132 lines
5.3 KiB
Python
132 lines
5.3 KiB
Python
from flask import Blueprint, render_template, request, redirect, url_for, flash, session
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from models import Movie, Genre, Rating, UserDefault, Occupation, User # 导入所需的模型
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from sqlalchemy import func
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from helpers import get_recommendations
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# 创建一个蓝图
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movies_bp = Blueprint('movies', __name__)
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# 电影详情路由
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@movies_bp.route('/detail')
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def detail():
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# 检查用户是否登录
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if 'user_id' not in session:
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flash("Please log in to access this page.")
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return redirect(url_for('auth'))
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# 获取电影 ID 参数
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movie_id = request.args.get('id')
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if not movie_id:
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return redirect(url_for('dashboard'))
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# 查询电影详情
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movie = Movie.query.filter_by(id=movie_id).first()
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if not movie:
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return redirect(url_for('dashboard'))
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# 解析 genres 字段，查询对应的 genre 名称
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genre_ids = movie.genres.split(",") if movie.genres else []
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genres = Genre.query.filter(Genre.id.in_(genre_ids)).all()
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genre_names = [genre.name for genre in genres]
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# 获取评分分布
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rating_counts = (
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Rating.query.with_entities(Rating.rating, func.count(Rating.rating).label('count'))
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.filter_by(item_id=movie_id)
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.group_by(Rating.rating)
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.all()
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)
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total_ratings = sum(count for _, count in rating_counts)
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rating_distribution = {i: 0 for i in range(1, 6)} # 初始化 1-5 分的评分比例为 0
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for rating, count in rating_counts:
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rating_distribution[rating] = count / total_ratings if total_ratings else 0
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# 获取当前用户类型
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user_id = session.get('user_id')
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user = User.query.filter_by(id=user_id).first()
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user_type = user.user_type if user else 'U' # 默认为普通用户
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# 如果是制片人用户，进行用户喜好详细分析
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user_analysis = None
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if user_type == 'P':
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# 性别分析
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gender_stats = (
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Rating.query.join(UserDefault, Rating.user_id == UserDefault.user_id)
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.filter(Rating.item_id == movie_id)
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.with_entities(UserDefault.gender, func.avg(Rating.rating).label('average'), func.count(Rating.rating).label('count'))
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.group_by(UserDefault.gender)
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.all()
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)
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gender_analysis = {gender: {'average': avg, 'count': cnt} for gender, avg, cnt in gender_stats}
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# 年龄分析
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age_ranges = [
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(0, 10), (11, 20), (21, 30), (31, 40),
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(41, 50), (51, 60), (61, 70), (71, 80)
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]
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age_analysis = {}
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for min_age, max_age in age_ranges:
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age_data = (
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Rating.query.join(UserDefault, Rating.user_id == UserDefault.user_id)
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.filter(Rating.item_id == movie_id, UserDefault.age.between(min_age, max_age))
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.with_entities(func.avg(Rating.rating).label('average'), func.count(Rating.rating).label('count'))
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.first()
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)
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age_analysis[f"{min_age}-{max_age}"] = {
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'average': age_data.average if age_data and age_data.count else 0,
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'count': age_data.count if age_data else 0
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}
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# 职业分析
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occupation_stats = (
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Rating.query.join(UserDefault, Rating.user_id == UserDefault.user_id)
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.filter(Rating.item_id == movie_id)
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.with_entities(UserDefault.occupation, func.avg(Rating.rating).label('average'), func.count(Rating.rating).label('count'))
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.group_by(UserDefault.occupation)
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.all()
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)
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occupation_analysis = {}
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for occ_id, avg, cnt in occupation_stats:
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occupation_name = Occupation.query.filter_by(id=occ_id).first().name
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occupation_analysis[occupation_name] = {'average': avg, 'count': cnt}
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user_analysis = {
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'gender_analysis': gender_analysis,
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'age_analysis': age_analysis,
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'occupation_analysis': occupation_analysis
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}
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# 获取相似电影并转换每部相似电影的 genres 为名称
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similar_movie_ids = [int(mid) for mid in movie.similar_movie.split(',')] if movie.similar_movie else []
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similar_movies = Movie.query.filter(Movie.id.in_(similar_movie_ids)).all()
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similar_movies_data = []
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for sim_movie in similar_movies:
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sim_genre_ids = sim_movie.genres.split(",") if sim_movie.genres else []
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sim_genres = Genre.query.filter(Genre.id.in_(sim_genre_ids)).all()
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sim_genre_names = [genre.name for genre in sim_genres]
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similar_movies_data.append({
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'id': sim_movie.id,
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'title': sim_movie.title,
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'release_date': sim_movie.release_date,
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'genres': ", ".join(sim_genre_names), # 转换后的 genres 名称
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'rating_count': sim_movie.rating_count,
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'average_rating': sim_movie.average_rating,
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'actor': sim_movie.actor,
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})
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# 获取“猜你喜欢”的推荐数据
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recommendations_data = get_recommendations(user_id, page=1, per_page=10) if user_id else {}
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# 渲染电影详情模板
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return render_template(
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'detail.html',
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movie=movie,
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genre_names=genre_names,
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rating_distribution=rating_distribution,
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user_analysis=user_analysis,
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user_type=user_type,
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**recommendations_data,
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similar_movies=similar_movies_data, # 使用转换后的相似电影数据
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)
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