wehub-resource-sync
103 lines
3.4 KiB
Python
103 lines
3.4 KiB
Python
"""Flow moves (seed-master, tailor) + the pure scorer-runner.
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The scorer-runner wraps the deterministic scorers already proven in
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``tests/evals/scorers.py`` so the harness and the eval suite agree on what
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"a good tailoring" means. (The HTTP moves are appended to this module in a
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later task.)
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"""
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from __future__ import annotations
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from pathlib import Path
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from typing import Any
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import httpx
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from e2e_monitor import API_BASE
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from tests.evals.scorers import (
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is_valid_resume,
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jd_keywords_present,
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no_fabricated_employers,
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personal_info_unchanged,
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sections_preserved,
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)
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def score_tailoring(
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original: dict[str, Any], tailored: dict[str, Any], keywords: list[str]
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) -> dict[str, Any]:
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"""Run every structural scorer over an (original, tailored) pair."""
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return {
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"sections_preserved": sections_preserved(original, tailored),
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"fabricated_employers": no_fabricated_employers(original, tailored),
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"personal_info_unchanged": personal_info_unchanged(original, tailored),
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"is_valid_resume": is_valid_resume(tailored),
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"jd_keyword_coverage": jd_keywords_present(tailored, keywords),
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}
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def seed_master_db(data_dir: Path, master: dict[str, Any]) -> str:
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"""Pre-seed the isolated DB with a known master BEFORE the server boots.
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The upload endpoint only accepts documents (and runs a non-deterministic LLM
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parse), so for a controlled, deterministic master we write it straight into
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the isolated TinyDB file via app.database.Database — the same file the server
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opens once booted with DATA_DIR=<data_dir>. Returns the master's resume_id.
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"""
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from app.database import Database
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db = Database(db_path=data_dir / "database.json")
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try:
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doc = db.create_resume(
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content="(seeded master resume)",
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content_type="md",
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is_master=True,
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processed_data=master,
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processing_status="ready",
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)
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return doc["resume_id"]
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finally:
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db.close()
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def tailor(
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resume_id: str, jd_text: str, keywords: list[str], original: dict[str, Any]
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) -> dict[str, Any]:
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"""jobs/upload -> improve/preview -> improve/confirm; returns tailored + scores."""
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jobs_resp = httpx.post(
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f"{API_BASE}/jobs/upload",
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json={"job_descriptions": [jd_text], "resume_id": resume_id},
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timeout=120,
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)
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jobs_resp.raise_for_status()
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job_ids = jobs_resp.json().get("job_id", [])
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if not job_ids:
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raise RuntimeError("jobs/upload returned no job_id")
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job_id = job_ids[0]
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preview_resp = httpx.post(
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f"{API_BASE}/resumes/improve/preview",
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json={"resume_id": resume_id, "job_id": job_id},
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timeout=240,
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)
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preview_resp.raise_for_status()
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data = preview_resp.json()["data"]
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tailored = data["resume_preview"]
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improvements = data["improvements"]
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confirm_resp = httpx.post(
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f"{API_BASE}/resumes/improve/confirm",
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json={"resume_id": resume_id, "job_id": job_id,
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"improved_data": tailored, "improvements": improvements},
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timeout=240,
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)
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confirm_resp.raise_for_status()
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confirm = confirm_resp.json()
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return {
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"job_id": job_id,
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"tailored": tailored,
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"tailored_resume_id": confirm["data"].get("resume_id"),
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"keywords": keywords,
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"scores": score_tailoring(original, tailored, keywords),
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}
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