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brief_generator.py — Generates a structured candidate preparation brief.
"""
from datetime import datetime, timezone
from . import seed
def generate_brief(
candidate, client, score
) -> dict:
"""
Build a candidate interview preparation brief.
Returns a dictionary containing all brief content, suitable for
JSON serialization and frontend rendering.
"""
archetype = client.archetype.value if hasattr(client.archetype, "value") else client.archetype
competencies = [
("Communication", score.communication, client.min_communication),
("Adaptability", score.adaptability, client.min_adaptability),
("Collaboration", score.collaboration, client.min_collaboration),
("Problem Solving", score.problem_solving, client.min_problem_solving),
("Leadership", score.leadership, client.min_leadership),
]
competency_scores = []
for name, scored, min_req in competencies:
competency_scores.append({
"name": name,
"score": scored,
"min_required": min_req,
"status": "pass" if scored >= min_req else "warning",
})
# Gather coaching tips for all competencies
archetype_tips = seed.COACHING_TIPS.get(archetype, {})
coaching_tips: list[str] = []
for key in ["communication", "adaptability", "collaboration", "problem_solving", "leadership"]:
tips = archetype_tips.get(key, [])
if tips:
coaching_tips.append(tips[0]) # Take the top tip from each competency
# If we have fewer than 3, pad; if more, take top 3
coaching_tips = coaching_tips[:3] if len(coaching_tips) >= 3 else coaching_tips
# Get the archetype-specific tips more strategically:
# Focus on competencies where the candidate scored lowest
sorted_competencies = sorted(competency_scores, key=lambda c: c["score"])
focused_tips: list[str] = []
for comp in sorted_competencies:
comp_key = comp["name"].lower().replace(" ", "_")
tips = archetype_tips.get(comp_key, [])
for tip in tips:
if tip not in focused_tips:
focused_tips.append(tip)
if len(focused_tips) >= 3:
break
if len(focused_tips) >= 3:
break
practice_questions = seed.PRACTICE_QUESTIONS.get(archetype, [])
return {
"candidate_name": candidate.name,
"client_name": client.name,
"client_archetype": archetype,
"client_expectations": client.expectations or "",
"competency_scores": competency_scores,
"coaching_tips": focused_tips,
"practice_questions": practice_questions,
"overall_match_percentage": round(score.overall_match * 100, 1),
"generated_at": datetime.now(timezone.utc).isoformat(),
}
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