401 lines
15 KiB
Python
401 lines
15 KiB
Python
# 练习方案生成服务
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import os
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import re
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import requests
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from datetime import datetime
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from app.config import load_api_config
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def generate_practice_plan(
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student_name, problems, practice_time="30-60分钟", goals=None
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):
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"""
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根据学员问题和练习时间生成针对性练习方案
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Args:
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student_name: 学员姓名
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problems: 问题列表 [{problem_id, problem_name, severity, level, content}]
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practice_time: 练习时间描述
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goals: 目标列表 [{goal_id, goal_name, goal_content, status, mastery_level}]，默认为空列表
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Returns:
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dict: 包含方案内容的字典
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"""
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# 解析练习时间
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time_mapping = {
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"15分钟": {"total": 15, "basic": 10, "tech": 2, "piece": 3},
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"30分钟": {"total": 30, "basic": 15, "tech": 5, "piece": 10},
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"45分钟": {"total": 45, "basic": 15, "tech": 10, "piece": 20},
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"60分钟": {"total": 60, "basic": 20, "tech": 15, "piece": 25},
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"90分钟": {"total": 90, "basic": 25, "tech": 25, "piece": 40},
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"120分钟": {"total": 120, "basic": 30, "tech": 35, "piece": 55},
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"150分钟以上": {"total": 150, "basic": 35, "tech": 45, "piece": 70},
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}
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time_config = time_mapping.get(practice_time, time_mapping["30分钟"])
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# 从数据库问题内容构建
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problem_contents = []
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for p in problems:
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content = p.get("content", "") or ""
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problem_contents.append(
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{
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"name": p["problem_name"],
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"severity": p["severity"],
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"level": p.get("level", ""),
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"content": _extract_key_sections(content) if content else {"problem": f"针对{p['problem_name']}的练习"},
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"time_allocation": _calculate_time_allocation(
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p["severity"], time_config
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),
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}
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)
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# 生成方案
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plan = {
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"student_name": student_name,
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"practice_time": practice_time,
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"total_daily_minutes": time_config["total"],
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"problems": [
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{
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"name": p["name"],
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"severity": p["severity"],
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"level": p.get("level", ""),
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"focus": p["time_allocation"],
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}
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for p in problem_contents
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],
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"generated_at": datetime.now().strftime("%Y-%m-%d"),
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}
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return plan
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def _extract_key_sections(content):
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"""从问题文件中提取关键部分"""
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sections = {}
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# 提取问题表现
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match = re.search(r"## 问题表现\s*\n(.*?)(?=##|$)", content, re.DOTALL)
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if match:
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sections["problem"] = match.group(1).strip()
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# 提取原因分析
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match = re.search(r"## 原因分析\s*\n(.*?)(?=##|$)", content, re.DOTALL)
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if match:
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sections["reason"] = match.group(1).strip()
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# 提取日常基础练习
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match = re.search(
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r"## 针对性练习方案\s*.*?\| 练习名称.*?\n\|[-|\s]+\|.*?\n((?:\|.*?\n)+)",
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content,
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re.DOTALL,
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)
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if match:
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table_lines = match.group(1).strip().split("\n")
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practices = []
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for line in table_lines:
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if "|" in line and line.strip():
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parts = [p.strip() for p in line.split("|")[1:-1]]
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if len(parts) >= 4 and parts[0] != "练习名称":
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practices.append(
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{
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"name": parts[0],
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"duration": parts[1],
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"frequency": parts[2],
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"purpose": parts[3],
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}
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)
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sections["practices"] = practices
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# 提取具体操作
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match = re.search(r"## 具体操作\s*\n(.*?)(?=##|$)", content, re.DOTALL)
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if match:
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sections["operations"] = match.group(1).strip()
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# 提取练习提醒
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match = re.search(r"### ⚠️ 禁忌\s*\n(.*?)(?=###|$)", content, re.DOTALL)
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if match:
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sections["taboos"] = match.group(1).strip()
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match = re.search(r"### ✓ 正确做法\s*\n(.*?)(?=###|$)", content, re.DOTALL)
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if match:
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sections["dos"] = match.group(1).strip()
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# 提取评估标准
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match = re.search(r"## 评估标准\s*\n(.*?)(?=##|$)", content, re.DOTALL)
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if match:
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sections["standards"] = match.group(1).strip()
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return sections
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def _calculate_time_allocation(severity, time_config):
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"""根据严重程度计算时间分配"""
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multiplier = {"严重": 1.3, "中等": 1.0, "轻微": 0.7}.get(severity, 1.0)
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return {
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"basic": int(time_config["basic"] * multiplier),
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"tech": int(time_config["tech"] * multiplier),
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"piece": int(time_config["piece"] / multiplier)
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if severity == "严重"
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else time_config["piece"],
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}
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def _get_severity_advice(severity):
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"""根据严重程度给出建议"""
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advice = {
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"严重": "需要重点突破，建议每次练习开始时先练这部分，精力最充沛时处理难点。",
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"中等": "保持稳定练习，逐步改善，不要急于求成。",
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"轻微": "作为日常练习的一部分即可，注意保持。",
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}
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return advice.get(severity, "")
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# ==================== AI生成报告 ====================
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def generate_ai_report(
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student_name, wechat_nickname, problems, practice_time, time_config, template_id=None, dry_run=False, goals=None
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):
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"""
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使用AI生成个性化的练习报告
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Args:
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student_name: 学员姓名
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wechat_nickname: 微信昵称
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problems: 问题列表 [{problem_id, problem_name, severity, level, content}]
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practice_time: 练习时间描述
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time_config: 时间配置 {total, basic, tech, piece}
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template_id: 可选的AI提示词模板ID
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dry_run: 如果为True，只返回提示词不调用API
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goals: 目标列表 [{goal_id, goal_name, goal_content, status, mastery_level}]
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practice_time: 练习时间描述
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time_config: 时间配置 {total, basic, tech, piece}
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template_id: 可选的AI提示词模板ID
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dry_run: 如果为True，只返回提示词不调用API
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Returns:
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tuple: (prompt, ai_report, error) - 提示词、AI报告内容、错误信息
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"""
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config = load_api_config()
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api_key = config.get("api_key", "")
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base_url = config.get("base_url", "")
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model = config.get("model", "doubao-seed-2.0-pro")
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temperature = config.get("temperature", 0.7)
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provider = config.get("provider", "volcengine")
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# 优先从数据库模板获取AI提示词，其次从配置文件
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prompt_template = ""
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try:
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from app.models import Template
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if template_id:
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# 使用指定的模板
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tmpl = Template.query.get(template_id)
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if tmpl and tmpl.type == "ai_prompt":
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prompt_template = tmpl.content
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else:
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# 使用排序后的第一个模板
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tmpl = Template.query.filter_by(type="ai_prompt").order_by(Template.sort_order.asc()).first()
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if tmpl:
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prompt_template = tmpl.content
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except:
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pass
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if not prompt_template:
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prompt_template = config.get("prompt_template", "")
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# 从问题文件内容中提取关键信息
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def extract_problem_info(content_str):
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if not content_str or not isinstance(content_str, str):
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return {"practices": "针对性练习", "standards": ""}
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import re
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# 匹配表格中的练习名称
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practice_matches = re.findall(r"\|\s*([^|]+?)\s*\|\s*\d+\s*分钟", content_str)
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practice_names = (
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", ".join([m.strip() for m in practice_matches[:3]])
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if practice_matches
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else "针对性练习"
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)
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# 提取评估标准
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standards_match = re.search(
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r"## 评估标准\s*\n(.*?)(?=##|$)", content_str, re.DOTALL
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)
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standards = standards_match.group(1).strip() if standards_match else ""
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return {"practices": practice_names, "standards": standards}
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# 构建学员问题列表（学员被诊断出的问题）
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student_problems = []
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for p in problems:
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level = p.get("level", "入门")
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severity = p.get("severity", "中等")
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student_problems.append(f"- **{p['problem_name']}**（级别：{level}，程度：{severity}）")
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# 构建完整问题内容（包含所有详细信息）
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problems_full = []
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for p in problems:
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content = p.get("content", "") or ""
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level = p.get("level", "入门")
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severity = p.get("severity", "中等")
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problems_full.append(
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f"### 问题：{p['problem_name']}\n"
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f"**学员级别**: {level} | **问题程度**: {severity}\n\n"
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f"**详细内容和练习方法**:\n{content}"
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)
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# 构建学员未达成目标列表（只包含未完成的目标）
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student_goals = []
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if goals:
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for g in goals:
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# 只处理未完成的目标
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if g.get("status") != "已完成":
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student_goals.append(
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f"- **{g['goal_name']}**\n 内容：{g.get('goal_content', '未提供具体内容')}"
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)
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# 使用配置的模板，如果为空则使用默认模板
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if prompt_template:
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prompt = prompt_template.format(
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student_name=student_name,
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wechat_nickname=wechat_nickname or "未设置",
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practice_time=practice_time,
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student_problems="\n".join(student_problems) if student_problems else "无",
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problems="\n\n".join(problems_full) if problems_full else "无",
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student_goals="\n".join(student_goals) if student_goals else "无",
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)
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else:
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# 默认模板（向后兼容）
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prompt = f"""你是一位专业的钢琴教师，请为学员生成一份简洁的个性化练习方案报告。
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## 学员信息
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- 姓名：{student_name}
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- 微信昵称：{wechat_nickname}
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- 每日练习时间：{practice_time}（约{time_config["total"]}分钟）
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- 基础练习：{time_config["basic"]}分钟 | 技术练习：{time_config["tech"]}分钟 | 曲目练习：{time_config["piece"]}分钟
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## 级别定义
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- 启蒙：刚开始学习，需要建立基本概念
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- 入门：掌握基础姿势和简单曲目
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- 进阶：能独立练习中等难度曲目
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- 熟练：技术稳定，能处理复杂曲目
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- 精通：达到专业演奏水平
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## 问题详情
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{chr(10).join(problems_summary)}
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请生成一份简洁的练习方案报告（不超过400字），包含：
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1. 方案概述（一句话）
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2. 每日练习安排（按时间阶段列出）
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3. 针对每个问题的核心练习建议（1-2句话/问题）
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4. 重点注意事项（最多3条）
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语言要专业、简洁、有鼓励性。使用Markdown格式。"""
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# 如果是 dry_run（预览模式），直接返回提示词不调用API
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if dry_run:
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# 计算各部分字数
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student_problems_text = "\n".join(student_problems) if student_problems else "无"
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problems_text = "\n\n".join(problems_full) if problems_full else "无"
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student_goals_text = "\n".join(student_goals) if student_goals else "无"
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return prompt, None, None, {
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"student_problems_length": len(student_problems_text),
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"problems_length": len(problems_text),
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"student_goals_length": len(student_goals_text),
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}
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# API调用需要 Key
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if not api_key:
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return None, None, "未配置API Key，请在设置页面配置", None
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# 调用API
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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}
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payload = {
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"model": model,
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"messages": [{"role": "user", "content": prompt}],
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"temperature": temperature,
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"max_tokens": 4096,
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}
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# 确定 endpoint
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if provider == "minimax":
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endpoint = f"{base_url}/messages"
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else:
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endpoint = f"{base_url}/chat/completions"
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# 调试信息
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req_info = {"endpoint": endpoint, "model": model, "provider": provider}
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try:
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response = requests.post(
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endpoint,
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headers=headers,
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json=payload,
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timeout=180,
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allow_redirects=False,
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)
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if response.status_code == 200:
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try:
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result = response.json()
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except Exception as je:
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raw_text = response.text[:300] if response.text else "(empty body)"
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return prompt, None, f"API返回非JSON [{endpoint}]: {raw_text}", None
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# MiniMax (Anthropic格式) 返回结构不同
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if provider == "minimax":
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content_list = result.get("content", [])
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content = ""
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for item in content_list:
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if item.get("type") == "text":
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content = item.get("text", "")
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break
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extra = {"finish_reason": "n/a"}
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else:
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choice = result.get("choices", [{}])[0]
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content = choice.get("message", {}).get("content", "")
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finish = choice.get("finish_reason", "missing")
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usage = result.get("usage", {})
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# 调试信息
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extra = {
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"finish_reason": finish,
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"prompt_tokens": usage.get("prompt_tokens", 0),
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"completion_tokens": usage.get("completion_tokens", 0),
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"raw_keys": list(result.keys()),
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}
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if not content or not content.strip():
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err_detail = "API返回空内容"
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if extra.get("finish_reason", "") not in ("", "n/a"):
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err_detail += f" (finish_reason={extra['finish_reason']})"
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if extra.get("completion_tokens", 0) > 0:
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err_detail += f", 有{extra['completion_tokens']}个输出token但content为空"
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if extra.get("raw_keys"):
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err_detail += f", 响应keys={extra['raw_keys']}"
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return prompt, None, err_detail, extra
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return prompt, content, None, extra
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else:
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error_msg = f"API返回HTTP {response.status_code}"
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if response.status_code in (301, 302, 303, 307, 308):
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loc = response.headers.get("Location", "unknown")
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error_msg += f" 重定向到: {loc}"
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try:
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error_body = response.json()
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error_msg += ": " + error_body.get("error", {}).get("message", str(error_body)[:200])
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except:
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raw = response.text[:200] if response.text else "(empty)"
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error_msg += ": " + raw
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return prompt, None, error_msg, None
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except requests.exceptions.Timeout:
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return prompt, None, "AI响应超时（等待180秒），网络较慢或模型负载高，请重试", None
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except requests.exceptions.ConnectionError as e:
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return prompt, None, f"无法连接到API服务器，请检查网络或Endpoint配置", None
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except Exception as e:
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return prompt, None, f"调用API失败: {str(e)}", None
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