modules/nutrition_analyzer.py

"""
PregoPal - 营养分析与报告模块
==============================
分析饮食数据，基于中国官方营养标准输出可视化报告。

当前：基于内置 DEFAULT_NUTRITION_DB（通用知识估算）
后续：替换为中国官方标准（DRIs 2023 / 中国食物成分表）
"""

import json
import datetime
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from pathlib import Path
from config import NUTRITION_DB_FILE, DEFAULT_NUTRITION_DB, REPORTS_DIR
from utils import setup_chinese_font

# 启动时设置中文字体
_CHINESE_FONT = setup_chinese_font()


class NutritionAnalyzer:
    """分析饮食数据，输出可视化营养报告"""

    def __init__(self):
        self.nutrition_db = self._load_nutrition_db()

    def _load_nutrition_db(self):
        """加载营养数据库"""
        if NUTRITION_DB_FILE.exists():
            with open(NUTRITION_DB_FILE, 'r', encoding='utf-8') as f:
                return json.load(f)
        # 使用默认数据库并保存
        self._save_nutrition_db(DEFAULT_NUTRITION_DB)
        return DEFAULT_NUTRITION_DB

    def _save_nutrition_db(self, db):
        with open(NUTRITION_DB_FILE, 'w', encoding='utf-8') as f:
            json.dump(db, f, ensure_ascii=False, indent=2)

    def analyze_diet(self, records: list) -> dict:
        """
        分析一段时间内的饮食记录

        Args:
            records: 饮食记录列表
        Returns:
            分析结果字典
        """
        if not records:
            return {"error": "暂无饮食记录", "score": 0}

        # 统计各餐次频率
        meal_counts = {"早餐": 0, "午餐": 0, "晚餐": 0, "加餐": 0}
        food_items = []
        total_days = len(set(r["date"] for r in records))

        for r in records:
            for meal_time, food in r.get("meals", {}).items():
                if meal_time in meal_counts:
                    meal_counts[meal_time] += 1
                # 提取食物关键词
                food_items.extend(self._extract_foods(food))

        # 计算营养覆盖情况
        nutrition_coverage = self._calculate_nutrition_coverage(food_items)

        # 计算饮食多样性评分
        diversity_result = self._calculate_diversity_score(meal_counts, total_days)

        # 生成建议
        suggestions = self._generate_suggestions(nutrition_coverage, diversity_result)

        # 综合评分：多样性(50%) + 营养覆盖(50%)
        diversity_num = diversity_result.get("score", 0) if isinstance(diversity_result, dict) else 0
        covered_nutrients = sum(1 for v in nutrition_coverage.values() if v["covered"])
        total_nutrients = max(len(nutrition_coverage), 1)
        nutrition_score = int(covered_nutrients / total_nutrients * 100)
        overall_score = int(diversity_num * 0.5 + nutrition_score * 0.5)

        return {
            "score": overall_score,
            "total_days": total_days,
            "total_records": len(records),
            "meal_counts": meal_counts,
            "food_items": list(set(food_items)),
            "nutrition_coverage": nutrition_coverage,
            "diversity_score": diversity_num,
            "diversity_details": diversity_result.get("details", []),
            "suggestions": suggestions
        }

    def _extract_foods(self, food_str: str) -> list:
        """从餐食描述中提取食物名称"""
        # 简单分词提取
        separators = ['+', '、', '，', ',', '/', ' ']
        foods = [food_str]
        for sep in separators:
            expanded = []
            for f in foods:
                expanded.extend(f.split(sep))
            foods = expanded
        return [f.strip() for f in foods if f.strip()]

    def _calculate_nutrition_coverage(self, food_items: list) -> dict:
        """计算营养覆盖情况"""
        coverage = {}
        food_text = " ".join(food_items)

        for nutrient, info in self.nutrition_db.items():
            # 检查食物列表中是否包含推荐食物
            matched_foods = [f for f in info["foods"] if f in food_text]
            coverage[nutrient] = {
                "matched_foods": matched_foods,
                "covered": len(matched_foods) > 0,
                "recommended_foods": info["foods"],
                "benefit": info["benefit"],
                "daily_recommend": info.get("daily_recommend_mg") or info.get("daily_recommend_g") or info.get("daily_recommend_mcg", ""),
                "unit": "mg" if "daily_recommend_mg" in info else ("g" if "daily_recommend_g" in info else "mcg")
            }

        return coverage

    def _calculate_diversity_score(self, meal_counts: dict, total_days: int) -> dict:
        """计算饮食多样性评分"""
        if total_days == 0:
            return {"score": 0, "details": "暂无数据"}

        max_possible = total_days * len(meal_counts)
        actual = sum(meal_counts.values())
        score = min(100, int((actual / max_possible) * 100))

        details = []
        for meal, count in meal_counts.items():
            rate = count / total_days if total_days > 0 else 0
            status = "[OK]" if rate >= 0.7 else ("[!]" if rate >= 0.4 else "[!!]")
            details.append(f"{status} {meal}: {count}/{total_days}天 ({rate:.0%})")

        return {"score": score, "details": details}

    def _generate_suggestions(self, nutrition_coverage: dict, diversity: dict) -> list:
        """生成营养建议"""
        suggestions = []

        # 检查未覆盖的营养素
        missing = [n for n, info in nutrition_coverage.items() if not info["covered"]]
        if missing:
            suggestions.append(f"[!] 以下营养素摄入不足: {', '.join(missing[:5])}")
            for n in missing[:3]:
                info = nutrition_coverage[n]
                suggestions.append(f"  -> 建议补充 {n}（{info['benefit']}）：可多吃 {', '.join(info['recommended_foods'][:3])}")

        if diversity["score"] < 60:
            suggestions.append("[!] 饮食多样性不足，建议增加食物种类")
        elif diversity["score"] >= 80:
            suggestions.append("[OK] 饮食多样性良好，继续保持！")

        suggestions.append("  建议每天摄入12种以上食物，每周25种以上")
        suggestions.append("  保证每天1.5-2L饮水")

        return suggestions

    def generate_report_chart(self, analysis: dict) -> plt.Figure:
        """生成营养报告图表"""
        if "error" in analysis:
            fig, ax = plt.subplots(figsize=(8, 4))
            ax.text(0.5, 0.5, analysis["error"], ha='center', va='center', fontsize=14)
            return fig

        fig = plt.figure(figsize=(14, 10))

        # 1. 营养覆盖雷达图
        ax1 = fig.add_subplot(2, 2, 1, polar=True)
        nutrients = list(analysis["nutrition_coverage"].keys())[:8]
        coverage_values = [1 if analysis["nutrition_coverage"][n]["covered"] else 0 for n in nutrients]

        angles = np.linspace(0, 2 * np.pi, len(nutrients), endpoint=False).tolist()
        coverage_values += coverage_values[:1]
        angles += angles[:1]

        ax1.plot(angles, coverage_values, 'o-', linewidth=2, color='#FF6B9D')
        ax1.fill(angles, coverage_values, alpha=0.25, color='#FF6B9D')
        ax1.set_xticks(angles[:-1])
        ax1.set_xticklabels(nutrients, fontsize=9)
        ax1.set_ylim(0, 1.2)
        ax1.set_title('营养覆盖雷达图', pad=20, fontsize=13, fontweight='bold')

        # 2. 各餐次频率柱状图
        ax2 = fig.add_subplot(2, 2, 2)
        meals = list(analysis["meal_counts"].keys())
        counts = list(analysis["meal_counts"].values())
        colors = ['#FF9AA2', '#FFB7B2', '#FFDAC1', '#E2F0CB']
        bars = ax2.bar(meals, counts, color=colors, edgecolor='white', linewidth=1.5)
        ax2.set_title('各餐次记录频率', fontsize=13, fontweight='bold')
        ax2.set_ylabel('记录次数')
        for bar, count in zip(bars, counts):
            ax2.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.1,
                     str(count), ha='center', va='bottom', fontsize=11)

        # 3. 饮食多样性评分仪表盘
        ax3 = fig.add_subplot(2, 2, 3)
        score = analysis.get("diversity_score", 0)
        if isinstance(score, dict): score = score.get("score", 0)
        ax3.pie([score, 100 - score], startangle=90,
                colors=['#FF6B9D', '#F0F0F0'],
                wedgeprops={'width': 0.3, 'edgecolor': 'white'})
        ax3.text(0, 0, f'{score}', ha='center', va='center', fontsize=28, fontweight='bold')
        ax3.text(0, -0.15, '多样性评分', ha='center', va='center', fontsize=10, color='gray')
        ax3.set_title('饮食多样性评分', fontsize=13, fontweight='bold')

        # 4. 建议文本
        ax4 = fig.add_subplot(2, 2, 4)
        ax4.axis('off')
        suggestions = analysis.get("suggestions", [])
        if suggestions:
            text = "-- 营养建议 --\n" + "\n".join(f"* {s}" for s in suggestions[:6])
        else:
            text = "[OK] 营养状况良好！"
        ax4.text(0.05, 0.95, text, transform=ax4.transAxes,
                 fontsize=10, verticalalignment='top',
                 fontfamily='sans-serif',
                 bbox=dict(boxstyle='round,pad=0.5', facecolor='#FFF5F5', edgecolor='#FF6B9D'))

        plt.tight_layout()
        return fig

    def generate_report_text(self, analysis: dict) -> str:
        """生成文本格式的营养报告"""
        if "error" in analysis:
            return f"⚠️ {analysis['error']}"

        lines = [
            "=" * 50,
            "--- 孕期营养分析报告 ---",
            "=" * 50,
            f"分析周期: {analysis['total_days']} 天",
            f"记录总数: {analysis['total_records']} 条",
            "",
            "饮食多样性评分: {}/100".format(analysis.get('diversity_score', 0) if not isinstance(analysis.get('diversity_score'), dict) else analysis['diversity_score'].get('score', 0)),
        ]

        lines.append("")
        lines.append("各餐次记录情况:")
        for detail in analysis.get('diversity_details', []):
            lines.append(f"  {detail}")

        lines.append("")
        lines.append("营养覆盖情况:")
        for nutrient, info in analysis['nutrition_coverage'].items():
            status = "[OK]" if info['covered'] else "[!!]"
            matched = ", ".join(info['matched_foods']) if info['matched_foods'] else "无"
            lines.append(f"  {status} {nutrient}: 匹配食物 [{matched}]")
            lines.append(f"     -> {info['benefit']}")

        lines.append("")
        lines.append("改善建议:")
        for s in analysis.get("suggestions", []):
            lines.append(f"  {s}")

        lines.append("")
        lines.append("=" * 50)
        lines.append("由 PregoPal 自动生成")

        return "\n".join(lines)

    def export_report_markdown(self, analysis: dict, filename: str = None) -> Path:
        """导出营养报告为 Markdown 文件"""
        if filename is None:
            filename = f"营养报告_{datetime.date.today().isoformat()}.md"

        md_path = REPORTS_DIR / filename

        content = f"""# 孕期营养分析报告

## 基本信息
- **分析周期**: {analysis.get('total_days', 0)} 天
- **记录总数**: {analysis.get('total_records', 0)} 条
- **生成时间**: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M')}

## 饮食多样性评分
**评分: {analysis.get('diversity_score', 0) if not isinstance(analysis.get('diversity_score'), dict) else analysis['diversity_score'].get('score', 0)}/100**

| 餐次 | 记录天数 | 覆盖率 |
|------|---------|--------|
"""
        for detail in analysis.get('diversity_details', []):
            parts = detail.split(': ', 1)
            if len(parts) == 2:
                content += f"| {parts[0]} | {parts[1]} |\n"

        content += """
## 营养覆盖分析

| 营养素 | 状态 | 匹配食物 | 功效 |
|--------|------|---------|------|
"""
        for nutrient, info in analysis.get('nutrition_coverage', {}).items():
            status = "OK" if info['covered'] else "!!"
            matched = ", ".join(info['matched_foods']) if info['matched_foods'] else "-"
            content += f"| {nutrient} | {status} | {matched} | {info['benefit']} |\n"

        content += """
## 改善建议

"""
        for s in analysis.get('suggestions', []):
            content += f"- {s}\n"

        content += """
---
*由 PregoPal 自动生成 | 仅供参考，不构成医疗建议*
"""

        with open(md_path, 'w', encoding='utf-8') as f:
            f.write(content)

        return md_path