AI Interview Assistant

# interview_assistant.py — Vol. 23 capstone (educational) # MVP = practice interviewer. Not a hiring oracle. from fastapi import FastAPI, HTTPException from pydantic import BaseModel, Field app = FastAPI(title="Vol23 Interview Assistant") DISCLAIMER = "Practice feedback only. Not a hiring decision. Not an assessment of a protected class." BANNED = ("race", "ethnicity", "religion", "gender", "age", "disability", "pregnancy", "national origin") class RubricDim(BaseModel): id: str name: str description: str scale_min: int = 1 scale_max: int = 5 class StartIn(BaseModel): session_id: str mode: str = Field(pattern="^practice$") # stretch may add interviewer_aid later job_description: str = Field(min_length=20, max_length=8000) rubric: list[RubricDim] = Field(min_length=2, max_length=8) disclaimer_ack: bool class AnswerIn(BaseModel): session_id: str question: str answer: str = Field(min_length=1, max_length=8000) class DimScore(BaseModel): dim_id: str score: int evidence: str coaching: str class ScoreOut(BaseModel): disclaimer: str dimensions: list[DimScore] overall_note: str hiring_decision: None = None # always null in MVP SESSIONS: dict[str, dict] = {} def llm_score(jd: str, rubric: list[RubricDim], question: str, answer: str) -> list[dict]: # Replace with structured-output chat. Must quote evidence from the answer. return [ { "dim_id": d.id, "score": 3, "evidence": answer[:180], "coaching": "Replace with model coaching tied to the rubric descriptor.", } for d in rubric ] @app.post("/v1/interview/start") def start(body: StartIn): if not body.disclaimer_ack: raise HTTPException(403, "Disclaimer acknowledgment required.") SESSIONS[body.session_id] = {"jd": body.job_description, "rubric": body.rubric, "mode": body.mode} return {"session_id": body.session_id, "mode": "practice", "disclaimer": DISCLAIMER} @app.post("/v1/interview/score", response_model=ScoreOut) def score(body: AnswerIn): sess = SESSIONS.get(body.session_id) if not sess: raise HTTPException(404, "unknown session") blob = (body.question + " " + body.answer).lower() if any(term in blob for term in BANNED) and "job requirement" not in blob: # Heuristic demo only: do not score demographics; refuse. raise HTTPException(422, "Refusing demographic / protected-class inference. Re-answer the work sample.") raw = llm_score(sess["jd"], sess["rubric"], body.question, body.answer) dims = [] for d, row in zip(sess["rubric"], raw): sc = int(row["score"]) if not d.scale_min <= sc <= d.scale_max: raise HTTPException(422, "Score outside rubric scale.") dims.append(DimScore(dim_id=d.id, score=sc, evidence=str(row["evidence"]), coaching=str(row["coaching"]))) return ScoreOut( disclaimer=DISCLAIMER, dimensions=dims, overall_note="Use these notes to practice. A human hiring process is separate.", hiring_decision=None, )