Youth-Mental-Health-Digital-Outreach

import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from dataclasses import dataclass
from enum import Enum

class RiskLevel(Enum):
    LOW = "low"
    MODERATE = "moderate"
    HIGH = "high"
    CRITICAL = "critical"

@dataclass
class MoodEntry:
    score: int           # 1-10, 使用者自評情緒分數
    text: str            # 使用者輸入的自由文本
    timestamp: float

class MentalHealthRiskClassifier:
    """結合文本語義與情緒軌跡的多層次風險分層模型"""

    _CRISIS_PATTERNS = [
        "不想醒來", "消失", "結束", "沒有我",
        "撐不下去", "走不下去", "解脫", "永遠睡著"
    ]

    def __init__(self, history_window: int = 14):
        self.vectorizer = TfidfVectorizer(max_features=5000, ngram_range=(1, 3))
        self.model = LogisticRegression(class_weight='balanced')
        self.history_window = history_window

    def _keyword_alert(self, text: str) -> bool:
        """高優先級關鍵詞快速匹配，觸發即時警報"""
        return any(pattern in text for pattern in self._CRISIS_PATTERNS)

    def _mood_trajectory(self, history: list[MoodEntry]) -> dict:
        """分析情緒軌跡的趨勢與變異性"""
        scores = [entry.score for entry in history[-self.history_window:]]
        if len(scores) < 3:
            return {"trend": "insufficient_data", "slope": 0.0}
        x = np.arange(len(scores))
        slope = np.polyfit(x, scores, 1)[0]
        volatility = np.std(scores)
        return {
            "trend": "declining" if slope < < -0.3 else "stable" if abs(slope) <= 0.3 else "improving",
            "slope": slope,
            "volatility": volatility
        }

    def assess_risk(self, current: MoodEntry, history: list[MoodEntry]) -> tuple[RiskLevel, dict]:
        """綜合評估當前風險等級與處置建議"""
        trajectory = self._mood_trajectory(history)
        keyword_hit = self._keyword_alert(current.text)

        # 危機關鍵詞命中 → 直接升級至 CRITICAL
        if keyword_hit:
            return RiskLevel.CRITICAL, {
                "reason": "crisis keyword detected",
                "action": "immediate human intervention",
                "trajectory": trajectory
            }

        risk_score = 0.0
        if current.score <= 2: risk_score += 2.5
        elif current.score <= 4: risk_score += 1.2
        elif current.score <= 6: risk_score += 0.4

        if trajectory["trend"] == "declining": risk_score += 1.0
        if trajectory.get("volatility", 0) > 2.5: risk_score += 0.8

        if risk_score >= 3.0: level = RiskLevel.HIGH
        elif risk_score >= 1.5: level = RiskLevel.MODERATE
        else: level = RiskLevel.LOW

        actions = {
            RiskLevel.HIGH: "notify counselor within 1 hour",
            RiskLevel.MODERATE: "suggest peer support + mood exercise",
            RiskLevel.LOW: "continue routine mood tracking",
        }
        return level, {"risk_score": risk_score, "action": actions[level], "trajectory": trajectory}

# ---- 使用範例 ----
classifier = MentalHealthRiskClassifier(history_window=14)
recent_history = [
    MoodEntry(score=7, text="今天還不錯，和朋友一起吃了午飯", timestamp=0),
    MoodEntry(score=5, text="有點累，但還可以", timestamp=1),
    MoodEntry(score=3, text="覺得自己好沒用", timestamp=2),
]
level, detail = classifier.assess_risk(
    MoodEntry(score=2, text="我不知道還能撐多久", timestamp=3),
    recent_history
)
print(f"風險等級: {level.value.upper()} | 處置: {detail['action']}")