- Pop _linkedin_extracted before st.tabs() so tab_builder sees the
freshly populated _parsed_resume in the same render pass (no extra rerun needed)
- Fix tab label capitalisation: "Build Manually" (capital M) per spec
- Add st.rerun() after LinkedIn merge in Settings so form fields
refresh immediately to show the newly applied data
resolve_session() is a no-op in local mode — no behavior change for existing users.
In cloud mode, injects user-scoped db_path into st.session_state at page load.
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
- preflight.py now writes PEREGRINE_GPU_COUNT and PEREGRINE_GPU_NAMES to
.env so the app container gets GPU info without needing nvidia-smi access
- compose.yml passes PEREGRINE_GPU_COUNT, PEREGRINE_GPU_NAMES, and
RECOMMENDED_PROFILE as env vars to the app service
- 0_Setup.py _detect_gpus() reads PEREGRINE_GPU_NAMES env var first;
falls back to nvidia-smi (bare / GPU-passthrough environments)
- 0_Setup.py _suggest_profile() reads RECOMMENDED_PROFILE env var first
- requirements.txt: add pdfplumber (needed for resume PDF parsing)
- llm.yaml + example: replace localhost URLs with Docker service names
(ollama:11434, vllm:8000, vision:8002); replace personal model names
(alex-cover-writer, llama3.1:8b) with llama3.2:3b
- user.yaml.example: update service hosts to Docker names (ollama, vllm,
searxng) and searxng port from 8888 (host-mapped) to 8080 (internal)
- wizard step 5: fix hardcoded localhost defaults — wizard runs inside
Docker, so service name defaults are required for connection tests to pass
- scrapers/companyScraper.py: bundle scraper so Dockerfile COPY succeeds
- setup.sh: remove host Ollama install (conflicts with Docker Ollama on
port 11434); Docker entrypoint handles model download automatically
- README + setup.sh banner: add Circuit Forge mission statement
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.
