Grille 2x2, ratio 1:1, faites ceci pour 4 plats internationaux célèbres :
# In[1] : Importer les bibliothèques de chimie des saveurs
import auto_inference_engine as ai
import editorial_renderer as er
subject = "[$DISH_NAME]"
# In[2] : Déconstruire en composés volatils
df_gastronomy = ai.dissect_dish(subject, layers=['Nuage aromatique', 'Matrice de texture', 'Toile de Maillard', 'Mémoire culturelle'])
df_gastronomy['pairing_note'] = df_gastronomy.apply(lambda row: ai.suggest_wine_pairing(row.molecule), axis=1)
# In[3] : Construire une vue éclatée comestible
fig = er.Canvas(style="Photographie gastronomique haut de gamme en 3D", lighting="Softbox et vapeur en contre-jour", dof="Mise au point sélective sur les composés aromatiques")
fig.add_title(f"L’ARCHITECTURE INVISIBLE DE {subject.upper()}")
for index, layer in df_gastronomy.iterrows():
frame = fig.add_frame(shape="Segment en coin de roue des saveurs", size=layer.intensity_value)
frame.render_3d_model(layer.ingredient_scan, lighting="Brillance et translucidité")
frame.add_tasting_label(f"{layer.sensory_layer} | {layer.key_molecule} | {layer.pairing_note}")
fig.draw_aroma_trail(previous_frame, frame, style="Volute volatile")
fig.add_sidebar("Carnet du chef : origines des techniques, modificateurs de texture")
fig.add_tasting_grid(df_gastronomy[['sensory_layer', 'key_molecule', 'pairing_note', 'umami_bump']])
# In[4] : Rendu
fig.render(quality="Photoréalisme éditorial digne du Guide Michelin")
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