Pramp AI模拟面试平台使用指南

import time class MockInterviewSession:    """Pramp 风格面试模拟会话管理器"""     INTERVIEW_TYPES = ["algorithm", "system_design", "behavioral", "frontend"]    KEY_POINTS = {        "system_design": ["scalability", "availability", "database",                          "cache", "load_balancer", "api_design"],        "algorithm": ["time_complexity", "space_complexity", "edge_case",                      "optimization", "data_structure"],    }     def __init__(self, session_type: str, difficulty: str = "medium"):        self.session_type = session_type        self.difficulty = difficulty        self.questions = []        self.answers = []        self.scores = {}        self.role = None        self.feedback_list = []        self.start_time = None     def start_session(self, role: str) -> dict:        """开始面试会话，设定角色（面试者/面试官）"""        self.role = role        self.start_time = time.time()        return {"status": "started", "role": role, "type": self.session_type}     def submit_answer(self, question_idx: int, answer: str) -> int:        """提交面试回答"""        self.answers.append({            "question_idx": question_idx,            "answer": answer,            "timestamp": time.time(),        })        return len(self.answers)     def evaluate_answer(self, answer: str) -> dict:        """评估单个回答的关键点覆盖度"""        answer_lower = answer.lower()        key_points = self.KEY_POINTS.get(self.session_type, [])        covered = [kp for kp in key_points if kp in answer_lower]        coverage = len(covered) / len(key_points) if key_points else 0        return {            "coverage": round(coverage * 100, 1),            "covered_points": covered,            "missing_points": [kp for kp in key_points if kp not in covered],        }     def generate_followup(self, answer: str) -> str:        """根据回答内容生成追问"""        if "cache" in answer.lower():            return "请详细说明缓存策略，如何处理穿透和雪崩？"        elif "database" in answer.lower():            return "数据库方案在读写比例变化时如何调整？"        elif "api" in answer.lower():            return "API设计如何处理版本控制和向后兼容？"        return "请进一步解释设计决策背后的权衡。"     def record_feedback(self, feedback: dict):        """记录搭档反馈"""        self.feedback_list.append(feedback)     def evaluate_session(self) -> dict:        """综合评估整场面试表现"""        total = len(self.answers)        if total == 0:            return {"score": 0, "feedback": "未提交任何答案"}        self.scores = {            "technical_depth": min(100, total * 15),            "communication": min(100, total * 12),            "time_management": min(100, total * 10),            "overall": min(100, total * 13),        }        return self.scores  class PracticeScheduler:    """面试练习计划调度器"""     PHASES = {        "foundation": {"ai_ratio": 0.8, "pair_ratio": 0.2, "daily": 1},        "enhancement": {"ai_ratio": 0.6, "pair_ratio": 0.4, "daily": 2},        "sprint": {"ai_ratio": 0.3, "pair_ratio": 0.7, "daily": 2},    }     def __init__(self, interview_type: str):        self.interview_type = interview_type     def generate_plan(self, weeks: int) -> list:        """根据可用周数生成阶段性练习计划"""        if weeks >= 4:            phases = ["foundation"] * (weeks - 2) + ["enhancement", "sprint"]        elif weeks >= 2:            phases = ["enhancement"] * (weeks - 1) + ["sprint"]        else:            phases = ["sprint"]        plan = []        for i, phase in enumerate(phases):            cfg = self.PHASES[phase]            plan.append({                "week": i + 1,                "phase": phase,                "ai_sessions": round(cfg["daily"] * cfg["ai_ratio"] * 7),                "pair_sessions": round(cfg["daily"] * cfg["pair_ratio"] * 7),            })        return plan  class FeedbackTracker:    """练习反馈追踪与进步分析"""     def __init__(self):        self.sessions = []     def record_session(self, data: dict):        """记录练习会话数据"""        self.sessions.append(data)     def analyze_progress(self) -> dict:        """分析进步趋势和薄弱环节"""        if not self.sessions:            return {"message": "暂无练习记录"}        type_scores = {}        for s in self.sessions:            t = s["type"]            type_scores.setdefault(t, []).append(s.get("score", 0))        trends = {}        for t, scores in type_scores.items():            trends[t] = {                "average": round(sum(scores) / len(scores), 1),                "first": scores[0],                "latest": scores[-1],                "improvement": round(scores[-1] - scores[0], 1),            }        weakest = min(trends.items(), key=lambda x: x[1]["average"])        return {            "trends": trends,            "weakest_area": weakest[0],            "total_sessions": len(self.sessions),        }  # 使用示例session = MockInterviewSession("system_design")session.start_session("interviewee")session.submit_answer(0, "我会使用 cache 和 load_balancer 来设计")eval_result = session.evaluate_answer("use cache and load_balancer")followup = session.generate_followup("use cache and database")scores = session.evaluate_session() scheduler = PracticeScheduler("algorithm")plan = scheduler.generate_plan(4) tracker = FeedbackTracker()tracker.record_session({"type": "algorithm", "score": 65})tracker.record_session({"type": "algorithm", "score": 78})progress = tracker.analyze_progress()print(f"进步分析: {progress}")