"""
修复 AI 标签映射：ground-truth ep编号 ≠ Excel 播出期号
用标题模糊匹配重新关联正确的 AI 标签
"""

import json
import sys
import os
from difflib import SequenceMatcher

sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'backend'))

from app.db.session import get_session
from app.models.episode import Episode
from app.models.user import User
from sqlmodel import select

project_root = os.path.join(os.path.dirname(__file__), '..')

# 读 ground-truth
with open(os.path.join(project_root, 'ai-labeling', 'benchmark-set', 'ground-truth.json'), 'r', encoding='utf-8') as f:
    gt_data = json.load(f)

gt_episodes = gt_data['episodes']

# 读数据库
session = next(get_session())
db_episodes = session.exec(select(Episode).order_by(Episode.episode_number)).all()

print(f"DB episodes: {len(db_episodes)}")
print(f"GT episodes: {len(gt_episodes)}")
print()

# ── 标题相似度匹配 ──
def title_similarity(a, b):
    """计算两个标题的相似度"""
    # 清理标题
    clean_a = a.replace('"', '').replace('"', '').replace("'", '').replace('——', '').replace('：', '').replace(' ', '').replace('《', '').replace('》', '')
    clean_b = b.replace('"', '').replace('"', '').replace("'", '').replace('——', '').replace('：', '').replace(' ', '').replace('《', '').replace('》', '')
    return SequenceMatcher(None, clean_a, clean_b).ratio()

# 对每个 DB episode，找最佳匹配的 GT episode
matched = []
used_gt = set()

for db_ep in db_episodes:
    best_score = 0
    best_gt = None

    for i, gt_ep in enumerate(gt_episodes):
        if i in used_gt:
            continue

        gt_title = gt_ep.get('title', '')
        score = title_similarity(db_ep.program_name, gt_title)

        # 也检查份额是否匹配（辅助判断）
        gt_share = gt_ep.get('share')
        share_match = (gt_share is not None and db_ep.audience_share is not None
                      and abs(float(gt_share) - float(db_ep.audience_share)) < 0.01)

        # 份额匹配加分
        if share_match:
            score += 0.3

        if score > best_score:
            best_score = score
            best_gt = (i, gt_ep)

    if best_gt and best_score > 0.3:
        used_gt.add(best_gt[0])
        matched.append((db_ep, best_gt[1], best_score))
        status = 'OK' if best_score > 0.5 else 'WEAK'
        print(f"[{status}] DB ep{db_ep.episode_number:02d} \"{db_ep.program_name[:15]}\" -> GT ep{best_gt[1]['ep']:02d} \"{best_gt[1]['title'][:15]}\" (score={best_score:.2f})")
    else:
        print(f"[MISS] DB ep{db_ep.episode_number:02d} \"{db_ep.program_name[:15]}\" -> NO MATCH")
        matched.append((db_ep, None, 0))

print(f"\nMatched: {sum(1 for _,gt,_ in matched if gt is not None)}/{len(db_episodes)}")

# ── 确认后更新 ──
print("\n--- Updating AI labels ---")
updated = 0
for db_ep, gt_ep, score in matched:
    if gt_ep is None:
        continue

    db_ep.program_format = gt_ep.get('program_format')
    db_ep.equipment_domain = gt_ep.get('equipment_domain')
    db_ep.scene_tags = gt_ep.get('scene_tags')
    db_ep.tech_tags = gt_ep.get('tech_tags')
    db_ep.narrative_structure = gt_ep.get('narrative_structure')
    db_ep.opening_hook = gt_ep.get('opening_hook')
    db_ep.ai_label_confidence = 'reviewed'

    session.add(db_ep)
    updated += 1

session.commit()
print(f"Updated {updated} episodes")

# ── 验证 ──
print("\n--- Verification ---")
db_episodes = session.exec(select(Episode).order_by(Episode.episode_number)).all()
for ep in db_episodes:
    print(f"ep{ep.episode_number:02d} | {ep.program_name[:18]:18s} | {ep.program_format or '-':8s} | {ep.narrative_structure or '-':6s} | {ep.opening_hook or '-'}")