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refactor

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This commit is contained in:
yuetsh
2026-03-11 21:35:54 +08:00
parent 3763032a9a
commit 8a3a7e21cf
2 changed files with 135 additions and 249 deletions
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329
ai/views/oj.py
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@@ -40,6 +40,12 @@ SMALL_SCALE_PENALTY = {
"downgrade": {"S": "A", "A": "B"},
}
# 等级权重映射（用于加权平均计算）
GRADE_WEIGHTS = {"S": 4, "A": 3, "B": 2, "C": 1}
# 平均等级阈值：(最小权重, 等级)
AVERAGE_GRADE_THRESHOLDS = [(3.5, "S"), (2.5, "A"), (1.5, "B")]
def get_cache_key(prefix, *args):
return hashlib.md5(f"{prefix}:{'_'.join(map(str, args))}".encode()).hexdigest()
@@ -61,35 +67,44 @@ def get_grade(rank, submission_count):
特殊规则：
- 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）
Args:
rank: 用户排名（1表示第一名）
submission_count: 总AC人数
Returns:
评级字符串 ("S", "A", "B", "C")
"""
# 边界检查
if not rank or rank <= 0 or submission_count <= 0:
return "C"
# 计算百分位（0-100），使用 (rank-1) 使第一名的百分位为0
percentile = (rank - 1) / submission_count * 100
# 根据百分位确定基础评级
base_grade = "C"
for threshold, grade in GRADE_THRESHOLDS:
if percentile < threshold:
base_grade = grade
break
# 小规模参与惩罚：人数太少时降低评级
if submission_count < SMALL_SCALE_PENALTY["threshold"]:
base_grade = SMALL_SCALE_PENALTY["downgrade"].get(base_grade, base_grade)
return base_grade
def calculate_average_grade(grades):
"""根据等级列表计算加权平均等级"""
scores = [GRADE_WEIGHTS[g] for g in grades if g in GRADE_WEIGHTS]
if not scores:
return ""
avg = sum(scores) / len(scores)
for threshold, grade in AVERAGE_GRADE_THRESHOLDS:
if avg >= threshold:
return grade
return "C"
def find_user_rank(ranking_list, user_id):
"""在排名列表中找到用户的排名（1-based），未找到返回 None"""
return next(
(idx + 1 for idx, rec in enumerate(ranking_list) if rec["user_id"] == user_id),
None,
)
def get_class_user_ids(user):
if not user.class_name:
return []
@@ -137,6 +152,54 @@ def get_user_first_ac_submissions(
return user_first_ac, by_problem, problem_ids
def stream_ai_response(client, system_prompt, user_prompt, on_complete=None):
"""SSE 流式响应生成器，on_complete(full_text) 在流结束时调用"""
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"
chunks = []
try:
for chunk in stream:
if not chunk.choices:
continue
choice = chunk.choices[0]
if choice.finish_reason:
if on_complete:
on_complete("".join(chunks).strip())
yield f"data: {json.dumps({'type': 'done'})}\n\n"
break
content = choice.delta.content
if content:
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:
yield "event: end\n\n"
def make_sse_response(generator):
"""创建 SSE StreamingHttpResponse"""
response = StreamingHttpResponse(
streaming_content=generator,
content_type="text/event-stream",
)
response["Cache-Control"] = "no-cache"
return response
class AIDetailDataAPI(APIView):
@login_required
def get(self, request):
@@ -254,7 +317,7 @@ class AIDetailDataAPI(APIView):
{
"solved": solved,
"flowcharts": flowcharts_data,
"grade": self._calculate_average_grade(solved),
"grade": calculate_average_grade([s["grade"] for s in solved]),
"tags": self._calculate_top_tags(problems.values()),
"difficulty": self._calculate_difficulty_distribution(
problems.values()
@@ -275,14 +338,7 @@ class AIDetailDataAPI(APIView):
continue
ranking_list = by_problem.get(pid, [])
rank = next(
(
idx + 1
for idx, rec in enumerate(ranking_list)
if rec["user_id"] == user_id
),
None,
)
rank = find_user_rank(ranking_list, user_id)
if problem.contest_id:
contest_ids.append(problem.contest_id)
@@ -305,52 +361,6 @@ class AIDetailDataAPI(APIView):
return sorted(solved, key=lambda x: x["ac_time"]), contest_ids
def _calculate_average_grade(self, solved):
"""
计算平均等级，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:
solved: 已解决的题目列表，每个包含grade字段
Returns:
平均等级字符串 ("S", "A", "B", "C")
"""
if not solved:
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 s in solved:
grade = s["grade"]
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"
def _calculate_top_tags(self, problems):
tags_counter = defaultdict(int)
for problem in problems:
@@ -420,9 +430,14 @@ class AIDurationDataAPI(APIView):
)
if user_first_ac:
period_data["problem_count"] = len(problem_ids)
period_data["grade"] = self._calculate_period_grade(
user_first_ac, by_problem, user.id
)
grades = [
get_grade(
find_user_rank(by_problem.get(item["problem_id"], []), user.id),
len(by_problem.get(item["problem_id"], [])),
)
for item in user_first_ac
]
period_data["grade"] = calculate_average_grade(grades)
duration_data.append(period_data)
@@ -464,64 +479,6 @@ class AIDurationDataAPI(APIView):
},
)
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 AILoginSummaryAPI(APIView):
@@ -644,75 +601,20 @@ class AIAnalysisAPI(APIView):
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(其中部分字段含义是 flowcharts:流程图的提交,solved:代码的提交)\n每周或每月的数据: {duration}"
analysis_chunks = []
saved_instance = None
completed = False
def on_complete(full_text):
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=full_text,
)
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",
return make_sse_response(
stream_ai_response(client, system_prompt, user_prompt, on_complete)
)
response["Cache-Control"] = "no-cache"
return response
class AIHintAPI(APIView):
@@ -755,44 +657,9 @@ class AIHintAPI(APIView):
f"学生代码：\n```\n{submission.code[:2000]}\n```"
)
def stream_generator():
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:
yield f"data: {json.dumps({'type': 'done'})}\n\n"
break
content = choice.delta.content
if 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:
yield "event: end\n\n"
response = StreamingHttpResponse(
streaming_content=stream_generator(),
content_type="text/event-stream",
return make_sse_response(
stream_ai_response(client, system_prompt, user_prompt)
)
response["Cache-Control"] = "no-cache"
return response
class AIHeatmapDataAPI(APIView):