fix: resume CID glyphs, resume YAML path, PyJWT dep, candidate voice & mission UI
- resume_parser: add _clean_cid() to strip (cid:NNN) glyph refs from ATS PDFs; CIDs 127/149/183 become bullets, unknowns are stripped; applied to PDF/DOCX/ODT - resume YAML: canonicalize plain_text_resume.yaml path to config/ across all references (Settings, Apply, Setup, company_research, migrate); was pointing at unmounted aihawk/data_folder/ in Docker - requirements/environment: add PyJWT>=2.8 (was missing; broke Settings page) - user_profile: add candidate_voice field - generate_cover_letter: inject candidate_voice into SYSTEM_CONTEXT; add social_impact mission signal category (nonprofit, community, equity, etc.) - Settings: add Voice & Personality textarea to Identity expander; add Mission & Values expander with editable fields for all 4 mission categories - .gitignore: exclude CLAUDE.md, config/plain_text_resume.yaml, config/user.yaml.working - search_profiles: add default profile
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parent
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5
.gitignore
vendored
5
.gitignore
vendored
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@ -19,6 +19,7 @@ unsloth_compiled_cache/
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data/survey_screenshots/*
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!data/survey_screenshots/.gitkeep
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config/user.yaml
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config/plain_text_resume.yaml
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config/.backup-*
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config/integrations/*.yaml
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!config/integrations/*.yaml.example
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@ -30,3 +31,7 @@ scrapers/raw_scrapes/
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compose.override.yml
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config/license.json
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config/user.yaml.working
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# Claude context files — kept out of version control
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CLAUDE.md
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212
CLAUDE.md
212
CLAUDE.md
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@ -1,212 +0,0 @@
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# Job Seeker Platform — Claude Context
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## Project
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Automated job discovery + resume matching + application pipeline for Meghan McCann.
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Full pipeline:
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```
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JobSpy → discover.py → SQLite (staging.db) → match.py → Job Review UI
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→ Apply Workspace (cover letter + PDF) → Interviews kanban
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→ phone_screen → interviewing → offer → hired
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↓
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Notion DB (synced via sync.py)
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```
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## Environment
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- Python env: `conda run -n job-seeker <cmd>` — always use this, never bare python
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- Run tests: `/devl/miniconda3/envs/job-seeker/bin/pytest tests/ -v`
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(use direct binary — `conda run pytest` can spawn runaway processes)
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- Run discovery: `conda run -n job-seeker python scripts/discover.py`
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- Recreate env: `conda env create -f environment.yml`
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- pytest.ini scopes test collection to `tests/` only — never widen this
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## ⚠️ AIHawk env isolation — CRITICAL
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- NEVER `pip install -r aihawk/requirements.txt` into the job-seeker env
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- AIHawk pulls torch + CUDA (~7GB) which causes OOM during test runs
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- AIHawk must run in its own env: `conda create -n aihawk-env python=3.12`
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- job-seeker env must stay lightweight (no torch, no sentence-transformers, no CUDA)
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## Web UI (Streamlit)
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- Run: `bash scripts/manage-ui.sh start` → http://localhost:8501
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- Manage: `start | stop | restart | status | logs`
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- Direct binary: `/devl/miniconda3/envs/job-seeker/bin/streamlit run app/app.py`
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- Entry point: `app/app.py` (uses `st.navigation()` — do NOT run `app/Home.py` directly)
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- `staging.db` is gitignored — SQLite staging layer between discovery and Notion
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### Pages
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| Page | File | Purpose |
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|------|------|---------|
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| Home | `app/Home.py` | Dashboard, discovery trigger, danger-zone purge |
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| Job Review | `app/pages/1_Job_Review.py` | Batch approve/reject with sorting |
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| Settings | `app/pages/2_Settings.py` | LLM backends, search profiles, Notion, services |
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| Resume Profile | Settings → Resume Profile tab | Edit AIHawk YAML profile (was standalone `3_Resume_Editor.py`) |
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| Apply Workspace | `app/pages/4_Apply.py` | Cover letter gen + PDF export + mark applied + reject listing |
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| Interviews | `app/pages/5_Interviews.py` | Kanban: phone_screen→interviewing→offer→hired |
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| Interview Prep | `app/pages/6_Interview_Prep.py` | Live reference sheet during calls + Practice Q&A |
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| Survey Assistant | `app/pages/7_Survey.py` | Culture-fit survey help: text paste + screenshot (moondream2) |
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## Job Status Pipeline
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```
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pending → approved/rejected (Job Review)
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approved → applied (Apply Workspace — mark applied)
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approved → rejected (Apply Workspace — reject listing button)
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applied → survey (Interviews — "📋 Survey" button; pre-kanban section)
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applied → phone_screen (Interviews — triggers company research)
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survey → phone_screen (Interviews — after survey completed)
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phone_screen → interviewing
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interviewing → offer
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offer → hired
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any stage → rejected (rejection_stage captured for analytics)
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applied/approved → synced (sync.py → Notion)
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```
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## SQLite Schema (`staging.db`)
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### `jobs` table key columns
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- Standard: `id, title, company, url, source, location, is_remote, salary, description`
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- Scores: `match_score, keyword_gaps`
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- Dates: `date_found, applied_at, survey_at, phone_screen_at, interviewing_at, offer_at, hired_at`
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- Interview: `interview_date, rejection_stage`
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- Content: `cover_letter, notion_page_id`
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### Additional tables
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- `job_contacts` — email thread log per job (direction, subject, from/to, body, received_at)
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- `company_research` — LLM-generated brief per job (company_brief, ceo_brief, talking_points, raw_output, accessibility_brief)
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- `background_tasks` — async LLM task queue (task_type, job_id, status: queued/running/completed/failed)
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- `survey_responses` — per-job Q&A pairs (survey_name, received_at, source, raw_input, image_path, mode, llm_output, reported_score)
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## Scripts
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| Script | Purpose |
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|--------|---------|
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| `scripts/discover.py` | JobSpy + custom board scrape → SQLite insert |
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| `scripts/custom_boards/adzuna.py` | Adzuna Jobs API (app_id + app_key in config/adzuna.yaml) |
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| `scripts/custom_boards/theladders.py` | The Ladders scraper via curl_cffi + __NEXT_DATA__ SSR parse |
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| `scripts/match.py` | Resume keyword matching → match_score |
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| `scripts/sync.py` | Push approved/applied jobs to Notion |
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| `scripts/llm_router.py` | LLM fallback chain (reads config/llm.yaml) |
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| `scripts/generate_cover_letter.py` | Cover letter via LLM; detects mission-aligned companies (music/animal welfare/education) and injects Para 3 hint |
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| `scripts/company_research.py` | Pre-interview brief via LLM + optional SearXNG scrape; includes Inclusion & Accessibility section |
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| `scripts/prepare_training_data.py` | Extract cover letter JSONL for fine-tuning |
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| `scripts/finetune_local.py` | Unsloth QLoRA fine-tune on local GPU |
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| `scripts/db.py` | All SQLite helpers (single source of truth) |
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| `scripts/task_runner.py` | Background thread executor — `submit_task(db, type, job_id)` dispatches daemon threads for LLM jobs |
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| `scripts/vision_service/main.py` | FastAPI moondream2 inference on port 8002; `manage-vision.sh` lifecycle |
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## LLM Router
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- Config: `config/llm.yaml`
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- Cover letter fallback order: `claude_code → ollama (meghan-cover-writer:latest) → vllm → copilot → anthropic`
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- Research fallback order: `claude_code → vllm (__auto__, ouroboros) → ollama_research (llama3.1:8b) → ...`
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- `meghan-cover-writer:latest` is cover-letter only — it doesn't follow structured markdown prompts for research
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- `LLMRouter.complete()` accepts `fallback_order=` override for per-task routing
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- `LLMRouter.complete()` accepts `images: list[str]` (base64) — vision backends only; non-vision backends skipped when images present
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- Vision fallback order config key: `vision_fallback_order: [vision_service, claude_code, anthropic]`
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- `vision_service` backend type: POST to `/analyze`; skipped automatically when no images provided
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- Claude Code wrapper: `/Library/Documents/Post Fight Processing/server-openai-wrapper-v2.js`
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- Copilot wrapper: `/Library/Documents/Post Fight Processing/manage-copilot.sh start`
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## Fine-Tuned Model
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- Model: `meghan-cover-writer:latest` registered in Ollama
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- Base: `unsloth/Llama-3.2-3B-Instruct` (QLoRA, rank 16, 10 epochs)
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- Training data: 62 cover letters from `/Library/Documents/JobSearch/`
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- JSONL: `/Library/Documents/JobSearch/training_data/cover_letters.jsonl`
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- Adapter: `/Library/Documents/JobSearch/training_data/finetune_output/adapter/`
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- Merged: `/Library/Documents/JobSearch/training_data/gguf/meghan-cover-writer/`
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- Re-train: `conda run -n ogma python scripts/finetune_local.py`
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(uses `ogma` env with unsloth + trl; pin to GPU 0 with `CUDA_VISIBLE_DEVICES=0`)
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## Background Tasks
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- Cover letter gen and company research run as daemon threads via `scripts/task_runner.py`
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- Tasks survive page navigation; results written to existing tables when done
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- On server restart, `app.py` startup clears any stuck `running`/`queued` rows to `failed`
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- Dedup: only one queued/running task per `(task_type, job_id)` at a time
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- Sidebar indicator (`app/app.py`) polls every 3s via `@st.fragment(run_every=3)`
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- ⚠️ Streamlit fragment + sidebar: use `with st.sidebar: _fragment()` — sidebar context must WRAP the call, not be inside the fragment body
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## Vision Service
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- Script: `scripts/vision_service/main.py` (FastAPI, port 8002)
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- Model: `vikhyatk/moondream2` revision `2025-01-09` — lazy-loaded on first `/analyze` (~1.8GB download)
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- GPU: 4-bit quantization when CUDA available (~1.5GB VRAM); CPU fallback
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- Conda env: `job-seeker-vision` — separate from job-seeker (torch + transformers live here)
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- Create env: `conda env create -f scripts/vision_service/environment.yml`
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- Manage: `bash scripts/manage-vision.sh start|stop|restart|status|logs`
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- Survey page degrades gracefully to text-only when vision service is down
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- ⚠️ Never install vision deps (torch, bitsandbytes, transformers) into the job-seeker env
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## Company Research
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- Script: `scripts/company_research.py`
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- Auto-triggered when a job moves to `phone_screen` in the Interviews kanban
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- Three-phase: (1) SearXNG company scrape → (1b) SearXNG news snippets → (2) LLM synthesis
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- SearXNG scraper: `/Library/Development/scrapers/companyScraper.py`
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- SearXNG Docker: run `docker compose up -d` from `/Library/Development/scrapers/SearXNG/` (port 8888)
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- `beautifulsoup4` and `fake-useragent` are installed in job-seeker env (required for scraper)
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- News search hits `/search?format=json` — JSON format must be enabled in `searxng-config/settings.yml`
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- ⚠️ `settings.yml` owned by UID 977 (container user) — use `docker cp` to update, not direct writes
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- ⚠️ `settings.yml` requires `use_default_settings: true` at the top or SearXNG fails schema validation
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- `companyScraper` calls `sys.exit()` on missing deps — use `except BaseException` not `except Exception`
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## Email Classifier Labels
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Six labels: `interview_request`, `rejection`, `offer`, `follow_up`, `survey_received`, `other`
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- `survey_received` — links or requests to complete a culture-fit survey/assessment
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## Services (managed via Settings → Services tab)
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| Service | Port | Notes |
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|---------|------|-------|
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| Streamlit UI | 8501 | `bash scripts/manage-ui.sh start` |
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| Ollama | 11434 | `sudo systemctl start ollama` |
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| Claude Code Wrapper | 3009 | `manage-services.sh start` in Post Fight Processing |
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| GitHub Copilot Wrapper | 3010 | `manage-copilot.sh start` in Post Fight Processing |
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| vLLM Server | 8000 | Manual start only |
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| SearXNG | 8888 | `docker compose up -d` in scrapers/SearXNG/ |
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| Vision Service | 8002 | `bash scripts/manage-vision.sh start` — moondream2 survey screenshot analysis |
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## Notion
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- DB: "Tracking Job Applications" (ID: `1bd75cff-7708-8007-8c00-f1de36620a0a`)
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- `config/notion.yaml` is gitignored (live token); `.example` is committed
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- Field names are non-obvious — always read from `field_map` in `config/notion.yaml`
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- "Salary" = Notion title property (unusual — it's the page title field)
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- "Job Source" = `multi_select` type
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- "Role Link" = URL field
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- "Status of Application" = status field; new listings use "Application Submitted"
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- Sync pushes `approved` + `applied` jobs; marks them `synced` after
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## Key Config Files
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- `config/notion.yaml` — gitignored, has token + field_map
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- `config/notion.yaml.example` — committed template
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- `config/search_profiles.yaml` — titles, locations, boards, custom_boards, exclude_keywords, mission_tags (per profile)
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- `config/llm.yaml` — LLM backend priority chain + enabled flags
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- `config/tokens.yaml` — gitignored, stores HF token (chmod 600)
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- `config/adzuna.yaml` — gitignored, Adzuna API app_id + app_key
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- `config/adzuna.yaml.example` — committed template
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## Custom Job Board Scrapers
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- `scripts/custom_boards/adzuna.py` — Adzuna Jobs API; credentials in `config/adzuna.yaml`
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- `scripts/custom_boards/theladders.py` — The Ladders SSR scraper; needs `curl_cffi` installed
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- Scrapers registered in `CUSTOM_SCRAPERS` dict in `discover.py`
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- Activated per-profile via `custom_boards: [adzuna, theladders]` in `search_profiles.yaml`
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- `enrich_all_descriptions()` in `enrich_descriptions.py` covers all sources (not just Glassdoor)
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- Home page "Fill Missing Descriptions" button dispatches `enrich_descriptions` task
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## Mission Alignment & Accessibility
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- Preferred industries: music, animal welfare, children's education (hardcoded in `generate_cover_letter.py`)
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- `detect_mission_alignment(company, description)` injects a Para 3 hint into cover letters for aligned companies
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- Company research includes an "Inclusion & Accessibility" section (8th section of the brief) in every brief
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- Accessibility search query in `_SEARCH_QUERIES` hits SearXNG for ADA/ERG/disability signals
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- `accessibility_brief` column in `company_research` table; shown in Interview Prep under ♿ section
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- This info is for personal decision-making ONLY — never disclosed in applications
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- In generalization: these become `profile.mission_industries` + `profile.accessibility_priority` in `user.yaml`
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## Document Rule
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Resumes and cover letters live in `/Library/Documents/JobSearch/` or Notion — never committed to this repo.
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## AIHawk (LinkedIn Easy Apply)
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- Cloned to `aihawk/` (gitignored)
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- Config: `aihawk/data_folder/plain_text_resume.yaml` — search FILL_IN for gaps
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- Self-ID: non-binary, pronouns any, no disability/drug-test disclosure
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- Run: `conda run -n job-seeker python aihawk/main.py`
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- Playwright: `conda run -n job-seeker python -m playwright install chromium`
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## Git Remote
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- Forgejo self-hosted at https://git.opensourcesolarpunk.com (username: pyr0ball)
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- `git remote add origin https://git.opensourcesolarpunk.com/pyr0ball/job-seeker.git`
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## Subagents
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Use `general-purpose` subagent type (not `Bash`) when tasks require file writes.
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@ -405,7 +405,7 @@ elif step == 4:
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if errs:
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st.error("\n".join(errs))
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else:
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resume_yaml_path = _ROOT / "aihawk" / "data_folder" / "plain_text_resume.yaml"
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resume_yaml_path = _ROOT / "config" / "plain_text_resume.yaml"
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resume_yaml_path.parent.mkdir(parents=True, exist_ok=True)
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resume_data = {**parsed, "experience": experience} if parsed else {"experience": experience}
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resume_yaml_path.write_text(
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@ -24,7 +24,7 @@ SEARCH_CFG = CONFIG_DIR / "search_profiles.yaml"
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BLOCKLIST_CFG = CONFIG_DIR / "blocklist.yaml"
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LLM_CFG = CONFIG_DIR / "llm.yaml"
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NOTION_CFG = CONFIG_DIR / "notion.yaml"
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RESUME_PATH = Path(__file__).parent.parent.parent / "aihawk" / "data_folder" / "plain_text_resume.yaml"
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RESUME_PATH = Path(__file__).parent.parent.parent / "config" / "plain_text_resume.yaml"
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KEYWORDS_CFG = CONFIG_DIR / "resume_keywords.yaml"
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def load_yaml(path: Path) -> dict:
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u_linkedin = c2.text_input("LinkedIn URL", _u.get("linkedin", ""))
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u_summary = st.text_area("Career Summary (used in LLM prompts)",
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_u.get("career_summary", ""), height=100)
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u_voice = st.text_area(
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"Voice & Personality (shapes cover letter tone)",
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_u.get("candidate_voice", ""),
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height=80,
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help="Personality traits and writing voice that the LLM uses to write authentically in your style. Never disclosed in applications.",
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)
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with st.expander("🎯 Mission & Values"):
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st.caption("Industry passions and causes you care about. Used to inject authentic Para 3 alignment when a company matches. Never disclosed in applications.")
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_mission = dict(_u.get("mission_preferences", {}))
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_mission_keys = ["animal_welfare", "education", "music", "social_impact"]
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_mission_labels = {
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"animal_welfare": "🐾 Animal Welfare",
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"education": "📚 Education / EdTech / Kids",
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"music": "🎵 Music Industry",
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"social_impact": "🌍 Social Impact / Nonprofits",
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}
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_mission_updated = {}
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for key in _mission_keys:
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_mission_updated[key] = st.text_area(
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_mission_labels[key],
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_mission.get(key, ""),
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height=68,
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key=f"mission_{key}",
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help=f"Your personal connection to this domain. Leave blank to use the default prompt hint.",
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)
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# Preserve any extra keys the user may have added manually in YAML
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for k, v in _mission.items():
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if k not in _mission_keys:
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_mission_updated[k] = v
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with st.expander("🔒 Sensitive Employers (NDA)"):
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st.caption("Companies listed here appear as 'previous employer (NDA)' in research briefs.")
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@ -180,10 +210,11 @@ with tab_profile:
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new_data = {
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"name": u_name, "email": u_email, "phone": u_phone,
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"linkedin": u_linkedin, "career_summary": u_summary,
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"candidate_voice": u_voice,
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"nda_companies": nda_list,
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"docs_dir": u_docs, "ollama_models_dir": u_ollama, "vllm_models_dir": u_vllm,
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"inference_profile": u_inf_profile,
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"mission_preferences": _u.get("mission_preferences", {}),
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"mission_preferences": {k: v for k, v in _mission_updated.items() if v.strip()},
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"candidate_accessibility_focus": u_access_focus,
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"candidate_lgbtq_focus": u_lgbtq_focus,
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"services": {
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@ -673,7 +704,7 @@ with tab_resume:
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)
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if not RESUME_PATH.exists():
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st.error(f"Resume YAML not found at `{RESUME_PATH}`. Is AIHawk cloned?")
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st.error(f"Resume YAML not found at `{RESUME_PATH}`. Copy or create `config/plain_text_resume.yaml`.")
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st.stop()
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_data = yaml.safe_load(RESUME_PATH.read_text()) or {}
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@ -28,7 +28,7 @@ from scripts.db import (
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from scripts.task_runner import submit_task
|
||||
|
||||
DOCS_DIR = _profile.docs_dir if _profile else Path.home() / "Documents" / "JobSearch"
|
||||
RESUME_YAML = Path(__file__).parent.parent.parent / "aihawk" / "data_folder" / "plain_text_resume.yaml"
|
||||
RESUME_YAML = Path(__file__).parent.parent.parent / "config" / "plain_text_resume.yaml"
|
||||
|
||||
st.title("🚀 Apply Workspace")
|
||||
|
||||
|
|
|
|||
|
|
@ -1,4 +1,15 @@
|
|||
profiles:
|
||||
- boards:
|
||||
- linkedin
|
||||
- indeed
|
||||
- glassdoor
|
||||
- zip_recruiter
|
||||
job_titles:
|
||||
- Customer Service Specialist
|
||||
locations:
|
||||
- San Francisco CA
|
||||
name: default
|
||||
remote_only: false
|
||||
- boards:
|
||||
- linkedin
|
||||
- indeed
|
||||
|
|
|
|||
|
|
@ -28,7 +28,7 @@ dependencies:
|
|||
- fake-useragent # company scraper rotation
|
||||
|
||||
# ── LLM / AI backends ─────────────────────────────────────────────────────
|
||||
- openai>=1.0 # used for OpenAI-compat backends (ollama, vllm, wrappers)
|
||||
- openai>=1.55.0,<2.0.0 # >=1.55 required for httpx 0.28 compat; <2.0 for langchain-openai
|
||||
- anthropic>=0.80 # direct Anthropic API fallback
|
||||
- ollama # Python client for Ollama management
|
||||
- langchain>=0.2
|
||||
|
|
@ -54,6 +54,9 @@ dependencies:
|
|||
- pyyaml>=6.0
|
||||
- python-dotenv
|
||||
|
||||
# ── Auth / licensing ──────────────────────────────────────────────────────
|
||||
- PyJWT>=2.8
|
||||
|
||||
# ── Utilities ─────────────────────────────────────────────────────────────
|
||||
- sqlalchemy
|
||||
- tqdm
|
||||
|
|
|
|||
|
|
@ -22,7 +22,7 @@ curl_cffi
|
|||
fake-useragent
|
||||
|
||||
# ── LLM / AI backends ─────────────────────────────────────────────────────
|
||||
openai>=1.0
|
||||
openai>=1.55.0,<2.0.0 # >=1.55 required for httpx 0.28 compat; <2.0 for langchain-openai
|
||||
anthropic>=0.80
|
||||
ollama
|
||||
langchain>=0.2
|
||||
|
|
@ -51,6 +51,9 @@ json-repair
|
|||
pyyaml>=6.0
|
||||
python-dotenv
|
||||
|
||||
# ── Auth / licensing ──────────────────────────────────────────────────────
|
||||
PyJWT>=2.8
|
||||
|
||||
# ── Utilities ─────────────────────────────────────────────────────────────
|
||||
sqlalchemy
|
||||
tqdm
|
||||
|
|
|
|||
|
|
@ -193,7 +193,7 @@ def _parse_sections(text: str) -> dict[str, str]:
|
|||
return sections
|
||||
|
||||
|
||||
_RESUME_YAML = Path(__file__).parent.parent / "aihawk" / "data_folder" / "plain_text_resume.yaml"
|
||||
_RESUME_YAML = Path(__file__).parent.parent / "config" / "plain_text_resume.yaml"
|
||||
_KEYWORDS_YAML = Path(__file__).parent.parent / "config" / "resume_keywords.yaml"
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -26,11 +26,19 @@ LETTERS_DIR = _profile.docs_dir if _profile else Path.home() / "Documents" / "Jo
|
|||
LETTER_GLOB = "*Cover Letter*.md"
|
||||
|
||||
# Background injected into every prompt so the model has the candidate's facts
|
||||
SYSTEM_CONTEXT = (
|
||||
f"You are writing cover letters for {_profile.name}. {_profile.career_summary}"
|
||||
if _profile else
|
||||
"You are a professional cover letter writer. Write in first person."
|
||||
)
|
||||
def _build_system_context() -> str:
|
||||
if not _profile:
|
||||
return "You are a professional cover letter writer. Write in first person."
|
||||
parts = [f"You are writing cover letters for {_profile.name}. {_profile.career_summary}"]
|
||||
if _profile.candidate_voice:
|
||||
parts.append(
|
||||
f"Voice and personality: {_profile.candidate_voice} "
|
||||
"Write in a way that reflects these authentic traits — not as a checklist, "
|
||||
"but as a natural expression of who this person is."
|
||||
)
|
||||
return " ".join(parts)
|
||||
|
||||
SYSTEM_CONTEXT = _build_system_context()
|
||||
|
||||
|
||||
# ── Mission-alignment detection ───────────────────────────────────────────────
|
||||
|
|
@ -58,6 +66,13 @@ _MISSION_SIGNALS: dict[str, list[str]] = {
|
|||
"instructure", "canvas lms", "clever", "district", "teacher",
|
||||
"k-12", "k12", "grade", "pedagogy",
|
||||
],
|
||||
"social_impact": [
|
||||
"nonprofit", "non-profit", "501(c)", "social impact", "mission-driven",
|
||||
"public benefit", "community", "underserved", "equity", "justice",
|
||||
"humanitarian", "advocacy", "charity", "foundation", "ngo",
|
||||
"social good", "civic", "public health", "mental health", "food security",
|
||||
"housing", "homelessness", "poverty", "workforce development",
|
||||
],
|
||||
}
|
||||
|
||||
_candidate = _profile.name if _profile else "the candidate"
|
||||
|
|
@ -79,6 +94,11 @@ _MISSION_DEFAULTS: dict[str, str] = {
|
|||
f"{_candidate}'s values. Para 3 should reflect this authentic connection specifically "
|
||||
"and warmly."
|
||||
),
|
||||
"social_impact": (
|
||||
f"This organization is mission-driven / social impact focused — exactly the kind of "
|
||||
f"cause {_candidate} cares deeply about. Para 3 should warmly reflect their genuine "
|
||||
"desire to apply their skills to work that makes a real difference in people's lives."
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -84,9 +84,9 @@ def _extract_career_summary(source: Path) -> str:
|
|||
|
||||
def _extract_personal_info(source: Path) -> dict:
|
||||
"""Extract personal info from aihawk resume yaml."""
|
||||
resume = source / "aihawk" / "data_folder" / "plain_text_resume.yaml"
|
||||
if not resume.exists():
|
||||
resume = source / "config" / "plain_text_resume.yaml"
|
||||
if not resume.exists():
|
||||
resume = source / "aihawk" / "data_folder" / "plain_text_resume.yaml"
|
||||
if not resume.exists():
|
||||
return {}
|
||||
data = _load_yaml(resume)
|
||||
|
|
@ -197,8 +197,10 @@ def _copy_configs(source: Path, dest: Path, apply: bool) -> None:
|
|||
|
||||
def _copy_aihawk_resume(source: Path, dest: Path, apply: bool) -> None:
|
||||
print("\n── Copying AIHawk resume profile")
|
||||
src = source / "config" / "plain_text_resume.yaml"
|
||||
if not src.exists():
|
||||
src = source / "aihawk" / "data_folder" / "plain_text_resume.yaml"
|
||||
dst = dest / "aihawk" / "data_folder" / "plain_text_resume.yaml"
|
||||
dst = dest / "config" / "plain_text_resume.yaml"
|
||||
_copy_file(src, dst, apply)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -92,6 +92,18 @@ def _find_column_split(page) -> float | None:
|
|||
return split_x if split_x and best_gap > page.width * 0.03 else None
|
||||
|
||||
|
||||
_CID_BULLETS = {127, 149, 183} # common bullet CIDs across ATS-reembedded fonts
|
||||
|
||||
def _clean_cid(text: str) -> str:
|
||||
"""Replace (cid:NNN) glyph references emitted by pdfplumber when a PDF font
|
||||
lacks a ToUnicode map. Known bullet CIDs become '•'; everything else is
|
||||
stripped so downstream section parsing sees clean text."""
|
||||
def _replace(m: re.Match) -> str:
|
||||
n = int(m.group(1))
|
||||
return "•" if n in _CID_BULLETS else ""
|
||||
return re.sub(r"\(cid:(\d+)\)", _replace, text)
|
||||
|
||||
|
||||
def extract_text_from_pdf(file_bytes: bytes) -> str:
|
||||
"""Extract text from PDF, handling two-column layouts via gutter detection.
|
||||
|
||||
|
|
@ -116,12 +128,12 @@ def extract_text_from_pdf(file_bytes: bytes) -> str:
|
|||
pages.append("\n".join(filter(None, [header_text, left_text, right_text])))
|
||||
continue
|
||||
pages.append(page.extract_text() or "")
|
||||
return "\n".join(pages)
|
||||
return _clean_cid("\n".join(pages))
|
||||
|
||||
|
||||
def extract_text_from_docx(file_bytes: bytes) -> str:
|
||||
doc = Document(io.BytesIO(file_bytes))
|
||||
return "\n".join(p.text for p in doc.paragraphs if p.text.strip())
|
||||
return _clean_cid("\n".join(p.text for p in doc.paragraphs if p.text.strip()))
|
||||
|
||||
|
||||
def extract_text_from_odt(file_bytes: bytes) -> str:
|
||||
|
|
@ -139,7 +151,7 @@ def extract_text_from_odt(file_bytes: bytes) -> str:
|
|||
text = "".join(elem.itertext()).strip()
|
||||
if text:
|
||||
lines.append(text)
|
||||
return "\n".join(lines)
|
||||
return _clean_cid("\n".join(lines))
|
||||
|
||||
|
||||
# ── Section splitter ──────────────────────────────────────────────────────────
|
||||
|
|
|
|||
|
|
@ -15,6 +15,7 @@ _DEFAULTS = {
|
|||
"phone": "",
|
||||
"linkedin": "",
|
||||
"career_summary": "",
|
||||
"candidate_voice": "",
|
||||
"nda_companies": [],
|
||||
"docs_dir": "~/Documents/JobSearch",
|
||||
"ollama_models_dir": "~/models/ollama",
|
||||
|
|
@ -61,6 +62,7 @@ class UserProfile:
|
|||
self.phone: str = data["phone"]
|
||||
self.linkedin: str = data["linkedin"]
|
||||
self.career_summary: str = data["career_summary"]
|
||||
self.candidate_voice: str = data.get("candidate_voice", "")
|
||||
self.nda_companies: list[str] = [c.lower() for c in data["nda_companies"]]
|
||||
self.docs_dir: Path = Path(data["docs_dir"]).expanduser().resolve()
|
||||
self.ollama_models_dir: Path = Path(data["ollama_models_dir"]).expanduser().resolve()
|
||||
|
|
|
|||
Loading…
Reference in a new issue