Update app.py

app.py

@@ -1,286 +1,75 @@
 import streamlit as st
 import pandas as pd
-import numpy as np
-import requests
-import time
-from collections import defaultdict
+import re
 
-# 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")
+st.set_page_config(layout="wide")
 
+st.title('影片放映时间表分析')
 
-# --- Efficiency Analysis Functions ---
-def clean_movie_title(title):
-    if not isinstance(title, str):
-        return title
-    return title.split(' ', 1)[0]
+# 1. 文件上传组件
+uploaded_file = st.file_uploader("上传“影片放映时间表.xlsx”文件", type=['xlsx'])
+ad_duration = st.number_input('输入每个广告的时长（分钟）', min_value=0, value=9)
 
-
-def style_efficiency(row):
-    green = 'background-color: #E6F5E6;'  # Light Green
-    red = 'background-color: #FFE5E5;'    # Light Red
-    default = ''
-    styles = [default] * len(row)
-    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:
-        styles = [green] * len(row)
-    elif seat_efficiency < 0.5 or session_efficiency < 0.5:
-        styles = [red] * len(row)
-    return styles
-
-
-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
+if uploaded_file is not None:
     try:
-        parts = time_str.split(':'); hours = int(parts[0]); minutes = int(parts[1])
-        return hours * 60 + minutes
-    except (ValueError, IndexError):
-        return None
-
+        # 读取Excel文件
+        df = pd.read_excel(uploaded_file, header=3)
 
-# --- 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:
-            # FIX 3: Change matching from 'in' to 'startswith'
-            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']]))
-                
-                # FIX 2: Handle empty version name to remove colon
-                if version_name:
-                    found_versions.append(f"{version_name}：{circled_halls}")
-                else:
-                    found_versions.append(circled_halls)
-        
-        locations.append('｜'.join(found_versions))
+        # --- 错误修复 ---
+        # 明确将“影片”列转换为字符串类型，以避免混合类型错误
+        df['影片'] = df['影片'].astype(str)
 
-    analysis_df['影片所在影厅位置'] = locations
-    return analysis_df
+        st.subheader('上传的原始数据')
+        st.dataframe(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")
+        # 2. 数据处理和清洗
+        # 清洗“影厅”列
+        def clean_hall_name(name):
+            if isinstance(name, str):
+                match = re.search(r'【(\d+)号', name)
+                if match:
+                    return f"{match.group(1)}号厅"
+            return name
 
 
-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}'}
+        df['影厅'] = df['影厅'].apply(clean_hall_name)
 
-        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)
-                
-                # FIX 1: Reorder columns
-                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]
+        # 将“放映日期”转换为日期时间对象
+        df['放映日期'] = pd.to_datetime(df['放映日期'])
+        df['日期'] = df['放映日期'].dt.strftime('%m月%d日')
 
-                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]
+        # 删除在“影厅”或“片长”列中缺少数据的行
+        df.dropna(subset=['影厅', '片长'], inplace=True)
 
-                st.toast("TMS 影片位置关联成功！", icon="🔗")
+        # 3. 统计每天每个影厅的影片数量和播放时长
+        summary = df.groupby(['日期', '影厅']).agg(
+            影片数量=('影片', 'count'),
+            影片播放时长=('片长', 'sum')
+        ).reset_index()
 
+        # 计算广告时长
+        summary['广告时长'] = summary['影片数量'] * ad_duration
 
-        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),
-                use_container_width=True, hide_index=True)
+        # 4. 创建数据透视表以进行最终显示
+        pivot_table = summary.pivot_table(
+            index='日期',
+            columns='影厅',
+            values=['广告时长', '影片播放时长']
+        )
 
-        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),
-                use_container_width=True, hide_index=True)
+        # 将所有空白（NaN）值填充为 0
+        pivot_table = pivot_table.fillna(0)
 
-        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}")
+        # 将数值转换为整数，使表格更整洁
+        pivot_table = pivot_table.astype(int)
 
+        # 交换列的层级顺序并排序，以获得所需的输出格式
+        if not pivot_table.empty:
+            pivot_table = pivot_table.swaplevel(0, 1, axis=1).sort_index(axis=1)
 
-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��成功！", icon="🎉")
+        st.subheader('影厅播放统计')
+        st.dataframe(pivot_table)
 
-            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}")
-            
+    except Exception as e:
+        st.error(f"处理文件时出错: {e}")