508 lines
21 KiB
Python
508 lines
21 KiB
Python
"""
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ATRI CF 查分与刷题分析插件 📊 v1.0
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查询 Codeforces 用户信息、Rating、刷题统计
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支持用户绑定、可视化图表、分析报告
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"""
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import json
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import os
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import re
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import time
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import urllib.request
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from collections import Counter
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from pathlib import Path
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from astrbot.api.all import *
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from astrbot.api.event import filter, AstrMessageEvent
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from astrbot.api.star import Context, Star, register
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# ---------- 数据存储 ----------
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BIND_DIR = Path("data/plugins/astrbot_plugin_cf_query")
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BIND_FILE = BIND_DIR / "bindings.json"
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def _load_bindings() -> dict:
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if BIND_FILE.exists():
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with open(BIND_FILE, "r", encoding="utf-8") as f:
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return json.load(f)
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return {}
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def _save_bindings(data: dict):
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BIND_DIR.mkdir(parents=True, exist_ok=True)
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with open(BIND_FILE, "w", encoding="utf-8") as f:
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json.dump(data, f, ensure_ascii=False, indent=2)
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# ---------- CF API 工具函数 ----------
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def _cf_api(url: str) -> dict | None:
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"""调用 CF API，返回 result 或 None"""
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req = urllib.request.Request(url, headers={"User-Agent": "ATRI-CF-Plugin/1.0"})
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try:
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with urllib.request.urlopen(req, timeout=15) as resp:
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data = json.loads(resp.read())
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if data.get("status") == "OK":
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return data["result"]
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return None
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except Exception:
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return None
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def _query_user_info(handle: str) -> dict | None:
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result = _cf_api(f"https://codeforces.com/api/user.info?handles={handle}")
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if result and len(result) > 0:
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return result[0]
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return None
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def _query_rating_history(handle: str) -> list:
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result = _cf_api(f"https://codeforces.com/api/user.rating?handle={handle}")
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return result if result else []
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def _query_submissions(handle: str, count: int = 200) -> list:
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result = _cf_api(
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f"https://codeforces.com/api/user.status?handle={handle}&from=1&count={count}"
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)
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return result if result else []
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def _analyze_submissions(submissions: list) -> dict:
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"""分析提交记录，返回统计信息"""
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total = len(submissions)
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ac = sum(1 for s in submissions if s.get("verdict") == "OK")
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wa = sum(1 for s in submissions if "WRONG_ANSWER" in s.get("verdict", ""))
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tle = sum(1 for s in submissions if "TIME_LIMIT_EXCEEDED" in s.get("verdict", ""))
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ce = sum(1 for s in submissions if "COMPILATION_ERROR" in s.get("verdict", ""))
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rating_counter = Counter()
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tag_counter = Counter()
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solved = set()
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ac_records = []
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for s in submissions:
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if s.get("verdict") == "OK":
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pid = f"{s['problem'].get('contestId', '')}{s['problem']['index']}"
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rtg = s["problem"].get("rating", 0)
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if pid not in solved:
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solved.add(pid)
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rating_counter[rtg] += 1
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for t in s["problem"].get("tags", []):
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tag_counter[t] += 1
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ac_records.append({
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"pid": pid,
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"name": s["problem"]["name"],
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"rating": rtg,
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"time": s.get("creationTimeSeconds", 0),
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})
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ac_records.sort(key=lambda x: x["time"], reverse=True)
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return {
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"total": total,
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"ac": ac,
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"wa": wa,
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"tle": tle,
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"ce": ce,
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"solved_count": len(solved),
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"rating_dist": dict(sorted(rating_counter.items())),
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"tag_dist": dict(tag_counter.most_common(10)),
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"recent_ac": ac_records[:10],
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}
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def _generate_rating_chart(handle: str, history: list, save_path: str) -> bool:
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try:
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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import numpy as np
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except ImportError:
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return False
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if not history:
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return False
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dates, ratings, old_ratings, changes, colors = [], [], [], [], []
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for c in history:
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ts = time.strftime("%m/%d", time.localtime(c["ratingUpdateTimeSeconds"]))
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dates.append(f"#{c['contestId']}\n{ts}")
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old_ratings.append(c["oldRating"])
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ratings.append(c["newRating"])
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ch = c["newRating"] - c["oldRating"]
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changes.append(ch)
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colors.append("#3eb86b" if ch >= 0 else "#e8785a")
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fig, ax = plt.subplots(figsize=(10, 5.5))
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fig.patch.set_facecolor("#f8f4f0")
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ax.set_facecolor("#ffffff")
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x = np.arange(len(dates))
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bars = ax.bar(x, ratings, width=0.5, color=colors, edgecolor="white", linewidth=1.5, zorder=3)
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ax.bar(x, old_ratings, width=0.5, color="#e0d0c0", alpha=0.4, edgecolor="white", linewidth=1.5, zorder=2)
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for i, (bar, r, ch) in enumerate(zip(bars, ratings, changes)):
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ax.text(bar.get_x() + bar.get_width()/2, bar.get_height()+15, str(r),
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ha="center", va="bottom", fontsize=13, fontweight="bold", color="#333")
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sign = "+" if ch > 0 else ""
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clr = "#3eb86b" if ch > 0 else "#e8785a"
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mid = old_ratings[i] + (r - old_ratings[i]) / 2
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ax.annotate(f"{sign}{ch}", xy=(bar.get_x()+bar.get_width()/2, mid),
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fontsize=11, fontweight="bold", color="white", ha="center", va="center",
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bbox=dict(boxstyle="round,pad=0.2", facecolor=clr, edgecolor="none", alpha=0.85))
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ax.set_xticks(x)
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ax.set_xticklabels(dates, fontsize=10)
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ax.set_title(f"{handle} · Rating Changes", fontsize=17, fontweight="bold", color="#d06040", pad=15)
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ax.set_ylabel("Rating", fontsize=12, fontweight="bold", color="#555")
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ax.set_ylim(0, max(ratings) * 1.3)
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for s in ["top", "right"]:
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ax.spines[s].set_visible(False)
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for s in ["left", "bottom"]:
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ax.spines[s].set_color("#ddd")
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ax.tick_params(colors="#888")
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ax.grid(axis="y", alpha=0.3, color="#ddd", zorder=0)
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plt.tight_layout()
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fig.savefig(save_path, dpi=180, bbox_inches="tight", facecolor="#f8f4f0")
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plt.close()
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return True
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def _generate_problem_chart(handle: str, rating_dist: dict, save_path: str) -> bool:
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try:
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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import numpy as np
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except ImportError:
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return False
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if not rating_dist:
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return False
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labels_map = {0: "Unrated"}
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labels, values, colors = [], [], []
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palette = ["#b0b0b0", "#8bc34a", "#4caf50", "#26a69a", "#00bcd4", "#2196f3", "#7e57c2", "#9c27b0"]
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for i, (rtg, cnt) in enumerate(sorted(rating_dist.items())):
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labels.append(labels_map.get(rtg, str(rtg)))
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values.append(cnt)
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colors.append(palette[i % len(palette)])
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fig, ax = plt.subplots(figsize=(10, 5))
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fig.patch.set_facecolor("#f8f4f0")
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ax.set_facecolor("#ffffff")
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x = np.arange(len(labels))
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bars = ax.bar(x, values, width=0.55, color=colors, edgecolor="white", linewidth=1.5, zorder=3)
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for bar, val in zip(bars, values):
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ax.text(bar.get_x() + bar.get_width()/2, bar.get_height()+0.1, str(val),
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ha="center", va="bottom", fontsize=15, fontweight="bold", color="#333")
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ax.set_xticks(x)
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ax.set_xticklabels(labels, fontsize=12, fontweight="bold")
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ax.set_title(f"{handle} · Problem Ratings", fontsize=17, fontweight="bold", color="#d06040", pad=15)
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ax.set_ylabel("Problems Solved", fontsize=12, fontweight="bold", color="#555")
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ax.set_ylim(0, max(values) * 1.3)
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for s in ["top", "right"]:
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ax.spines[s].set_visible(False)
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for s in ["left", "bottom"]:
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ax.spines[s].set_color("#ddd")
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ax.tick_params(colors="#888")
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ax.grid(axis="y", alpha=0.3, color="#ddd", zorder=0)
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plt.tight_layout()
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fig.savefig(save_path, dpi=180, bbox_inches="tight", facecolor="#f8f4f0")
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plt.close()
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return True
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def _generate_tag_chart(handle: str, tag_dist: dict, save_path: str) -> bool:
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try:
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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except ImportError:
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return False
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if not tag_dist:
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return False
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labels = list(tag_dist.keys())
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values = list(tag_dist.values())
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colors = ["#e8785a", "#f0a030", "#4a90d9", "#3eb86b", "#9b59b6",
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"#1abc9c", "#e74c3c", "#3498db", "#f39c12", "#2ecc71", "#95a5a6"]
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fig, ax = plt.subplots(figsize=(8, 6))
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fig.patch.set_facecolor("#f8f4f0")
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wedges, texts, autotexts = ax.pie(values, labels=None, autopct="%1.0f%%",
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startangle=90, pctdistance=0.78,
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colors=colors[:len(labels)],
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wedgeprops=dict(width=0.4, edgecolor="white", linewidth=2),
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textprops=dict(fontsize=11, fontweight="bold"))
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ax.set_title(f"{handle} · Tags", fontsize=17, fontweight="bold", color="#d06040", pad=20)
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ax.legend(wedges, [f"{l} ({v})" for l, v in zip(labels, values)],
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title="Tags", title_fontsize=12,
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loc="center left", bbox_to_anchor=(1, 0.5),
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fontsize=10, framealpha=0.9)
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plt.tight_layout(rect=[0, 0.03, 0.75, 0.97])
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fig.savefig(save_path, dpi=180, bbox_inches="tight", facecolor="#f8f4f0")
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plt.close()
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return True
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def _combine_charts(paths: list, save_path: str) -> bool:
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try:
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from PIL import Image
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except ImportError:
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return False
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imgs = [Image.open(p) for p in paths if os.path.exists(p)]
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if not imgs:
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return False
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width = max(img.width for img in imgs)
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resized = []
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for img in imgs:
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ratio = width / img.width
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resized.append(img.resize((width, int(img.height * ratio)), Image.LANCZOS))
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total_h = sum(img.height for img in resized) + 10 * (len(resized) - 1)
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combined = Image.new("RGB", (width, total_h), "#f8f4f0")
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y = 0
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for img in resized:
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combined.paste(img, (0, y))
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y += img.height + 10
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combined.save(save_path, quality=92)
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return True
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def _build_text_report(handle: str, user_info: dict, history: list, analysis: dict) -> str:
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"""构建文本分析报告"""
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rating = user_info.get("rating", 0)
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max_r = user_info.get("maxRating", 0)
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rank = user_info.get("rank", "unrated").title()
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wins = sum(1 for c in history if c["newRating"] > c["oldRating"])
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losses = sum(1 for c in history if c["newRating"] < c["oldRating"])
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best_rank = min((c["rank"] for c in history), default="-")
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s = analysis
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ac_rate = f"{s['ac'] / s['total'] * 100:.1f}%" if s["total"] > 0 else "-"
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lines = [
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f"📊 CF Profile: {handle}",
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f"━━━━━━━━━━━━━━━━━━━━",
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f"Rating: {rating} ({rank.title()})",
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f"最高: {max_r} ({user_info.get('maxRank', 'unrated').title()})",
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f"参赛: {len(history)}场 | 涨{wins}掉{losses}",
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f"最佳排名: #{best_rank}",
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f"━━━━━━━━━━━━━━━━━━━━",
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f"📝 刷题统计",
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f"提交: {s['total']} | AC: {s['ac']} | AC率: {ac_rate}",
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f"通过不同题目: {s['solved_count']}道",
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]
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if s["rating_dist"]:
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lines.append("━━━━━━━━━━━━━━━━━━━━")
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lines.append("🎯 难度分布:")
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for r, c in sorted(s["rating_dist"].items()):
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lbl = "Unrated" if r == 0 else str(r)
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lines.append(f" {lbl:>7}: {'█'*c} {c}")
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if s["recent_ac"]:
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lines.append("━━━━━━━━━━━━━━━━━━━━")
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lines.append("🔥 最近AC:")
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for a in s["recent_ac"][:5]:
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rtg = f"[{a['rating']}]" if a["rating"] else "[?]"
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lines.append(f" ✅ {a['pid']} {rtg} {a['name']}")
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return "\n".join(lines)
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def _build_analysis_prompt(handle: str, user_info: dict, history: list, analysis: dict) -> str:
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"""构建AI分析用的prompt"""
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rating = user_info.get("rating", 0)
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max_r = user_info.get("maxRating", 0)
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rank = user_info.get("rank", "unrated").title()
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total_contests = len(history)
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wins = sum(1 for c in history if c["newRating"] > c["oldRating"])
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losses = sum(1 for c in history if c["newRating"] < c["oldRating"])
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best_rank = min((c["rank"] for c in history), default="无")
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ac_rate = f"{analysis['ac']/analysis['total']*100:.1f}%" if analysis["total"] > 0 else "无数据"
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recent = analysis["recent_ac"][:5]
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recent_str = "\n".join(f" - {a['pid']} [{a['rating']}] {a['name']}" for a in recent) if recent else " 无"
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dist_parts = []
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for r, c in sorted(analysis["rating_dist"].items()):
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lbl = "Unrated" if r == 0 else str(r)
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dist_parts.append(f"{lbl}: {c}题")
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dist_str = ", ".join(dist_parts)
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return f"""请根据以下Codeforces用户数据，生成一段简短、有温度的分析与建议（50~100字，中文，语气像朋友聊天一样自然，不要过于正式）：
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用户：{handle}
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当前Rating：{rating}（{rank}）
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最高Rating：{max_r}
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参赛：{total_contests}场（涨{wins}掉{losses}）
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最佳排名：#{best_rank}
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AC率：{ac_rate}
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通过题目数：{analysis['solved_count']}道
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难度分布：{dist_str}
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最近AC：
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{recent_str}
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请从以下角度给出简短分析：
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1. 当前水平总结（一句话）
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2. 优势和不足
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3. 后续练习建议"""
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# ---------- 主插件类 ----------
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@register("astrbot_plugin_cf_query", "ATRI (YHN-04B-009)", "CF 查分与刷题分析，支持绑定、图表、报告", "1.0.0", "https://gitea.kronecker.cc/Kronecker/ATRI-NOTES")
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class CFQueryPlugin(Star):
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def __init__(self, context: Context, config: dict = None):
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super().__init__(context, config)
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self.config = config or {}
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self.temp_dir = Path("/AstrBot/data/temp")
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# ========== 绑定 ==========
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@filter.command("cfbind")
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async def cf_bind(self, event: AstrMessageEvent, handle: str = None):
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"""绑定CF账号：cfbind <用户名>"""
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if not handle:
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yield event.plain_result("❌ 用法: cfbind <CF用户名>")
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return
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handle = handle.strip()
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info = _query_user_info(handle)
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if not info:
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yield event.plain_result(f"❌ CF用户 {handle} 不存在，请检查用户名")
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return
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uid = str(event.get_sender_id())
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bindings = _load_bindings()
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bindings[uid] = {
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"handle": handle,
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"bind_time": int(time.time()),
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"rating": info.get("rating", 0),
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}
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_save_bindings(bindings)
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rating = info.get("rating", 0)
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rank = info.get("rank", "unrated").title()
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yield event.plain_result(f"✅ 绑定成功！\n{handle} | Rating: {rating} ({rank})")
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@filter.command("cfunbind")
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async def cf_unbind(self, event: AstrMessageEvent):
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"""解绑CF账号"""
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uid = str(event.get_sender_id())
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bindings = _load_bindings()
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if uid not in bindings:
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yield event.plain_result("❌ 你还没有绑定CF账号")
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return
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yield event.plain_result(f"✅ 已解绑 {bindings[uid]['handle']}")
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del bindings[uid]
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_save_bindings(bindings)
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@filter.command("cfwho")
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async def cf_who(self, event: AstrMessageEvent):
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"""查看绑定信息"""
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uid = str(event.get_sender_id())
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bindings = _load_bindings()
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if uid not in bindings:
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yield event.plain_result("❌ 你还没有绑定CF账号，使用 cfbind <用户名> 绑定")
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return
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info = bindings[uid]
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t = time.strftime('%Y-%m-%d', time.localtime(info['bind_time']))
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yield event.plain_result(f"🔗 已绑定: {info['handle']} ({t})")
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@filter.command("cftop")
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async def cf_top(self, event: AstrMessageEvent):
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"""群内CF排行榜"""
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bindings = _load_bindings()
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if not bindings:
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yield event.plain_result("📭 还没有人绑定CF账号")
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return
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users = []
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for uid, info in bindings.items():
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handle = info["handle"]
|
||
fresh = _query_user_info(handle)
|
||
rating = fresh.get("rating", 0) if fresh else info.get("rating", 0)
|
||
users.append((handle, rating))
|
||
time.sleep(0.5)
|
||
users.sort(key=lambda x: x[1], reverse=True)
|
||
lines = ["🏆 群内CF排行榜", "━━━━━━━━━━━━━━"]
|
||
emojis = ["🥇", "🥈", "🥉"]
|
||
for i, (handle, rating) in enumerate(users[:10]):
|
||
emo = emojis[i] if i < 3 else f"{i+1}."
|
||
lines.append(f"{emo} {handle} — {rating}")
|
||
yield event.plain_result("\n".join(lines))
|
||
|
||
# ========== 主查询 ==========
|
||
@filter.command("cf")
|
||
async def cf_query(self, event: AstrMessageEvent, handle: str = None):
|
||
"""查询CF用户：cf <用户名> 或 cf（查已绑定）"""
|
||
if not handle:
|
||
uid = str(event.get_sender_id())
|
||
bindings = _load_bindings()
|
||
if uid in bindings:
|
||
handle = bindings[uid]["handle"]
|
||
if not handle:
|
||
yield event.plain_result("❌ 用法: cf <用户名> 或先用 cfbind 绑定后直接 cf")
|
||
return
|
||
|
||
yield event.plain_result(f"🔍 正在查询 {handle} 的数据...")
|
||
|
||
user_info = _query_user_info(handle)
|
||
if not user_info:
|
||
yield event.plain_result(f"❌ 查询失败，用户 {handle} 不存在或网络异常")
|
||
return
|
||
|
||
history = _query_rating_history(handle)
|
||
submissions = _query_submissions(handle, 200)
|
||
analysis = _analyze_submissions(submissions)
|
||
|
||
# --- 文本报告 ---
|
||
rating = user_info.get("rating", 0)
|
||
max_r = user_info.get("maxRating", 0)
|
||
rank = user_info.get("rank", "unrated").title()
|
||
wins = sum(1 for c in history if c["newRating"] > c["oldRating"])
|
||
losses = sum(1 for c in history if c["newRating"] < c["oldRating"])
|
||
best_rank = min((c["rank"] for c in history), default="-")
|
||
ac_rate = f"{analysis['ac']/analysis['total']*100:.1f}%" if analysis["total"] > 0 else "-"
|
||
|
||
lines = [
|
||
f"📊 CF Profile: {handle}",
|
||
f"━━━━━━━━━━━━━━━━━━━━",
|
||
f"Rating: {rating} ({rank}) | 最高: {max_r}",
|
||
f"参赛: {len(history)}场 | 涨{wins}掉{losses} | 最佳排名: #{best_rank}",
|
||
f"━━━━━━━━━━━━━━━━━━━━",
|
||
f"提交: {analysis['total']} | AC: {analysis['ac']} | AC率: {ac_rate}",
|
||
f"通过不同题目: {analysis['solved_count']}道",
|
||
]
|
||
if analysis["rating_dist"]:
|
||
lines.append("━━━━━━━━━━━━━━━━━━━━")
|
||
lines.append("🎯 难度分布:")
|
||
for r, c in sorted(analysis["rating_dist"].items()):
|
||
lbl = "Unrated" if r == 0 else str(r)
|
||
lines.append(f" {lbl:>7}: {'█'*c} {c}")
|
||
if analysis["recent_ac"]:
|
||
lines.append("━━━━━━━━━━━━━━━━━━━━")
|
||
lines.append("🔥 最近AC:")
|
||
for a in analysis["recent_ac"][:5]:
|
||
rtg = f"[{a['rating']}]" if a["rating"] else "[?]"
|
||
lines.append(f" ✅ {a['pid']} {rtg} {a['name']}")
|
||
text_report = "\n".join(lines)
|
||
|
||
# --- 图表 ---
|
||
ts = int(time.time())
|
||
charts = []
|
||
rc = str(self.temp_dir / f"cf_rating_{handle}_{ts}.png")
|
||
if _generate_rating_chart(handle, history, rc):
|
||
charts.append(rc)
|
||
pc = str(self.temp_dir / f"cf_problems_{handle}_{ts}.png")
|
||
if _generate_problem_chart(handle, analysis["rating_dist"], pc):
|
||
charts.append(pc)
|
||
tc = str(self.temp_dir / f"cf_tags_{handle}_{ts}.png")
|
||
if _generate_tag_chart(handle, analysis["tag_dist"], tc):
|
||
charts.append(tc)
|
||
|
||
style = self.config.get("default_chart_style", "combined")
|
||
if charts:
|
||
# 先发图表
|
||
if style == "combined" and len(charts) >= 2:
|
||
combined = str(self.temp_dir / f"cf_combined_{handle}_{ts}.png")
|
||
if _combine_charts(charts, combined):
|
||
yield event.image_result(combined)
|
||
else:
|
||
for c in charts:
|
||
yield event.image_result(c)
|
||
|
||
# 再用AI生成分析文本
|
||
try:
|
||
analysis_prompt = _build_analysis_prompt(handle, user_info, history, analysis)
|
||
provider_id = self.context.get_current_chat_provider_id(event.unified_msg_origin)
|
||
llm_resp = await self.context.llm_generate(
|
||
chat_provider_id=provider_id,
|
||
prompt=analysis_prompt,
|
||
)
|
||
if llm_resp and llm_resp.completion_text:
|
||
yield event.plain_result(llm_resp.completion_text)
|
||
except Exception as e:
|
||
# AI分析失败时打印错误但不影响主流程
|
||
yield event.plain_result(f"⚠️ AI分析异常: {type(e).__name__}: {e}")
|
||
return
|
||
|
||
# 没有图表时才发文本报告
|
||
yield event.plain_result(text_report)
|