233 lines
8.3 KiB
Python
233 lines
8.3 KiB
Python
"""
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儿童心理陪伴 - 筛查器
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基于 MiniMax API，对儿童对话进行心理问题筛查
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"""
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from __future__ import annotations
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import os
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import re
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import json
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import requests
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from typing import Literal
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from pydantic import BaseModel, Field
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# ============================================================================
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# 数据模型
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# ============================================================================
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class ConcernCategory(str):
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"""心理问题类别"""
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NONE = "none"
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BULLYING = "bullying" # 校园霸凌/同伴冲突
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DEPRESSION = "depression" # 抑郁情绪
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ANXIETY = "anxiety" # 焦虑/恐惧
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FAMILY_CONFLICT = "family_conflict" # 家庭矛盾
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SELF_ESTEEM = "self_esteem" # 自卑/自我否定
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TRAUMA = "trauma" # 创伤事件
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SOCIAL_ISOLATION = "social_isolation" # 社交孤立
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OTHER = "other" # 其他
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class ScreeningResult(BaseModel):
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"""筛查结果"""
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detected: bool = Field(description="是否检测到心理问题")
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category: str = Field(default=ConcernCategory.NONE, description="问题类别")
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severity: Literal["none", "low", "medium", "high"] = Field(
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default="none", description="严重程度"
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)
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summary: str = Field(default="", description="简要描述检测到的问题")
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suggestion: str = Field(default="", description="建议行动")
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raw_response: str = Field(default="", description="模型原始响应（调试用）")
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# ============================================================================
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# 筛查系统提示词
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# ============================================================================
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SYSTEM_PROMPT = """你是一个专业的儿童心理咨询师助手，专注于分析3-8岁儿童的对话内容，识别潜在的心理需求或问题。
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## 你的任务
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分析给定的儿童对话上下文，判断是否存在以下心理问题类别：
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1. **bullying** - 霸凌/同伴冲突：孩子表达被欺负、被嘲笑、被孤立、被人威胁等
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2. **depression** - 抑郁情绪：孩子表达悲伤、绝望、无助、对事物失去兴趣、提到"不想活了"等
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3. **anxiety** - 焦虑/恐惧：孩子表达担心、害怕、做噩梦、回避某些情境等
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4. **family_conflict** - 家庭矛盾：孩子表达父母争吵、离婚担心、被忽视、被严厉惩罚等
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5. **self_esteem** - 自卑/自我否定：孩子表达"我不行"、"没人喜欢我"、"我太笨了"等
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6. **trauma** - 创伤事件：孩子描述意外事故、暴力事件、亲人离世等创伤性经历
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7. **social_isolation** - 社交孤立：孩子表达没有朋友、被排斥、孤独感等
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8. **other** - 其他值得关注的心理需求
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## 输出格式
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请严格按以下JSON格式返回（不要添加任何额外内容）：
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{
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"detected": true/false,
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"category": "具体类别",
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"severity": "none/low/medium/high",
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"summary": "一句话描述检测到的问题",
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"suggestion": "建议的应对方式（简短，1-2句话）"
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}
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## 判断标准
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- **low**: 轻微迹象，需要关注但无需立即介入
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- **medium**: 中度迹象，建议与家长沟通
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- **high**: 严重迹象，需要专业干预
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如果对话内容完全正常，没有任何心理问题迹象，返回：
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{
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"detected": false,
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"category": "none",
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"severity": "none",
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"summary": "未检测到心理问题",
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"suggestion": ""
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}
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注意：
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- 只关注确实存在问题的迹象，不要过度解读
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- 儿童的语言表达可能不精确，需要结合上下文判断
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- 正常的情绪表达（偶尔哭、发脾气）不构成问题"""
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# ============================================================================
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# 筛查器
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# ============================================================================
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class PsychoScreener:
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"""
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儿童心理问题筛查器
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使用方法：
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screener = PsychoScreener(api_key="your-api-key")
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result = screener.screen("今天小明打我了，我很伤心")
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if result.detected:
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print(f"检测到问题：{result.summary}")
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"""
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def __init__(
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self,
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api_key: str | None = None,
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model: str = "MiniMax-M2.5",
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base_url: str = "https://api.minimaxi.com/v1/text/chatcompletion_v2",
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):
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self.api_key = api_key or os.environ.get("MINIMAX_API_KEY", "")
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self.model = model
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self.base_url = base_url
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if not self.api_key:
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raise ValueError(
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"MiniMax API key is required. "
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"Set MINIMAX_API_KEY env var or pass api_key parameter."
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)
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def _call_minimax(self, messages: list[dict]) -> str:
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"""调用 MiniMax API"""
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json",
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}
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payload = {
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"model": self.model,
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"messages": messages,
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}
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response = requests.post(
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self.base_url,
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headers=headers,
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json=payload,
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timeout=30,
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)
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response.raise_for_status()
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data = response.json()
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# 兼容不同返回格式
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if "choices" in data:
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return data["choices"][0]["message"]["content"]
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elif "output" in data:
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return data["output"]
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return str(data)
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def screen(self, context: str) -> ScreeningResult:
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"""
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对给定的对话上下文进行心理问题筛查
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Args:
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context: 儿童的对话上下文（可以是多轮对话的汇总文本）
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Returns:
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ScreeningResult: 包含检测结果的数据模型
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"""
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": f"请分析以下儿童对话内容：\n\n{context}"},
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]
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try:
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raw_response = self._call_minimax(messages)
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except Exception as e:
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return ScreeningResult(
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detected=False,
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category=ConcernCategory.OTHER,
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severity="none",
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summary=f"API调用失败: {str(e)}",
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suggestion="",
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raw_response=str(e),
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)
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# 尝试解析 JSON
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try:
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content = raw_response.strip()
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# 策略1：查找 ```json ... ``` 代码块（Markdown 格式）
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md_match = re.search(r"```json\s*(.*?)\s*```", content, re.DOTALL)
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if md_match:
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content = md_match.group(1).strip()
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else:
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# 策略2：查找原始 JSON 对象 { ... }
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json_start = content.find('{"')
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json_end = content.rfind('"}')
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if json_start != -1 and json_end != -1 and json_end > json_start:
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content = content[json_start:json_end + 2]
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else:
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# 策略3：去掉思考过程标记，取最后一个 { 之后的内容
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last_brace = content.rfind('{')
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if last_brace != -1:
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content = content[last_brace:]
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parsed = json.loads(content)
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return ScreeningResult(
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detected=parsed.get("detected", False),
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category=parsed.get("category", ConcernCategory.NONE),
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severity=parsed.get("severity", "none"),
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summary=parsed.get("summary", ""),
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suggestion=parsed.get("suggestion", ""),
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raw_response=raw_response,
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)
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except json.JSONDecodeError:
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# 无法解析 JSON，返回原始内容
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return ScreeningResult(
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detected=False,
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category=ConcernCategory.OTHER,
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severity="none",
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summary="无法解析模型响应",
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suggestion="",
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raw_response=raw_response,
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)
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def build_response_prefix(self, result: ScreeningResult) -> str:
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"""
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根据筛查结果构建响应前缀
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Args:
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result: 筛查结果
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Returns:
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str: 如果检测到问题，返回前缀字符串；否则返回空字符串
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"""
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if not result.detected:
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return ""
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return f"【已发现特定心理问题】类别：{result.category}，严重程度：{result.severity}，描述：{result.summary}"
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