Spaces:
Running
Running
| """ | |
| Exports precomputed evaluation trajectories, case dossiers, and benchmarks | |
| into ui/static_data.js for standalone static deployment (e.g. Hugging Face Spaces). | |
| """ | |
| import os | |
| import sys | |
| import json | |
| root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) | |
| if root_dir not in sys.path: | |
| sys.path.insert(0, root_dir) | |
| from agents.evidence_loader import EvidenceLoader | |
| from baseline.rubric_scorer import RubricScorer | |
| from eval_cases.dataset import CASES, SHARED_JD | |
| CACHE_DIR = os.path.join(root_dir, "trajectories") | |
| CASES_DIR = os.path.join(root_dir, "eval_cases") | |
| EVAL_FILE = os.path.join(root_dir, "eval", "eval_results.json") | |
| def generate_static_data(): | |
| summary_list = [] | |
| full_docs_map = {} | |
| eval_map = {} | |
| for c in CASES: | |
| cid = c["candidate_id"] | |
| traj_file = os.path.join(CACHE_DIR, f"{cid}_trajectory.json") | |
| fit_score = 50.0 | |
| consistency_score = 50.0 | |
| quadrant = "EVALUATING" | |
| has_disc = False | |
| final_report = {} | |
| if os.path.exists(traj_file): | |
| try: | |
| with open(traj_file, "r", encoding="utf-8") as f: | |
| tdata = json.load(f) | |
| final_report = tdata.get("final_report", {}) | |
| for step in reversed(tdata.get("steps", [])): | |
| if step.get("step") == "recommendation_writing": | |
| out = step.get("output", {}) | |
| if not final_report: | |
| final_report = out | |
| fit_score = out.get("role_fit_score", 50.0) | |
| consistency_score = out.get("evidence_consistency_score", 50.0) | |
| quadrant = out.get("quadrant", "REVIEW REQUIRED") | |
| has_disc = len(out.get("key_discrepancies", [])) > 0 | |
| break | |
| except Exception as err: | |
| print(f"Warning loading trajectory for {cid}: {err}") | |
| summary_list.append({ | |
| "candidate_id": cid, | |
| "name": c["name"], | |
| "category": c.get("category", "applicant"), | |
| "target_role": c.get("target_role", "Senior Software Engineer"), | |
| "role_fit_score": fit_score, | |
| "evidence_consistency_score": consistency_score, | |
| "quadrant": quadrant, | |
| "has_discrepancies": has_disc | |
| }) | |
| dossier = EvidenceLoader.load_case_from_dict(c) | |
| full_docs_map[cid] = { | |
| "candidate_id": cid, | |
| "name": c["name"], | |
| "target_role": c.get("target_role", "Senior Software Engineer"), | |
| "category": c.get("category", "applicant"), | |
| "documents": { | |
| "cv": c.get("cv_text", ""), | |
| "interview": c.get("interview_notes", ""), | |
| "assessment": c.get("technical_assessment", ""), | |
| "project_rfc": c.get("project_rfc", ""), | |
| "jd": c.get("jd_text", SHARED_JD) | |
| }, | |
| "structured_profile": dossier.structured_cv_profile, | |
| "evidence_spans": [s.to_dict() for s in dossier.spans] | |
| } | |
| rubric = RubricScorer.evaluate_from_dict(dossier.structured_cv_profile) | |
| eval_map[cid] = { | |
| "report": final_report, | |
| "baseline_a": rubric.to_dict(), | |
| "cached": True | |
| } | |
| eval_summary = {} | |
| if os.path.exists(EVAL_FILE): | |
| with open(EVAL_FILE, "r", encoding="utf-8") as f: | |
| edata = json.load(f) | |
| eval_summary = { | |
| "metadata": edata.get("metadata", {}), | |
| "metrics": edata.get("metrics", {}) | |
| } | |
| audit_summary = {} | |
| try: | |
| from ui.server import get_audit_summary | |
| audit_summary = get_audit_summary() | |
| except Exception as exc: | |
| print(f"Warning computing audit summary for static export: {exc}") | |
| data = { | |
| "cases": summary_list, | |
| "fullDocs": full_docs_map, | |
| "evaluations": eval_map, | |
| "evalSummary": eval_summary, | |
| "auditSummary": audit_summary | |
| } | |
| # Write to static_data.js in ui/ | |
| ui_out = os.path.join(root_dir, "ui", "static_data.js") | |
| with open(ui_out, "w", encoding="utf-8") as f: | |
| f.write("window.HIRETRACE_STATIC = " + json.dumps(data, indent=2) + ";\n") | |
| print(f"SUCCESS: Generated {ui_out} ({os.path.getsize(ui_out) / 1024:.1f} KB)") | |
| if __name__ == "__main__": | |
| generate_static_data() | |