feat: AI photo analysis for skin snapshots

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>

backend/innercontext/api/products.py

@@ -1,17 +1,16 @@
 import json
-import os
 from datetime import date
 from typing import Optional
 from uuid import UUID, uuid4
 
 from fastapi import APIRouter, Depends, HTTPException, Query
-from google import genai
 from google.genai import types as genai_types
 from pydantic import ValidationError
 from sqlmodel import Session, SQLModel, select
 
 from db import get_session
 from innercontext.api.utils import get_or_404
+from innercontext.llm import get_gemini_client
 from innercontext.models import (
     Product,
     ProductBase,
@@ -350,11 +349,7 @@ OUTPUT SCHEMA (all fields optional — omit what you cannot determine):
 
 @router.post("/parse-text", response_model=ProductParseResponse)
 def parse_product_text(data: ProductParseRequest) -> ProductParseResponse:
-    api_key = os.environ.get("GEMINI_API_KEY")
-    if not api_key:
-        raise HTTPException(status_code=503, detail="GEMINI_API_KEY not configured")
-    model = os.environ.get("GEMINI_MODEL", "gemini-flash-latest")
-    client = genai.Client(api_key=api_key)
+    client, model = get_gemini_client()
     response = client.models.generate_content(
         model=model,
         contents=f"Extract product data from this text:\n\n{data.text}",