Update app.py

app.py

@@ -1,55 +1,38 @@
 import streamlit as st
 import pandas as pd
 import numpy as np
+import requests
+import time
+from collections import defaultdict
 
-# 设置页面布局为宽屏模式，并设置页面标题
-st.set_page_config(layout="wide", page_title="影城排片效率分析")
+# 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):
-    """
-    清理并规范化电影标题。
-    根据用户最新指示：只保留字符串中第一个空格之前的部分。
-    """
     if not isinstance(title, str):
         return title
-    # 将标题按第一个空格分割，并只取第一部分
     return title.split(' ', 1)[0]
 
 
 def style_efficiency(row):
-    """
-    根据效率值高亮特定行。
-    高于 1.5 的淡绿色，低于 0.5 的淡红色。
-    """
-    # 定义颜色
-    green = 'background-color: #E6F5E6;'  # 淡绿色
-    red = 'background-color: #FFE5E5;'  # 淡红色
+    green = 'background-color: #E6F5E6;'  # Light Green
+    red = 'background-color: #FFE5E5;'  # Light Red
     default = ''
-
-    # 初始化样式列表，长度与行内元素数量一致
     styles = [default] * len(row)
-
-    # 获取效率值
     seat_efficiency = row.get('座次效率', 0)
     session_efficiency = row.get('场次效率', 0)
-
-    # 判断并应用样式
     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):
-    """
-    核心数据处理与分析函数。
-    """
     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'),
@@ -57,112 +40,207 @@ def process_and_analyze_data(df):
         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'
-    ]
+    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
 
 
-# --- Streamlit 用户界面 ---
+# --- 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', [])])
+        }
+
+    # 4. Prepare data for the two display views
+    # For View by Hall
+    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']
+        ))
+
+    # For View by Movie
+    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']
+            })
+
+    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
+
 
-st.title('影城排片效率分析工具')
-st.write("上传 `影片映出日累计报表.xlsx` 文件。")
+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, '')
+
+
+# --- 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'])
+full_day_analysis = pd.DataFrame()
 
 if uploaded_file is not None:
     try:
+        # Efficiency analysis part
         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)
-
+        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}'}
 
-        st.toast("文件上传成功，数据已按规则处理！", 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}',
-        }
-
-        # --- 1. 全天数据分析 ---
-        st.header("全天排片效率分析")
-
+        st.markdown("### 全天排片效率分析")
         full_day_analysis = process_and_analyze_data(df.copy())
-
         if not full_day_analysis.empty:
             table_height = (len(full_day_analysis) + 1) * 35 + 3
             st.dataframe(
                 full_day_analysis.style.format(format_config).apply(style_efficiency, axis=1).hide(axis="index"),
-                height=table_height,
-                use_container_width=True,
-                hide_index=True
-            )
-        else:
-            st.warning("全天数据不足，无法生成分析报告。")
-
-        # --- 2. 黄金时段数据分析 ---
-        st.header("黄金时段排片效率分析 (14:00-21:00)")
-
-        start_time = pd.to_datetime('14:00:00').time()
-        end_time = pd.to_datetime('21:00:00').time()
-        prime_time_df = df[df['放映时间'].between(start_time, end_time)]
+                height=table_height, use_container_width=True)
 
+        st.markdown("#### 黄金时段排片效率分析 (14:00-21:00)")
+        start_time, end_time = pd.to_datetime('14:00:00').time(), pd.to_datetime('21:00:00').time()
+        prime_time_df = df[df['放映时间'].between(start_time, end_time)]
         prime_time_analysis = process_and_analyze_data(prime_time_df.copy())
-
         if not prime_time_analysis.empty:
             table_height_prime = (len(prime_time_analysis) + 1) * 35 + 3
             st.dataframe(
                 prime_time_analysis.style.format(format_config).apply(style_efficiency, axis=1).hide(axis="index"),
-                height=table_height_prime,
-                use_container_width=True,
-                hide_index = True
-            )
-        else:
-            st.warning("黄金时段内没有有效场次数据，无法生成分析报告。")
-
-        # --- 3. 一键复制影片列表 ---
-        if not full_day_analysis.empty:
-            st.header("复制当日影片列表")
+                height=table_height_prime, use_container_width=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.warning("请确保上传的文件是'影片映出日累计报表.xlsx'，并且格式正确。")
 
-else:
-    st.info("请上传文件以开始分析。")
+
+# --- New Feature Module ---
+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，请稍候..."):
+        try:
+            priority_titles = full_day_analysis['影片'].tolist() if not full_day_analysis.empty else []
+            halls_data, movie_list_sorted = fetch_and_process_server_movies(priority_titles)
+            st.toast("TMS 服务器数据获取成功！", icon="🎉")
+
+            # --- View by Movie (in a single expander) ---
+            st.markdown("#### 按影片查看所在影厅")
+            with st.expander("点击展开 / 折叠影片列表", expanded = True):
+                for item in movie_list_sorted:
+                    circled_halls = " ".join(sorted([get_circled_number(h) for h in item['halls']]))
+                    st.markdown(f"**{item['assert_name']}** - {circled_halls} - `{item['content_name']}`")
+
+            # --- View by Hall ---
+            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:
+                    for movie_item in halls_data[hall_name]:
+                        details = movie_item['details']
+                        content_name = movie_item['content_name']
+                        assert_name = details['assert_name']
+
+                        display_name = assert_name if assert_name else content_name
+                        circled_halls = " ".join(sorted([get_circled_number(h) for h in details['halls']]))
+
+                        st.markdown(f"- **{display_name}** - {circled_halls} - `{content_name}`")
+
+        except Exception as e:
+            st.error(f"查询服务器时出错: {e}")