584 lines
19 KiB
Python
584 lines
19 KiB
Python
from collections import defaultdict
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from datetime import datetime, timedelta
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import hashlib
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import json
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from dateutil.relativedelta import relativedelta
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from django.core.cache import cache
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from django.db.models import Min, Count
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from django.db.models.functions import TruncDate
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from django.http import StreamingHttpResponse
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from django.utils import timezone
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from openai import OpenAI
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from utils.api import APIView
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from utils.shortcuts import get_env
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from account.models import User
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from problem.models import Problem
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from submission.models import Submission, JudgeStatus
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from account.decorators import login_required
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from ai.models import AIAnalysis
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CACHE_TIMEOUT = 300
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DIFFICULTY_MAP = {"Low": "简单", "Mid": "中等", "High": "困难"}
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DEFAULT_CLASS_SIZE = 45
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# 评级阈值配置：(百分位上限, 评级)
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GRADE_THRESHOLDS = [
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(10, "S"), # 前10%: S级 - 卓越
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(35, "A"), # 前35%: A级 - 优秀
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(75, "B"), # 前75%: B级 - 良好
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(100, "C"), # 其余: C级 - 及格
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]
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# 小规模参与惩罚配置：(最小人数, 等级降级映射)
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SMALL_SCALE_PENALTY = {
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"threshold": 10,
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"downgrade": {"S": "A", "A": "B"},
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}
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def get_cache_key(prefix, *args):
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return hashlib.md5(f"{prefix}:{'_'.join(map(str, args))}".encode()).hexdigest()
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def get_difficulty(difficulty):
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return DIFFICULTY_MAP.get(difficulty, "中等")
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def get_grade(rank, submission_count):
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"""
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计算题目完成评级
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评级标准：
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- S级：前10%，卓越水平（10%的人）
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- A级：前35%，优秀水平（25%的人）
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- B级：前75%，良好水平（40%的人）
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- C级：75%之后，及格水平（25%的人）
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特殊规则：
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- 参与人数少于10人时，S级降为A级，A级降为B级（避免因人少而评级虚高）
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Args:
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rank: 用户排名（1表示第一名）
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submission_count: 总AC人数
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Returns:
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评级字符串 ("S", "A", "B", "C")
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"""
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# 边界检查
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if not rank or rank <= 0 or submission_count <= 0:
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return "C"
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# 计算百分位（0-100），使用 (rank-1) 使第一名的百分位为0
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percentile = (rank - 1) / submission_count * 100
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# 根据百分位确定基础评级
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base_grade = "C"
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for threshold, grade in GRADE_THRESHOLDS:
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if percentile < threshold:
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base_grade = grade
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break
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# 小规模参与惩罚：人数太少时降低评级
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if submission_count < SMALL_SCALE_PENALTY["threshold"]:
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base_grade = SMALL_SCALE_PENALTY["downgrade"].get(base_grade, base_grade)
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return base_grade
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def get_class_user_ids(user):
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if not user.class_name:
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return []
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cache_key = get_cache_key("class_users", user.class_name)
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user_ids = cache.get(cache_key)
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if user_ids is None:
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user_ids = list(
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User.objects.filter(class_name=user.class_name).values_list("id", flat=True)
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)
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cache.set(cache_key, user_ids, CACHE_TIMEOUT)
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return user_ids
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def get_user_first_ac_submissions(
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user_id, start, end, class_user_ids=None, use_class_scope=False
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):
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base_qs = Submission.objects.filter(
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result=JudgeStatus.ACCEPTED, create_time__gte=start, create_time__lte=end
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)
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if use_class_scope and class_user_ids:
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base_qs = base_qs.filter(user_id__in=class_user_ids)
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user_first_ac = list(
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base_qs.filter(user_id=user_id)
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.values("problem_id")
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.annotate(first_ac_time=Min("create_time"))
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)
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if not user_first_ac:
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return [], {}, []
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problem_ids = [item["problem_id"] for item in user_first_ac]
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ranked_first_ac = list(
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base_qs.filter(problem_id__in=problem_ids)
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.values("user_id", "problem_id")
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.annotate(first_ac_time=Min("create_time"))
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)
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by_problem = defaultdict(list)
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for item in ranked_first_ac:
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by_problem[item["problem_id"]].append(item)
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for submissions in by_problem.values():
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submissions.sort(key=lambda x: (x["first_ac_time"], x["user_id"]))
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return user_first_ac, by_problem, problem_ids
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class AIDetailDataAPI(APIView):
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@login_required
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def get(self, request):
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start = request.GET.get("start")
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end = request.GET.get("end")
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user = request.user
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cache_key = get_cache_key(
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"ai_detail", user.id, user.class_name or "", start, end
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)
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cached_result = cache.get(cache_key)
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if cached_result:
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return self.success(cached_result)
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class_user_ids = get_class_user_ids(user)
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use_class_scope = bool(user.class_name) and len(class_user_ids) > 1
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user_first_ac, by_problem, problem_ids = get_user_first_ac_submissions(
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user.id, start, end, class_user_ids, use_class_scope
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)
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result = {
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"user": user.username,
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"class_name": user.class_name,
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"start": start,
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"end": end,
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"solved": [],
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"grade": "",
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"tags": {},
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"difficulty": {},
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"contest_count": 0,
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}
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if user_first_ac:
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problems = {
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p.id: p
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for p in Problem.objects.filter(id__in=problem_ids)
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.select_related("contest")
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.prefetch_related("tags")
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}
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solved, contest_ids = self._build_solved_records(
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user_first_ac, by_problem, problems, user.id
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)
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result.update(
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{
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"solved": solved,
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"grade": self._calculate_average_grade(solved),
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"tags": self._calculate_top_tags(problems.values()),
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"difficulty": self._calculate_difficulty_distribution(
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problems.values()
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),
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"contest_count": len(set(contest_ids)),
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}
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)
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cache.set(cache_key, result, CACHE_TIMEOUT)
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return self.success(result)
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def _build_solved_records(self, user_first_ac, by_problem, problems, user_id):
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solved, contest_ids = [], []
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for item in user_first_ac:
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pid = item["problem_id"]
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problem = problems.get(pid)
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if not problem:
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continue
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ranking_list = by_problem.get(pid, [])
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rank = next(
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(
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idx + 1
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for idx, rec in enumerate(ranking_list)
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if rec["user_id"] == user_id
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),
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None,
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)
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if problem.contest_id:
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contest_ids.append(problem.contest_id)
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solved.append(
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{
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"problem": {
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"display_id": problem._id,
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"title": problem.title,
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"contest_id": problem.contest_id,
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"contest_title": getattr(problem.contest, "title", ""),
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},
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"ac_time": timezone.localtime(item["first_ac_time"]).isoformat(),
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"rank": rank,
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"ac_count": len(ranking_list),
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"grade": get_grade(rank, len(ranking_list)),
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"difficulty": get_difficulty(problem.difficulty),
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}
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)
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return sorted(solved, key=lambda x: x["ac_time"]), contest_ids
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def _calculate_average_grade(self, solved):
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"""
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计算平均等级，使用加权平均方法
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等级权重：S=4, A=3, B=2, C=1
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计算加权平均后，根据阈值确定最终等级
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Args:
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solved: 已解决的题目列表，每个包含grade字段
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Returns:
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平均等级字符串 ("S", "A", "B", "C")
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"""
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if not solved:
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return ""
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# 等级权重映射
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grade_weights = {"S": 4, "A": 3, "B": 2, "C": 1}
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# 计算加权总分
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total_weight = 0
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total_score = 0
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for s in solved:
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grade = s["grade"]
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if grade in grade_weights:
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total_score += grade_weights[grade]
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total_weight += 1
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if total_weight == 0:
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return ""
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# 计算平均权重
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average_weight = total_score / total_weight
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# 根据平均权重确定等级
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# S级: 3.5-4.0, A级: 2.5-3.5, B级: 1.5-2.5, C级: 1.0-1.5
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if average_weight >= 3.5:
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return "S"
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elif average_weight >= 2.5:
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return "A"
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elif average_weight >= 1.5:
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return "B"
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else:
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return "C"
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def _calculate_top_tags(self, problems):
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tags_counter = defaultdict(int)
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for problem in problems:
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for tag in problem.tags.all():
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if tag.name:
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tags_counter[tag.name] += 1
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return dict(sorted(tags_counter.items(), key=lambda x: x[1], reverse=True)[:5])
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def _calculate_difficulty_distribution(self, problems):
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diff_counter = {"Low": 0, "Mid": 0, "High": 0}
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for problem in problems:
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diff_counter[
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problem.difficulty if problem.difficulty in diff_counter else "Mid"
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] += 1
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return {
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get_difficulty(k): v
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for k, v in sorted(diff_counter.items(), key=lambda x: x[1], reverse=True)
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}
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class AIDurationDataAPI(APIView):
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@login_required
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def get(self, request):
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end_iso = request.GET.get("end")
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duration = request.GET.get("duration")
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user = request.user
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||
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cache_key = get_cache_key(
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"ai_duration", user.id, user.class_name or "", end_iso, duration
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)
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cached_result = cache.get(cache_key)
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||
if cached_result:
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return self.success(cached_result)
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class_user_ids = get_class_user_ids(user)
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use_class_scope = bool(user.class_name) and len(class_user_ids) > 1
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time_config = self._parse_duration(duration)
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start = datetime.fromisoformat(end_iso) - time_config["total_delta"]
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duration_data = []
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for i in range(time_config["show_count"]):
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start = start + time_config["delta"]
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period_end = start + time_config["delta"]
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submission_count = Submission.objects.filter(
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user_id=user.id, create_time__gte=start, create_time__lte=period_end
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||
).count()
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||
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period_data = {
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||
"unit": time_config["show_unit"],
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||
"index": time_config["show_count"] - 1 - i,
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"start": start.isoformat(),
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||
"end": period_end.isoformat(),
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||
"problem_count": 0,
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||
"submission_count": submission_count,
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||
"grade": "",
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||
}
|
||
|
||
if submission_count > 0:
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||
user_first_ac, by_problem, problem_ids = get_user_first_ac_submissions(
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||
user.id,
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start.isoformat(),
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||
period_end.isoformat(),
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class_user_ids,
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use_class_scope,
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)
|
||
if user_first_ac:
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period_data["problem_count"] = len(problem_ids)
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period_data["grade"] = self._calculate_period_grade(
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user_first_ac, by_problem, user.id
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)
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duration_data.append(period_data)
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cache.set(cache_key, duration_data, CACHE_TIMEOUT)
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return self.success(duration_data)
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||
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||
def _parse_duration(self, duration):
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||
unit, count = duration.split(":")
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||
count = int(count)
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configs = {
|
||
("months", 2): {
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||
"show_count": 8,
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||
"show_unit": "weeks",
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||
"total_delta": timedelta(weeks=9),
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||
"delta": timedelta(weeks=1),
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||
},
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||
("months", 6): {
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||
"show_count": 6,
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||
"show_unit": "months",
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||
"total_delta": relativedelta(months=7),
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||
"delta": relativedelta(months=1),
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||
},
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||
("years", 1): {
|
||
"show_count": 12,
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||
"show_unit": "months",
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"total_delta": relativedelta(months=13),
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||
"delta": relativedelta(months=1),
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||
},
|
||
}
|
||
|
||
return configs.get(
|
||
(unit, count),
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{
|
||
"show_count": 4,
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||
"show_unit": "weeks",
|
||
"total_delta": timedelta(weeks=5),
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||
"delta": timedelta(weeks=1),
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},
|
||
)
|
||
|
||
def _calculate_period_grade(self, user_first_ac, by_problem, user_id):
|
||
"""
|
||
计算时间段内的平均等级，使用加权平均方法
|
||
|
||
等级权重：S=4, A=3, B=2, C=1
|
||
计算加权平均后，根据阈值确定最终等级
|
||
|
||
Args:
|
||
user_first_ac: 用户首次AC的提交记录
|
||
by_problem: 按题目分组的排名数据
|
||
user_id: 用户ID
|
||
|
||
Returns:
|
||
平均等级字符串 ("S", "A", "B", "C")
|
||
"""
|
||
if not user_first_ac:
|
||
return ""
|
||
|
||
# 等级权重映射
|
||
grade_weights = {"S": 4, "A": 3, "B": 2, "C": 1}
|
||
|
||
# 计算加权总分
|
||
total_weight = 0
|
||
total_score = 0
|
||
|
||
for item in user_first_ac:
|
||
ranking_list = by_problem.get(item["problem_id"], [])
|
||
rank = next(
|
||
(
|
||
idx + 1
|
||
for idx, rec in enumerate(ranking_list)
|
||
if rec["user_id"] == user_id
|
||
),
|
||
None,
|
||
)
|
||
if rank:
|
||
grade = get_grade(rank, len(ranking_list))
|
||
if grade in grade_weights:
|
||
total_score += grade_weights[grade]
|
||
total_weight += 1
|
||
|
||
if total_weight == 0:
|
||
return ""
|
||
|
||
# 计算平均权重
|
||
average_weight = total_score / total_weight
|
||
|
||
# 根据平均权重确定等级
|
||
# S级: 3.5-4.0, A级: 2.5-3.5, B级: 1.5-2.5, C级: 1.0-1.5
|
||
if average_weight >= 3.5:
|
||
return "S"
|
||
elif average_weight >= 2.5:
|
||
return "A"
|
||
elif average_weight >= 1.5:
|
||
return "B"
|
||
else:
|
||
return "C"
|
||
|
||
|
||
class AIAnalysisAPI(APIView):
|
||
@login_required
|
||
def post(self, request):
|
||
details = request.data.get("details")
|
||
duration = request.data.get("duration")
|
||
|
||
api_key = get_env("AI_KEY")
|
||
|
||
if not api_key:
|
||
return self.error("API_KEY is not set")
|
||
|
||
client = OpenAI(api_key=api_key, base_url="https://api.deepseek.com")
|
||
|
||
system_prompt = "你是一个风趣的编程老师，学生使用判题狗平台进行编程练习。请根据学生提供的详细数据和每周数据，给出用户的学习建议，最后写一句鼓励学生的话。请使用 markdown 格式输出，不要在代码块中输出。"
|
||
user_prompt = f"这段时间内的详细数据: {details}\n每周或每月的数据: {duration}"
|
||
|
||
analysis_chunks = []
|
||
saved_instance = None
|
||
completed = False
|
||
|
||
def save_analysis():
|
||
nonlocal saved_instance
|
||
if analysis_chunks and not saved_instance:
|
||
saved_instance = AIAnalysis.objects.create(
|
||
user=request.user,
|
||
provider="deepseek",
|
||
model="deepseek-chat",
|
||
data={"details": details, "duration": duration},
|
||
system_prompt=system_prompt,
|
||
user_prompt="这段时间内的详细数据，每周或每月的数据。",
|
||
analysis="".join(analysis_chunks).strip(),
|
||
)
|
||
|
||
def stream_generator():
|
||
nonlocal completed
|
||
try:
|
||
stream = client.chat.completions.create(
|
||
model="deepseek-chat",
|
||
messages=[
|
||
{"role": "system", "content": system_prompt},
|
||
{"role": "user", "content": user_prompt},
|
||
],
|
||
stream=True,
|
||
)
|
||
except Exception as exc:
|
||
yield f"data: {json.dumps({'type': 'error', 'message': str(exc)})}\n\n"
|
||
yield "event: end\n\n"
|
||
return
|
||
|
||
yield "event: start\n\n"
|
||
|
||
try:
|
||
for chunk in stream:
|
||
if not chunk.choices:
|
||
continue
|
||
|
||
choice = chunk.choices[0]
|
||
if choice.finish_reason:
|
||
completed = True
|
||
save_analysis()
|
||
yield f"data: {json.dumps({'type': 'done'})}\n\n"
|
||
break
|
||
|
||
content = choice.delta.content
|
||
if content:
|
||
analysis_chunks.append(content)
|
||
yield f"data: {json.dumps({'type': 'delta', 'content': content})}\n\n"
|
||
|
||
except Exception as exc:
|
||
yield f"data: {json.dumps({'type': 'error', 'message': str(exc)})}\n\n"
|
||
finally:
|
||
save_analysis()
|
||
if saved_instance and not completed:
|
||
try:
|
||
saved_instance.delete()
|
||
except Exception:
|
||
pass
|
||
yield "event: end\n\n"
|
||
|
||
response = StreamingHttpResponse(
|
||
streaming_content=stream_generator(),
|
||
content_type="text/event-stream",
|
||
)
|
||
response["Cache-Control"] = "no-cache"
|
||
return response
|
||
|
||
|
||
class AIHeatmapDataAPI(APIView):
|
||
@login_required
|
||
def get(self, request):
|
||
user = request.user
|
||
cache_key = get_cache_key("ai_heatmap", user.id, user.class_name or "")
|
||
cached_result = cache.get(cache_key)
|
||
if cached_result:
|
||
return self.success(cached_result)
|
||
|
||
end = datetime.now()
|
||
start = end - timedelta(days=365)
|
||
|
||
# 使用单次查询获取所有数据，按日期分组统计
|
||
submission_counts = (
|
||
Submission.objects.filter(
|
||
user_id=user.id, create_time__gte=start, create_time__lte=end
|
||
)
|
||
.annotate(date=TruncDate("create_time"))
|
||
.values("date")
|
||
.annotate(count=Count("id"))
|
||
.order_by("date")
|
||
)
|
||
|
||
# 将查询结果转换为字典，便于快速查找
|
||
submission_dict = {item["date"]: item["count"] for item in submission_counts}
|
||
|
||
# 生成365天的热力图数据
|
||
heatmap_data = []
|
||
current_date = start.date()
|
||
for i in range(365):
|
||
day_date = current_date + timedelta(days=i)
|
||
submission_count = submission_dict.get(day_date, 0)
|
||
heatmap_data.append(
|
||
{
|
||
"timestamp": int(datetime.combine(
|
||
day_date, datetime.min.time()
|
||
).timestamp() * 1000),
|
||
"value": submission_count,
|
||
}
|
||
)
|
||
|
||
cache.set(cache_key, heatmap_data, CACHE_TIMEOUT)
|
||
return self.success(heatmap_data)
|