- Add tests/e2e/test_smoke.py: page-load error check for all pages
- Add tests/e2e/test_interactions.py: click every interactable, diff
errors, XFAIL expected demo failures, flag regressions as XPASS
- Fix conftest get_page_errors() to use text_content() instead of
inner_text() so errors inside collapsed expanders are captured with
their actual message text (inner_text respects CSS display:none)
- Fix tests/e2e/modes/demo.py base_url to include /peregrine path prefix
(STREAMLIT_SERVER_BASE_URL_PATH=peregrine set in demo container)
App fixes surfaced by the harness:
- task_runner.py: add DEMO_MODE guard for discovery task — previously
crashed with FileNotFoundError on search_profiles.yaml before any
demo guard could fire; now returns friendly error immediately
- 6_Interview_Prep.py: stop auto-triggering LLM session on page load
in demo mode; show "AI features disabled" info instead, preventing
a silent st.error() inside the collapsed Practice Q&A expander
Both smoke and interaction tests now pass clean against demo mode.
When source == "jobgether", build_prompt() injects a recruiter context
note directing the LLM to address the Jobgether recruiter using
"Your client [at {company}] will appreciate..." framing rather than
addressing the employer directly. generate() and task_runner both
thread the is_jobgether flag through automatically.
Replaces the spawn-per-task model for LLM task types with scheduler
routing: cover_letter, company_research, and wizard_generate are now
enqueued via the TaskScheduler singleton for VRAM-aware batching.
Non-LLM tasks (discovery, email_sync, etc.) continue to spawn daemon
threads directly. Adds autouse clean_scheduler fixture to
test_task_runner.py to prevent singleton cross-test contamination.
- setup.sh: replace docker-image-based NVIDIA test with nvidia-ctk validate
(faster, no 100MB pull, no daemon required); add check_docker_running()
to auto-start the Docker service on Linux or warn on macOS
- prepare_training_data.py: also scan training_data/uploads/*.{md,txt}
so web-uploaded letters are included in training data
- task_runner.py: add prepare_training task type (calls build_records +
write_jsonl inline; reports pair count in task result)
- Settings fine-tune tab: Step 1 accepts .md/.txt uploads; Step 2 Extract
button submits prepare_training background task + shows status; Step 3
shows make finetune command + live Ollama model status poller
- generate() accepts previous_result + feedback; appends both to LLM prompt
- task_runner cover_letter handler parses params JSON, passes fields through
- Apply Workspace: "Refine with Feedback" expander with text area + Regenerate
button; only shown when a draft exists; clears feedback after submitting
- 8 new tests (TestGenerateRefinement + TestTaskRunnerCoverLetterParams)
- Add tier, dev_tier_override, wizard_complete, wizard_step, dismissed_banners
fields to UserProfile with defaults and effective_tier property
- Add params TEXT column to background_tasks table (CREATE + migration)
- Update insert_task() to accept params with params-aware dedup logic
- Update submit_task() and _run_task() to thread params through
- Add test_wizard_defaults, test_effective_tier_override,
test_effective_tier_no_override, and test_insert_task_with_params
