556 lines
18 KiB
Python
556 lines
18 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
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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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from textwrap import dedent
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# 常量定义
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CACHE_TIMEOUT = 300 # 5分钟缓存
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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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def get_cache_key(prefix, *args):
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"""生成缓存键"""
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key_string = f"{prefix}:{'_'.join(map(str, args))}"
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return hashlib.md5(key_string.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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根据排名和提交人数计算等级
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只有三分之一的人完成，直接给到 S
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"""
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if not rank or rank <= 0 or submission_count <= 0:
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return "C"
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if submission_count < DEFAULT_CLASS_SIZE // 3:
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return "S"
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top_percent = round(rank / submission_count * 100)
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if top_percent < 20:
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return "S"
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elif top_percent < 50:
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return "A"
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elif top_percent < 85:
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return "B"
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else:
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return "C"
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def get_class_user_ids(user):
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"""获取班级用户ID列表"""
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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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"""获取用户首次AC提交记录"""
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base_qs = Submission.objects.filter(
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result=JudgeStatus.ACCEPTED,
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create_time__gte=start,
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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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# 获取用户首次AC
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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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# 获取相关题目的所有首次AC记录用于排名
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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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# 按题目分组并排序
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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 _, arr in by_problem.items():
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arr.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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username = request.GET.get("username")
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if username:
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try:
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user = User.objects.get(username=username)
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except User.DoesNotExist:
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return self.error("User does not exist")
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else:
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user = request.user
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# 检查缓存
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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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# 获取班级用户ID
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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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# 获取用户首次AC记录
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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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if not user_first_ac:
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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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cache.set(cache_key, result, CACHE_TIMEOUT)
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return self.success(result)
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# 优化的题目查询 - 一次性获取所有需要的数据
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problems = self._get_problems_with_data(problem_ids)
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# 构建解题记录
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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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# 计算统计数据
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avg_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(problems.values())
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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": solved,
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"grade": avg_grade,
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"tags": tags,
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"difficulty": difficulty,
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"contest_count": len(set(contest_ids)),
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}
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# 缓存结果
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cache.set(cache_key, result, CACHE_TIMEOUT)
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return self.success(result)
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def _get_problems_with_data(self, problem_ids):
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"""优化的题目数据获取"""
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problem_qs = (
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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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return {p.id: p for p in problem_qs}
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def _build_solved_records(self, user_first_ac, by_problem, problems, user_id):
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"""构建解题记录"""
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solved = []
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contest_ids = []
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for item in user_first_ac:
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pid = item["problem_id"]
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ranking_list = by_problem.get(pid, [])
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# 查找用户排名
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rank = None
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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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rank = idx + 1
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break
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problem = problems.get(pid)
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if not problem:
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continue
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grade = get_grade(rank, len(ranking_list))
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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": grade,
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}
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)
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# 按AC时间排序
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solved.sort(key=lambda x: x["ac_time"])
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return solved, contest_ids
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def _calculate_average_grade(self, solved):
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"""计算平均等级（出现次数最多的等级）"""
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if not solved:
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return ""
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grade_count = defaultdict(int)
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for s in solved:
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grade_count[s["grade"]] += 1
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return max(grade_count, key=grade_count.get)
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def _calculate_top_tags(self, problems):
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"""计算标签TOP5"""
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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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top_tags = sorted(tags_counter.items(), key=lambda x: x[1], reverse=True)[:5]
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return {name: count for name, count in top_tags}
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def _calculate_difficulty_distribution(self, problems):
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"""计算难度分布"""
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diff_counter = {"Low": 0, "Mid": 0, "High": 0}
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for problem in problems:
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key = problem.difficulty if problem.difficulty in diff_counter else "Mid"
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diff_counter[key] += 1
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diff_sorted = sorted(diff_counter.items(), key=lambda x: x[1], reverse=True)
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return {get_difficulty(k): v for k, v in diff_sorted}
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class AIWeeklyDataAPI(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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username = request.GET.get("username")
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if username:
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try:
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user = User.objects.get(username=username)
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except User.DoesNotExist:
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return self.error("User does not exist")
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else:
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user = request.user
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# 检查缓存
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cache_key = get_cache_key(
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"ai_weekly", 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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# 获取班级用户ID
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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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# 解析时间参数
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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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weekly_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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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": 0,
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"grade": "",
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}
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# 获取提交数量
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submission_count = Submission.objects.filter(
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user_id=user.id,
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create_time__gte=start,
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create_time__lte=period_end,
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).count()
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period_data["submission_count"] = submission_count
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if submission_count == 0:
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weekly_data.append(period_data)
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continue
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# 获取AC记录和等级
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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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)
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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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weekly_data.append(period_data)
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# 缓存结果
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cache.set(cache_key, weekly_data, CACHE_TIMEOUT)
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return self.success(weekly_data)
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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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# 默认配置
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show_count = 4
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show_unit = "weeks"
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total_delta = timedelta(weeks=show_count + 1)
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delta = timedelta(weeks=1)
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if unit == "months" and count == 2:
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# 过去八周
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show_count = 8
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total_delta = timedelta(weeks=9)
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elif unit == "months" and count == 6:
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# 过去六个月
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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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elif unit == "years":
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# 过去一年
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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 {
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"show_count": show_count,
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"show_unit": show_unit,
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"total_delta": total_delta,
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"delta": delta,
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}
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def _calculate_period_grade(self, user_first_ac, by_problem, user_id):
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"""计算周期内的等级"""
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grade_count = defaultdict(int)
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for item in user_first_ac:
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pid = item["problem_id"]
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ranking_list = by_problem.get(pid, [])
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# 查找用户排名
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rank = None
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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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rank = idx + 1
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break
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grade = get_grade(rank, len(ranking_list))
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grade_count[grade] += 1
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return max(grade_count, key=grade_count.get) if grade_count else ""
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class AIAnalysisAPI(APIView):
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@login_required
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def post(self, request):
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user = request.user
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details = request.data.get("details")
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weekly = request.data.get("weekly")
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api_key = get_env("AI_KEY")
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if not api_key:
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return self.error("API_KEY is not set")
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client = OpenAI(api_key=api_key, base_url="https://api.deepseek.com")
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system_prompt ="""
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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。
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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。
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请使用 markdown 格式输出，不要在代码块中输出。
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最后不要忘记写一句祝福语。
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"""
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user_prompt = f"""
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这段时间内的详细数据: {details}
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每周或每月的数据: {weekly}
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"""
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analysis_chunks = []
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saved = False
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save_error = None
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store_error_sent = False
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def try_save():
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nonlocal saved, save_error
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if saved:
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return
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if not analysis_chunks:
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saved = True
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return
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try:
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AIAnalysis.objects.create(
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user=user,
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data={"details": details, "weekly": weekly},
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system_prompt=dedent(system_prompt).strip(),
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user_prompt="这段时间内的详细数据, 每周或每月的数据",
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analysis="".join(analysis_chunks).strip(),
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)
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except Exception as exc:
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save_error = str(exc)
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finally:
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saved = True
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def stream_generator():
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nonlocal store_error_sent
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try:
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stream = client.chat.completions.create(
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model="deepseek-chat",
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messages=[
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{
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"role": "system",
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"content": system_prompt,
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},
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{
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"role": "user",
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"content": user_prompt,
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},
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],
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stream=True,
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)
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except Exception as exc:
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payload = json.dumps({"type": "error", "message": str(exc)})
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yield f"data: {payload}\n\n"
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yield "event: end\n\n"
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return
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yield "event: start\n\n"
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try:
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for chunk in stream:
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if not chunk.choices:
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continue
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choice = chunk.choices[0]
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finish_reason = getattr(choice, "finish_reason", None)
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delta = getattr(choice, "delta", None)
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raw_content = getattr(delta, "content", None)
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if raw_content:
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if isinstance(raw_content, list):
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text_content = "".join(
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(
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item.get("text")
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if isinstance(item, dict)
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else getattr(item, "text", None) or ""
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)
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for item in raw_content
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)
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else:
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text_content = str(raw_content)
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if text_content:
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analysis_chunks.append(text_content)
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payload = json.dumps(
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{"type": "delta", "content": text_content}
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)
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yield f"data: {payload}\n\n"
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if finish_reason:
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try_save()
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if save_error and not store_error_sent:
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error_payload = json.dumps(
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{"type": "store_error", "message": save_error}
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)
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yield f"data: {error_payload}\n\n"
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store_error_sent = True
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payload = json.dumps({"type": "done"})
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yield f"data: {payload}\n\n"
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break
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except Exception as exc:
|
|
payload = json.dumps({"type": "error", "message": str(exc)})
|
|
yield f"data: {payload}\n\n"
|
|
finally:
|
|
try_save()
|
|
if save_error and not store_error_sent:
|
|
error_payload = json.dumps(
|
|
{"type": "store_error", "message": save_error}
|
|
)
|
|
yield f"data: {error_payload}\n\n"
|
|
store_error_sent = True
|
|
|
|
yield "event: end\n\n"
|
|
|
|
response = StreamingHttpResponse(
|
|
streaming_content=stream_generator(),
|
|
content_type="text/event-stream",
|
|
)
|
|
response["Cache-Control"] = "no-cache"
|
|
return response
|