2x2 grid, 1080x1080, do this for usa, turkey, spain, iran, language always english: 16:9, do this for paris: input: [regional_cuisine] :: [signature_dish] semantic_inference: infer(flavor_profile from [regional_cuisine].climate + trade_history) infer(plating_geometry from [signature_dish].traditional_preparation) infer(color_palette from [regional_cuisine].agricultural_base) equation: (plating_geometry::1.0 * architectural_section_logic::0.8) + (flavor_profile^0.6 * material_texture^0.4) - (restaurant_menu_layout::0.7 + food_photography_gloss::0.5) = culinary_infographic morphology: dish rendered as cross-sectional luxury brand asset ingredients become structural layers, spices become topographic contours negative space shaped by traditional serving vessels lighting/background: diffused gallery light | warm stone gradient | micro-shadow depth for layer separation negative: no steam, no rustic wood tables, no overhead flat lay, no chef hands, no saturation boost output: 1x1 premium editorial spread. ai infers all structural, chromatic, and cultural mappings from variables.