from flask import Blueprint, render_template, request, redirect, url_for, flash, session
from models import Movie, Genre, Rating, UserDefault, Occupation, User  # 导入所需的模型
from sqlalchemy import func
from helpers import get_recommendations

# 创建一个蓝图
movies_bp = Blueprint('movies', __name__)

# 电影详情路由
@movies_bp.route('/detail')
def detail():
    # 检查用户是否登录
    if 'user_id' not in session:
        flash("Please log in to access this page.")
        return redirect(url_for('auth'))

    # 获取电影 ID 参数
    movie_id = request.args.get('id')
    if not movie_id:
        return redirect(url_for('dashboard'))

    # 查询电影详情
    movie = Movie.query.filter_by(id=movie_id).first()
    if not movie:
        return redirect(url_for('dashboard'))

    # 解析 genres 字段，查询对应的 genre 名称
    genre_ids = movie.genres.split(",") if movie.genres else []
    genres = Genre.query.filter(Genre.id.in_(genre_ids)).all()
    genre_names = [genre.name for genre in genres]

    # 获取评分分布
    rating_counts = (
        Rating.query.with_entities(Rating.rating, func.count(Rating.rating).label('count'))
        .filter_by(item_id=movie_id)
        .group_by(Rating.rating)
        .all()
    )
    total_ratings = sum(count for _, count in rating_counts)
    rating_distribution = {i: 0 for i in range(1, 6)}  # 初始化 1-5 分的评分比例为 0
    for rating, count in rating_counts:
        rating_distribution[rating] = count / total_ratings if total_ratings else 0

    # 获取当前用户类型
    user_id = session.get('user_id')
    user = User.query.filter_by(id=user_id).first()
    user_type = user.user_type if user else 'U'  # 默认为普通用户

    # 如果是制片人用户，进行用户喜好详细分析
    user_analysis = None
    if user_type == 'P':
        # 性别分析
        gender_stats = (
            Rating.query.join(UserDefault, Rating.user_id == UserDefault.user_id)
            .filter(Rating.item_id == movie_id)
            .with_entities(UserDefault.gender, func.avg(Rating.rating).label('average'), func.count(Rating.rating).label('count'))
            .group_by(UserDefault.gender)
            .all()
        )
        gender_analysis = {gender: {'average': avg, 'count': cnt} for gender, avg, cnt in gender_stats}

        # 年龄分析
        age_ranges = [
            (0, 10), (11, 20), (21, 30), (31, 40),
            (41, 50), (51, 60), (61, 70), (71, 80)
        ]
        age_analysis = {}
        for min_age, max_age in age_ranges:
            age_data = (
                Rating.query.join(UserDefault, Rating.user_id == UserDefault.user_id)
                .filter(Rating.item_id == movie_id, UserDefault.age.between(min_age, max_age))
                .with_entities(func.avg(Rating.rating).label('average'), func.count(Rating.rating).label('count'))
                .first()
            )
            age_analysis[f"{min_age}-{max_age}"] = {
                'average': age_data.average if age_data and age_data.count else 0,
                'count': age_data.count if age_data else 0
            }

        # 职业分析
        occupation_stats = (
            Rating.query.join(UserDefault, Rating.user_id == UserDefault.user_id)
            .filter(Rating.item_id == movie_id)
            .with_entities(UserDefault.occupation, func.avg(Rating.rating).label('average'), func.count(Rating.rating).label('count'))
            .group_by(UserDefault.occupation)
            .all()
        )
        occupation_analysis = {}
        for occ_id, avg, cnt in occupation_stats:
            occupation_name = Occupation.query.filter_by(id=occ_id).first().name
            occupation_analysis[occupation_name] = {'average': avg, 'count': cnt}

        user_analysis = {
            'gender_analysis': gender_analysis,
            'age_analysis': age_analysis,
            'occupation_analysis': occupation_analysis
        }

    # 获取相似电影并转换每部相似电影的 genres 为名称
    similar_movie_ids = [int(mid) for mid in movie.similar_movie.split(',')] if movie.similar_movie else []
    similar_movies = Movie.query.filter(Movie.id.in_(similar_movie_ids)).all()
    similar_movies_data = []
    for sim_movie in similar_movies:
        sim_genre_ids = sim_movie.genres.split(",") if sim_movie.genres else []
        sim_genres = Genre.query.filter(Genre.id.in_(sim_genre_ids)).all()
        sim_genre_names = [genre.name for genre in sim_genres]

        similar_movies_data.append({
            'id': sim_movie.id,
            'title': sim_movie.title,
            'release_date': sim_movie.release_date,
            'genres': ", ".join(sim_genre_names),  # 转换后的 genres 名称
            'rating_count': sim_movie.rating_count,
            'average_rating': sim_movie.average_rating,
            'actor': sim_movie.actor,
        })

    # 获取“猜你喜欢”的推荐数据
    recommendations_data = get_recommendations(user_id, page=1, per_page=10) if user_id else {}

    # 渲染电影详情模板
    return render_template(
        'detail.html',
        movie=movie,
        genre_names=genre_names,
        rating_distribution=rating_distribution,
        user_analysis=user_analysis,
        user_type=user_type,
        **recommendations_data,
        similar_movies=similar_movies_data,  # 使用转换后的相似电影数据
    )