to make function-call behavior directly inspectable via /ai-logs endpoints.
Expose leave-on behavior, contraindications, safety alerts, and compact usage notes in AVAILABLE PRODUCTS so Gemini can make safer routine decisions with real-world product constraints.
Introduces `get_extraction_config` and `get_creative_config` to standardize Gemini API calls.
* Defines explicit config profiles with appropriate `temperature` and `thinking_level` for Gemini 3 Flash.
* Extraction tasks use minimal thinking and temp=0.0 to reduce latency and token usage.
* Creative tasks use low thinking, temp=0.4, and top_p=0.8 to balance naturalness and safety.
* Applies these helpers across products, routines, and skincare endpoints.
* Also updates default model to `gemini-3-flash-preview`.
- 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
- Remove _build_inventory_context; fold pao_months into DOSTĘPNE PRODUKTY entries
- Remove "Otwarte równolegle" duplicate section from prompt
- Rename OSTATNIE RUTYNY (7 dni) → OSTATNIE RUTYNY
- Add _build_day_context and SuggestRoutineRequest.leaving_home (optional bool)
- System prompt: replace unconditional PAO rule with conditional; add SPF factor
selection logic based on KONTEKST DNIA leaving_home value
- Frontend: leaving_home checkbox (AM only) + i18n keys pl/en
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Gemini API rejects int-valued enums (StrengthLevel) in response_schema,
raising a validation error before any request is sent. Fix by introducing
AIActiveIngredient (inherits ActiveIngredient, overrides strength_level and
irritation_potential as Optional[int]) and ProductParseLLMResponse used only
as the Gemini schema. The two-step validation converts ints back to StrengthLevel
via Pydantic coercion. Adds a test covering the numeric strength level path.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
gemini-flash-latest resolves to gemini-3-flash-preview which uses
thinking_level instead of the legacy thinking_budget (mixing both
returns HTTP 400). Use LOW to reduce thinking overhead while keeping
basic reasoning, replacing the now-incompatible thinking_budget=0.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
When Gemini stops generation early (e.g. due to safety filters or
thinking-model quirks), finish_reason != STOP but no exception is raised,
causing the caller to receive truncated JSON and a confusing 502 "invalid
JSON" error. Now:
- finish_reason is extracted from candidates[0] and stored in ai_call_logs
- any non-STOP finish_reason raises HTTP 502 with a clear message
- Alembic migration adds the finish_reason column to ai_call_logs
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add include_minoxidil_beard flag to SuggestRoutineRequest and SuggestBatchRequest
- Detect minoxidil products by scanning name, brand, INCI and actives; pass them
to the LLM even though they are medications
- Inject CELE UŻYTKOWNIKA context block into prompts when flag is enabled
- Add _build_objectives_context() returning empty string when flag is off
- Add call_gemini() helper that centralises Gemini API calls and logs every
request/response to a new ai_call_logs table (AICallLog model + /ai-logs router)
- Nginx: raise client_max_body_size to 16 MB for photo uploads
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Batch-load all routine steps in a single query and attach them to each
routine dict, mirroring the detail endpoint pattern. Fixes "0 steps"
shown on the routines list page.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Without cascade, SQLAlchemy tried to NULL-out foreign keys on child rows
before deleting the parent, hitting NOT NULL constraints in PostgreSQL.
- Routine.steps: cascade="all, delete-orphan" (routine_steps.routine_id)
- MedicationEntry.usage_history: cascade="all, delete-orphan"
(medication_usages.medication_record_id)
Product.inventory already had cascade set correctly.
No DB migration needed — ORM-level only.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Pass response_schema to all three generate_content calls so Gemini
constrains its output to valid enum values and correct JSON structure:
- routines.py: _StepOut.action_type Optional[str] → Optional[GroomingAction]
- skincare.py: add _SkinAnalysisOut(PydanticBase) with OverallSkinState,
SkinType, SkinTexture, BarrierState, SkinConcern enums; add response_schema
- products.py: pass ProductParseResponse directly as response_schema;
remove NaN/Infinity/undefined regex cleanup, markdown-fence extraction,
finish_reason logging, and re import — all now unnecessary
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add Gemini-powered endpoints and frontend pages for proposing skincare
routines based on skin state, product compatibility, grooming schedule,
and recent history.
Backend (routines.py):
- POST /routines/suggest — single AM/PM routine for a date
- POST /routines/suggest-batch — AM+PM plan for up to 14 days
- Prompt context: skin snapshot, grooming schedule, 7-day history,
filtered product list with effects/incompatibilities/context rules
- Respects retinoid frequency limits, acid/retinoid separation,
grooming-aware safe_after_shaving rules
Frontend:
- /routines/suggest page with tab switcher (single / batch)
- Single tab: date + AM/PM + optional notes → generate → preview → save
- Batch tab: date range + notes → collapsible day cards (AM+PM) → save all
- Loading spinner during Gemini calls; product names resolved from map
- "Zaproponuj rutynę AI" button added to routines list page
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Replace category filter dropdown with client-side grouping and a
3-way ownership toggle (All / Owned / Not owned). Products are grouped
by category with header rows as visual dividers, sorted brand → name
within each group. Category column removed (redundant with headings).
Backend: GET /products now returns ProductWithInventory so inventory
data is available for ownership filtering (bulk-loaded in one query).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
SQLAlchemy was nulling out product_id on ProductInventory rows instead
of deleting them. Added cascade="all, delete-orphan" to the ORM
relationship and ondelete="CASCADE" to the FK field.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Model was emitting "anti_aging" as a valid ingredient function
(e.g. for retinoids, peptides). Add it to the enum and the
parse-text system prompt allowed values.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Regular generation was hitting MAX_TOKENS at 8192. Constrained decoding with
16384 should be a viable middle ground between the truncation at 8192 and the
timeout at 65536.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Constrained decoding is ~10x slower and consumes hidden tokens for constraint
processing, causing truncation at ~1000 chars even with 8192 max_output_tokens.
The system prompt already instructs the model to output raw minified JSON; our
NaN/markdown-fence sanitisation handles edge cases.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Replace truncation-recovery heuristic with a higher token budget.
On JSON parse failure, log finish_reason and 160-char error context
to make the root cause visible.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Pretty-printed JSON wastes 2-3x tokens on indentation/newlines.
Minified output fits more data (e.g. long INCI lists) within the
8192 output token limit.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Models sometimes emit JS-style literals for unknown numeric fields.
Replace NaN, Infinity, undefined with null before parsing.
Also add error logging to capture raw response on parse failure.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Increase max_output_tokens 4096 → 8192 to prevent truncated JSON on
products with long INCI lists
- Return explicit 502 when response.text is None (safety filter blocks)
- Fallback JSON extraction strips markdown fences or leading preamble
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add backend/innercontext/mcp_server.py with tools covering products,
inventory, routines, skin snapshots, medications, lab results, and
grooming schedule
- Mount MCP app at /mcp in main.py using combine_lifespans
- Fix test isolation: patch app.router.lifespan_context in conftest to
avoid StreamableHTTPSessionManager single-run limitation
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Remove the derived `trend` field (better computed from history by the MCP
agent) and add `texture: smooth|rough|flaky|bumpy` which LLM can reliably
assess from photos. Updates model, API, system prompt, tests, and frontend.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Pass `text=` as keyword arg to Part.from_text() and raise max_output_tokens
from 1024 to 2048 to prevent JSON truncation in the notes field.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add POST /skincare/analyze-photos endpoint that accepts 1–3 skin
photos, sends them to Gemini vision, and returns a structured
SkinPhotoAnalysisResponse for pre-filling the snapshot form.
Extract shared Gemini client setup into innercontext/llm.py
(get_gemini_client) so both products and skincare use a single
default model (gemini-flash-latest) and API key check.
Frontend: AI photo card on /skin page with file picker, previews,
and auto-fill of all form fields from the analysis result.
New fields (skin_type, sebum_tzone, sebum_cheeks) added to form
and server action.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add POST /products/parse-text endpoint that accepts raw product text,
calls Gemini (google-genai) with a structured extraction prompt, and
returns a partial ProductParseResponse. Frontend gains a collapsible
"AI pre-fill" card at the top of ProductForm that merges the LLM
response into all form fields reactively.
- Backend: ProductParseRequest/Response schemas, system prompt with
enum constraints, temperature=0.0 for deterministic extraction,
effect_profile always returned in full
- Frontend: parseProductText() in api.ts; controlled $state bindings
for all text/number/checkbox inputs; applyAiResult() merges response
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Adds full_weight_g and empty_weight_g to ProductBase (inherited by Product
and response models) so per-product package weight specs are captured.
Adds last_weighed_at to ProductInventory to record when a package was last
weighed. Wires up all fields through API schemas, frontend types, forms, and
the product detail page (add/edit/display).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
