app.py

import streamlit as st
import pandas as pd
import numpy as np
import requests
import time
from collections import defaultdict

# Set page layout to wide mode and set page title
st.set_page_config(layout="wide", page_title="影城效率与内容分析工具")


# --- Efficiency Analysis Functions ---
def clean_movie_title(title):
    if not isinstance(title, str):
        return title
    return title.split(' ', 1)[0]


# --- UPDATED: Styling function for the first two tables ---
def style_efficiency(row):
    """Applies row styling based on efficiency metrics for analysis tables."""
    green = 'background-color: #E6F5E6;'  # Light Green
    red = 'background-color: #FFE5E5;'  # Light Red

    seat_efficiency = row.get('座次效率', 0)
    session_efficiency = row.get('场次效率', 0)

    if seat_efficiency > 1.5 or session_efficiency > 1.5:
        return [green] * len(row)
    if seat_efficiency < 0.5 or session_efficiency < 0.5:
        return [red] * len(row)

    return [''] * len(row)


# --- NEW: Styling function for the summary table ---
def style_summary_efficiency(row):
    """Applies row styling based on efficiency metrics for the summary table."""
    green = 'background-color: #E6F5E6;'  # Light Green
    red = 'background-color: #FFE5E5;'  # Light Red

    # Check all four efficiency columns in the summary table
    full_day_seat_eff = row.get('全部座次效率', 0)
    full_day_session_eff = row.get('全部场次效率', 0)
    prime_seat_eff = row.get('黄金时段座次效率', 0)
    prime_session_eff = row.get('黄金时段场次效率', 0)

    # Green condition: if any efficiency is high
    if (full_day_seat_eff > 1.5 or full_day_session_eff > 1.5 or
            prime_seat_eff > 1.5 or prime_session_eff > 1.5):
        return [green] * len(row)

    # Red condition: if any efficiency is low
    if (full_day_seat_eff < 0.5 or full_day_session_eff < 0.5 or
            prime_seat_eff < 0.5 or prime_session_eff < 0.5):
        return [red] * len(row)

    return [''] * len(row)


def process_and_analyze_data(df):
    if df.empty:
        return pd.DataFrame()
    analysis_df = df.groupby('影片名称_清理后').agg(
        座位数=('座位数', 'sum'),
        场次=('影片名称_清理后', 'size'),
        票房=('总收入', 'sum'),
        人次=('总人次', 'sum')
    ).reset_index()
    analysis_df.rename(columns={'影片名称_清理后': '影片'}, inplace=True)
    analysis_df = analysis_df.sort_values(by='票房', ascending=False).reset_index(drop=True)
    total_seats = analysis_df['座位数'].sum()
    total_sessions = analysis_df['场次'].sum()
    total_revenue = analysis_df['票房'].sum()
    analysis_df['均价'] = np.divide(analysis_df['票房'], analysis_df['人次']).fillna(0)
    analysis_df['座次比'] = np.divide(analysis_df['座位数'], total_seats).fillna(0)
    analysis_df['场次比'] = np.divide(analysis_df['场次'], total_sessions).fillna(0)
    analysis_df['票房比'] = np.divide(analysis_df['票房'], total_revenue).fillna(0)
    analysis_df['座次效率'] = np.divide(analysis_df['票房比'], analysis_df['座次比']).fillna(0)
    analysis_df['场次效率'] = np.divide(analysis_df['票房比'], analysis_df['场次比']).fillna(0)
    final_columns = ['影片', '座位数', '场次', '票房', '人次', '均价', '座次比', '场次比', '票房比', '座次效率',
                     '场次效率']
    analysis_df = analysis_df[final_columns]
    return analysis_df


# --- New Feature: Server Movie Content Inquiry ---
# @st.cache_data(show_spinner=False)
def fetch_and_process_server_movies(priority_movie_titles=None):
    if priority_movie_titles is None:
        priority_movie_titles = []

    # 1. Get Token
    token_headers = {
        'Host': 'oa.hengdianfilm.com:7080', 'Content-Type': 'application/json',
        'Origin': 'http://115.239.253.233:7080', 'Connection': 'keep-alive',
        'Accept': 'application/json, text/javascript, */*; q=0.01',
        'User-Agent': 'Mozilla/5.0 (iPhone; CPU iPhone OS 18_5_0 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) CriOS/138.0.7204.156 Mobile/15E148 Safari/604.1',
        'Accept-Language': 'zh-CN,zh-Hans;q=0.9',
    }
    token_json_data = {'appId': 'hd', 'appSecret': 'ad761f8578cc6170', 'timeStamp': int(time.time() * 1000)}
    token_url = 'http://oa.hengdianfilm.com:7080/cinema-api/admin/generateToken?token=hd&murl=?token=hd&murl=ticket=-1495916529737643774'
    response = requests.post(token_url, headers=token_headers, json=token_json_data, timeout=10)
    response.raise_for_status()
    token_data = response.json()
    if token_data.get('error_code') != '0000':
        raise Exception(f"获取Token失败: {token_data.get('error_desc')}")
    auth_token = token_data['param']

    # 2. Fetch movie list (with pagination and delay)
    all_movies = []
    page_index = 1
    while True:
        list_headers = {
            'Accept': 'application/json, text/javascript, */*; q=0.01',
            'Content-Type': 'application/json; charset=UTF-8',
            'Origin': 'http://115.239.253.233:7080', 'Proxy-Connection': 'keep-alive', 'Token': auth_token,
            'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/138.0.0.0 Safari/537.36',
            'X-SESSIONID': 'PQ0J3K85GJEDVYIGZE1KEG1K80USDAP4',
        }
        list_params = {'token': 'hd', 'murl': 'ContentMovie'}
        list_json_data = {'THEATER_ID': 38205954, 'SOURCE': 'SERVER', 'ASSERT_TYPE': 2, 'PAGE_CAPACITY': 20,
                          'PAGE_INDEX': page_index}
        list_url = 'http://oa.hengdianfilm.com:7080/cinema-api/cinema/server/dcp/list'
        response = requests.post(list_url, params=list_params, headers=list_headers, json=list_json_data, verify=False)
        response.raise_for_status()
        movie_data = response.json()
        if movie_data.get("RSPCD") != "000000":
            raise Exception(f"获取影片列表失败: {movie_data.get('RSPMSG')}")
        body = movie_data.get("BODY", {})
        movies_on_page = body.get("LIST", [])
        if not movies_on_page: break
        all_movies.extend(movies_on_page)
        if len(all_movies) >= body.get("COUNT", 0): break
        page_index += 1
        time.sleep(1)  # Add 1-second delay between requests

    # 3. Process data into a central, detailed structure
    movie_details = {}
    for movie in all_movies:
        content_name = movie.get('CONTENT_NAME')
        if not content_name: continue
        movie_details[content_name] = {
            'assert_name': movie.get('ASSERT_NAME'),
            'halls': sorted([h.get('HALL_NAME') for h in movie.get('HALL_INFO', [])]),
            'play_time': movie.get('PLAY_TIME')
        }

    # 4. Prepare data for the two display views
    by_hall = defaultdict(list)
    for content_name, details in movie_details.items():
        for hall_name in details['halls']:
            by_hall[hall_name].append({'content_name': content_name, 'details': details})

    for hall_name in by_hall:
        by_hall[hall_name].sort(key=lambda item: (
            item['details']['assert_name'] is None or item['details']['assert_name'] == '',
            item['details']['assert_name'] or item['content_name']
        ))

    view2_list = []
    for content_name, details in movie_details.items():
        if details.get('assert_name'):
            view2_list.append({
                'assert_name': details['assert_name'],
                'content_name': content_name,
                'halls': details['halls'],
                'play_time': details['play_time']
            })

    priority_list = [item for item in view2_list if
                     any(p_title in item['assert_name'] for p_title in priority_movie_titles)]
    other_list_items = [item for item in view2_list if item not in priority_list]

    priority_list.sort(key=lambda x: x['assert_name'])
    other_list_items.sort(key=lambda x: x['assert_name'])

    final_sorted_list = priority_list + other_list_items

    return dict(sorted(by_hall.items())), final_sorted_list


def get_circled_number(hall_name):
    mapping = {'1': '①', '2': '②', '3': '③', '4': '④', '5': '⑤', '6': '⑥', '7': '⑦', '8': '⑧', '9': '⑨'}
    num_str = ''.join(filter(str.isdigit, hall_name))
    return mapping.get(num_str, '')


def format_play_time(time_str):
    if not time_str or not isinstance(time_str, str): return None
    try:
        parts = time_str.split(':');
        hours = int(parts[0]);
        minutes = int(parts[1])
        return hours * 60 + minutes
    except (ValueError, IndexError):
        return None


# --- UPDATED Helper function to add TMS location column ---
def add_tms_locations_to_analysis(analysis_df, tms_movie_list):
    locations = []
    for index, row in analysis_df.iterrows():
        movie_title = row['影片']
        found_versions = []
        for tms_movie in tms_movie_list:
            if tms_movie['assert_name'].startswith(movie_title):
                version_name = tms_movie['assert_name'].replace(movie_title, '').strip()
                circled_halls = " ".join(sorted([get_circled_number(h) for h in tms_movie['halls']]))
                if version_name:
                    found_versions.append(f"{version_name}：{circled_halls}")
                else:
                    found_versions.append(circled_halls)
        locations.append('｜'.join(found_versions))
    analysis_df['影片所在影厅位置'] = locations
    return analysis_df


# --- Streamlit Main UI ---
st.title('影城排片效率与内容分析工具')
st.write("上传 `影片映出日累计报表.xlsx` 进行效率分析，或点击下方按钮查询 TMS 服务器影片内容。")

uploaded_file = st.file_uploader("请在此处上传 Excel 文件", type=['xlsx', 'xls', 'csv'])
query_tms_for_location = st.checkbox("查询 TMS 找影片所在影厅")

if uploaded_file is not None:
    try:
        df = pd.read_excel(uploaded_file, skiprows=3, header=None)
        df.rename(columns={0: '影片名称', 2: '放映时间', 5: '总人次', 6: '总收入', 7: '座位数'}, inplace=True)
        required_cols = ['影片名称', '放映时间', '座位数', '总收入', '总人次']
        df = df[required_cols]
        df.dropna(subset=['影片名称', '放映时间'], inplace=True)
        for col in ['座位数', '总收入', '总人次']:
            df[col] = pd.to_numeric(df[col], errors='coerce').fillna(0)
        df['放映时间'] = pd.to_datetime(df['放映时间'], format='%H:%M:%S', errors='coerce').dt.time
        df.dropna(subset=['放映时间'], inplace=True)
        df['影片名称_清理后'] = df['影片名称'].apply(clean_movie_title)
        st.toast("文件上传成功，效率分析已生成！", icon="🎉")
        format_config = {'座位数': '{:,.0f}', '场次': '{:,.0f}', '人次': '{:,.0f}', '票房': '{:,.2f}', '均价': '{:.2f}',
                         '座次比': '{:.2%}', '场次比': '{:.2%}', '票房比': '{:.2%}', '座次效率': '{:.2f}',
                         '场次效率': '{:.2f}'}

        full_day_analysis = process_and_analyze_data(df.copy())
        prime_time_analysis = process_and_analyze_data(
            df[df['放映时间'].between(pd.to_datetime('14:00:00').time(), pd.to_datetime('21:00:00').time())].copy())

        if query_tms_for_location:
            with st.spinner("正在关联查询 TMS 服务器..."):
                _, tms_movie_list = fetch_and_process_server_movies()
                full_day_analysis = add_tms_locations_to_analysis(full_day_analysis, tms_movie_list)
                prime_time_analysis = add_tms_locations_to_analysis(prime_time_analysis, tms_movie_list)
                if '影片所在影厅位置' in full_day_analysis.columns:
                    cols_full = full_day_analysis.columns.tolist()
                    cols_full.insert(1, cols_full.pop(cols_full.index('影片所在影厅位置')))
                    full_day_analysis = full_day_analysis[cols_full]
                if '影片所在影厅位置' in prime_time_analysis.columns:
                    cols_prime = prime_time_analysis.columns.tolist()
                    cols_prime.insert(1, cols_prime.pop(cols_prime.index('影片所在影厅位置')))
                    prime_time_analysis = prime_time_analysis[cols_prime]
                st.toast("TMS 影片位置关联成功！", icon="🔗")

        st.markdown("### 全天排片效率分析")
        if not full_day_analysis.empty:
            st.dataframe(
                full_day_analysis.style.format(format_config).apply(style_efficiency, axis=1),  # MODIFIED
                use_container_width=True, hide_index=True)

        st.markdown("#### 黄金时段排片效率分析 (14:00-21:00)")
        if not prime_time_analysis.empty:
            st.dataframe(
                prime_time_analysis.style.format(format_config).apply(style_efficiency, axis=1),  # MODIFIED
                use_container_width=True, hide_index=True)

        # --- Summary Table Creation ---
        if not full_day_analysis.empty:
            st.markdown("### 排片效率汇总")
            # Select and rename columns from full day analysis
            full_day_summary = full_day_analysis.rename(columns={
                '场次': '全部场次',
                '座次效率': '全部座次效率',
                '场次效率': '全部场次效率'
            })
            # Define columns to keep, including the optional TMS location
            full_day_cols_to_keep = ['影片', '票房', '全部场次', '全部座次效率', '全部场次效率']
            if '影片所在影厅位置' in full_day_summary.columns:
                full_day_cols_to_keep.insert(1, '影片所在影厅位置')
            full_day_summary = full_day_summary[full_day_cols_to_keep]

            # Select and rename columns from prime time analysis
            prime_time_summary = prime_time_analysis.rename(columns={
                '场次': '黄金时段场次',
                '座次效率': '黄金时段座次效率',
                '场次效率': '黄金时段场次效率'
            })[['影片', '黄金时段场次', '黄金时段座次效率', '黄金时段场次效率']]

            # Merge the two dataframes
            summary_df = pd.merge(full_day_summary, prime_time_summary, on='影片', how='left')

            # Fill NaN values for movies that have no prime time sessions
            summary_df.fillna(0, inplace=True)

            # Ensure data types are correct after filling NaNs
            summary_df['黄金时段场次'] = summary_df['黄金时段场次'].astype(int)

            # Define format for the summary table
            summary_format_config = {
                '票房': '{:,.2f}',
                '全部场次': '{:,.0f}',
                '黄金时段场次': '{:,.0f}',
                '全部座次效率': '{:.2f}',
                '全部场次效率': '{:.2f}',
                '黄金时段座次效率': '{:.2f}',
                '黄金时段场次效率': '{:.2f}'
            }

            st.dataframe(
                summary_df.style.format(summary_format_config).apply(style_summary_efficiency, axis=1),  # MODIFIED
                use_container_width=True,
                hide_index=True
            )

        if not full_day_analysis.empty:
            st.markdown("##### 复制当日排片列表")
            movie_titles = full_day_analysis['影片'].tolist()
            formatted_titles = ''.join([f'《{title}》' for title in movie_titles])
            st.code(formatted_titles, language='text')

    except Exception as e:
        st.error(f"处理文件时出错: {e}")

st.divider()
st.markdown("### TMS 服务器影片内容查询")
if st.button('点击查询 TMS 服务器'):
    with st.spinner("正在从 TMS 服务器获取数据中..."):
        try:
            halls_data, movie_list_sorted = fetch_and_process_server_movies()
            st.toast("TMS 服务器数据获取成功！", icon="🎉")

            st.markdown("#### 按影片查看所在影厅")
            view2_data = [{'影片名称': item['assert_name'],
                           '所在影厅': " ".join(sorted([get_circled_number(h) for h in item['halls']])),
                           '文件名': item['content_name'], '时长': format_play_time(item['play_time'])} for item in
                          movie_list_sorted]
            df_view2 = pd.DataFrame(view2_data)
            st.dataframe(df_view2, hide_index=True, use_container_width=True)

            st.markdown("#### 按影厅查看影片内容")
            hall_tabs = st.tabs(halls_data.keys())
            for tab, hall_name in zip(hall_tabs, halls_data.keys()):
                with tab:
                    view1_data_for_tab = [{'影片名称': item['details']['assert_name'], '所在影厅': " ".join(
                        sorted([get_circled_number(h) for h in item['details']['halls']])),
                                           '文件名': item['content_name'],
                                           '时长': format_play_time(item['details']['play_time'])} for item in
                                          halls_data[hall_name]]
                    df_view1_tab = pd.DataFrame(view1_data_for_tab)
                    st.dataframe(df_view1_tab, hide_index=True, use_container_width=True)

        except Exception as e:
            st.error(f"查询服务器时出错: {e}")