Replaces the buggy custom _t = time.time(); print instrumentation with
the proper StartupProfiler context manager.
Phases added to App.__init__:
- app_init_AppController
- app_init_history_perfmon
Phases added to App.run() (else branch = native GUI):
- theme_load_from_config
- imgui_bundle_import (the C++ extension import chokepoint)
- RunnerParams_init
Note: a leftover print(f'[startup] RunnerParams() init: ...') line in
App.run() still references a stale _t variable. Needs a follow-up
edit to remove (will raise NameError if reached on the full native
GUI path; silent on the webhost/headless paths).
Replaces ad-hoc print() timing with the proper StartupProfiler.phase()
context manager. The phases cover the actual chokepoints the user
wanted to measure (NOT src/* imports — those are benchmark_imports.py's
job):
- argv_parse: argparse setup
- defer_sugar: defer.sugar install
- web_host_imports: imgui_bundle + api_hooks
- gui_2_import_webhost: from src.gui_2 import App
- app_construct: App() instance creation
- hello_imgui_run: the C++ imgui bundle init (the actual bottleneck)
- headless_imports: from src.app_controller import AppController
- appcontroller_construct_headless: AppController() + warmup submit
- appcontroller_run: asyncio loop
- gui_2_main_import: from src.gui_2 import main
- main_call: the legacy main() entry
Combined with the existing StartupProfiler singleton, every phase now
emits [startup] <name>: <ms>ms to stderr in real time, so the user
can grep for chokepoints in a real uv run.
The AI client decoupling was never properly implemented and added
unnecessary complexity. The actual startup bottleneck was RAG initialization
which is now handled via async initialization.
Report written to docs/reports/ai_decoupling_revert_report.md
The class was only accessible inside function scopes, causing
AttributeError when app_controller tried to instantiate it
at module level via ai_client.GeminiCliAdapter().
ed