Add pytest-playwright and pytest-json-report to requirements.txt; create
tests/e2e/ skeleton (modes/, pages/, results/) with __init__.py files and
.gitkeep; add results subdirs to .gitignore.
Implements idempotent calendar push for Apple Calendar (CalDAV) and
Google Calendar from the Interviews kanban.
- db: add calendar_event_id column (migration) + set_calendar_event_id helper
- integrations/apple_calendar: create_event / update_event via caldav + icalendar
- integrations/google_calendar: create_event / update_event via google-api-python-client;
test() now makes a real API call instead of checking file existence
- scripts/calendar_push: orchestrates push/update, builds event title from stage +
job title + company, attaches job URL and company brief to description,
defaults to noon UTC / 1hr duration
- app/pages/5_Interviews: "Add to Calendar" / "Update Calendar" button shown
when interview date is set and a calendar integration is configured
- environment.yml: pin caldav, icalendar, google-api-python-client, google-auth
- tests/test_calendar_push: 9 tests covering create, update, error handling,
event timing, idempotency, and missing job/date guards
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.
T13: Three fixes:
1. backup.py: _decrypt_db_to_bytes() decrypts SQLCipher DB before archiving
so the zip is portable to any local Docker install (plain SQLite).
2. backup.py: _encrypt_db_from_bytes() re-encrypts on restore in cloud mode
so the app can open the restored DB normally.
3. 2_Settings.py: _base_dir uses get_db_path().parent in cloud mode (user's
per-tenant data dir) instead of the hardcoded app root; db_key wired
through both create_backup() and restore_backup() calls.
6 new cloud backup tests + 2 unit tests for SQLCipher helpers (pysqlcipher3
mocked — not available in the local conda test env). 419/419 total passing.
T8: compose.cloud.yml — multi-tenant cloud stack on port 8505, CLOUD_MODE=true,
per-user encrypted data at /devl/menagerie-data, joins caddy-proxy_caddy-internal
network; .env.example extended with five cloud-only env vars.
T10: app/telemetry.py — log_usage_event() is the ONLY entry point to usage_events
table; hard kill switch (all_disabled) checked before any DB write; complete no-op
in local mode; swallows all exceptions so telemetry never crashes the app;
psycopg2-binary added to requirements.txt. Event calls wired into 4_Apply.py at
cover_letter_generated and job_applied. 5 tests, 413/413 total passing.
cloud_session.py: no-op in local mode; in cloud mode resolves Directus JWT
from X-CF-Session header to per-user db_path in st.session_state.
get_connection() in scripts/db.py: transparent SQLCipher/sqlite3 switch —
uses encrypted driver when CLOUD_MODE=true and key provided, vanilla sqlite3
otherwise. libsqlcipher-dev added to Dockerfile for Docker builds.
6 new cloud_session tests + 1 new get_connection test — 34/34 db tests pass.
Document defensive behavior: openai_compat with no base_url returns True
(cloud) because unknown destination is assumed cloud. Add explanatory
comment to LOCAL_URL_MARKERS for the 0.0.0.0 bind-address case.
Replaces NotImplementedError stub with full LLM-backed implementation.
Builds a prompt from the last 3 resume positions plus already-selected
skills/domains/keywords, calls LLMRouter, and returns de-duped suggestions
in all three categories.
Replaces NotImplementedError stub with a real LLMRouter-backed implementation
that builds a structured prompt covering blocklist alias expansion, values
misalignment, and role-type filtering, then parses the JSON response into
suggested_titles and suggested_excludes lists.
Moves LLMRouter import to module level so tests can patch it at
scripts.suggest_helpers.LLMRouter.
BYOK policy: if a user supplies any LLM backend (local ollama/vllm or
their own API key), they get full access to AI generation features.
Charging for the UI around a service they already pay for is bad UX.
app/wizard/tiers.py:
- BYOK_UNLOCKABLE frozenset: pure LLM-call features that unlock with
any configured backend (llm_career_summary, company_research,
interview_prep, survey_assistant, voice guidelines, etc.)
- has_configured_llm(): checks llm.yaml for any enabled non-vision
backend; local + external API keys both count
- can_use(tier, feature, has_byok=False): BYOK_UNLOCKABLE features
return True when has_byok=True regardless of tier
- tier_label(feature, has_byok=False): suppresses lock icon for
BYOK_UNLOCKABLE features when BYOK is active
Still gated (require CF infrastructure, not just an LLM call):
llm_keywords_blocklist, email_classifier, model_fine_tuning,
shared_cover_writer_model, multi_user, all integrations
app/pages/2_Settings.py:
- Compute _byok = has_configured_llm() once at page load
- Pass has_byok=_byok to can_use() for _gen_panel_active
- Update caption to mention BYOK as an alternative to paid tier
app/pages/0_Setup.py:
- Wizard generation widget passes has_byok=has_configured_llm()
to can_use() and tier_label()
tests/test_wizard_tiers.py:
- 6 new BYOK-specific tests covering unlock, non-unlock, and
label suppression cases
Primary parse path is now fully deterministic — no LLM, no token limits,
no JSON generation. Handles two-column experience headers, institution-before-
or-after-degree education layouts, and header bleed prevention via
looks_like_header detection.
LLM path retained as optional career_summary enhancement only (1500 chars,
falls back silently). structure_resume() now returns tuple[dict, str].
Tests updated to match the new API.
- 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)
Replaces the old 5-step wizard with a 7-step orchestrator that uses the
step modules built in Tasks 2-8. Steps 1-6 are mandatory (hardware, tier,
identity, resume, inference, search); step 7 (integrations) is optional.
Each Next click validates, writes wizard_step to user.yaml for crash recovery,
and resumes at the correct step on page reload. LLM generation buttons
submit wizard_generate tasks and poll via @st.fragment(run_every=3). Finish
sets wizard_complete=True, removes wizard_step, and calls apply_service_urls.
Adds tests/test_wizard_flow.py (7 tests) covering validate() chain, yaml
persistence helpers, and wizard state inference.
