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File size: 4,408 Bytes
a921baa 1bbf278 a921baa 1bbf278 a921baa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | """
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()
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