One last thing before we move on. pass this to mark the lesson done, or skip and keep moving. hop to the next when you're ready.
Final image drill. You're auditing a real image pipeline at an agency.
Write audit_image_pipeline(tasks) that takes a list of task
dicts (same shape as the previous step) and returns a list of
dicts, one per task, in the same order:
{"task_id": ..., "model": ..., "specialist_used": bool}
model comes from your pick_image_model(task) routing.
specialist_used is True if the chosen model is one of the
three specialty picks: ideogram-v4, recraftv4_1, or
flux-2-pro. Everything else is False (including the default
gemini-3.1-flash-image, Lite, gpt-image-2, and
gemini-3-pro-image).
Each task carries a task_id field (string). Preserve it in
the output.
Five tasks run. Expected output:
T1: ideogram-v4 specialist=True
T2: gpt-image-2 specialist=False
T3: gemini-3.1-flash-image specialist=False
T4: recraftv4_1 specialist=True
T5: gemini-3-pro-image specialist=False