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Update app.py
Browse files
app.py
CHANGED
@@ -119,7 +119,6 @@ def fetch_and_process_server_movies(priority_movie_titles=None):
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}
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# 4. Prepare data for the two display views
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-
# For View by Hall
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by_hall = defaultdict(list)
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for content_name, details in movie_details.items():
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for hall_name in details['halls']:
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@@ -131,7 +130,6 @@ def fetch_and_process_server_movies(priority_movie_titles=None):
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item['details']['assert_name'] or item['content_name']
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))
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# For View by Movie
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view2_list = []
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for content_name, details in movie_details.items():
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if details.get('assert_name'):
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@@ -161,24 +159,46 @@ def get_circled_number(hall_name):
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def format_play_time(time_str):
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if not time_str or not isinstance(time_str, str):
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return None
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try:
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parts = time_str.split(':')
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hours = int(parts[0])
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minutes = int(parts[1])
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return hours * 60 + minutes
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except (ValueError, IndexError):
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return None
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# --- Streamlit Main UI ---
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st.title('影城排片效率与内容分析工具')
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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。")
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uploaded_file = st.file_uploader("请在此处上传 Excel 文件", type=['xlsx', 'xls', 'csv'])
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if uploaded_file is not None:
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try:
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@@ -197,23 +217,40 @@ if uploaded_file is not None:
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'座次比': '{:.2%}', '场次比': '{:.2%}', '票房比': '{:.2%}', '座次效率': '{:.2f}',
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'场次效率': '{:.2f}'}
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st.markdown("### 全天排片效率分析")
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full_day_analysis = process_and_analyze_data(df.copy())
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if not full_day_analysis.empty:
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table_height = (len(full_day_analysis) + 1) * 35 + 3
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st.dataframe(
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full_day_analysis.style.format(format_config)
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st.markdown("#### 黄金时段排片效率分析 (14:00-21:00)")
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start_time, end_time = pd.to_datetime('14:00:00').time(), pd.to_datetime('21:00:00').time()
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prime_time_df = df[df['放映时间'].between(start_time, end_time)]
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prime_time_analysis = process_and_analyze_data(prime_time_df.copy())
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if not prime_time_analysis.empty:
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table_height_prime = (len(prime_time_analysis) + 1) * 35 + 3
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st.dataframe(
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prime_time_analysis.style.format(format_config)
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if not full_day_analysis.empty:
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st.markdown("##### 复制当日排片列表")
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@@ -224,42 +261,33 @@ if uploaded_file is not None:
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except Exception as e:
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st.error(f"处理文件时出错: {e}")
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st.markdown("### TMS 服务器影片内容查询")
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if st.button('点击查询 TMS 服务器'):
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with st.spinner("正在从 TMS 服务器获取数据中..."):
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try:
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halls_data, movie_list_sorted = fetch_and_process_server_movies(priority_titles)
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st.toast("TMS 服务器数据获取成功!", icon="🎉")
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# --- View by Movie (Table Format) ---
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st.markdown("#### 按影片查看所在影厅")
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'文件名': item['content_name'],
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'时长': format_play_time(item['play_time'])
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} for item in movie_list_sorted]
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df_view2 = pd.DataFrame(view2_data)
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st.dataframe(df_view2, hide_index=True, use_container_width=True)
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# --- View by Hall (Table Format) ---
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st.markdown("#### 按影厅查看影片内容")
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hall_tabs = st.tabs(halls_data.keys())
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for tab, hall_name in zip(hall_tabs, halls_data.keys()):
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with tab:
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view1_data_for_tab = [{
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} for item in halls_data[hall_name]]
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df_view1_tab = pd.DataFrame(view1_data_for_tab)
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st.dataframe(df_view1_tab, hide_index=True, use_container_width=True)
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except Exception as e:
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st.error(f"查询服务器时出错: {e}")
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}
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# 4. Prepare data for the two display views
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by_hall = defaultdict(list)
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for content_name, details in movie_details.items():
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for hall_name in details['halls']:
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item['details']['assert_name'] or item['content_name']
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))
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view2_list = []
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for content_name, details in movie_details.items():
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if details.get('assert_name'):
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def format_play_time(time_str):
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if not time_str or not isinstance(time_str, str): return None
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try:
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parts = time_str.split(':');
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hours = int(parts[0]);
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minutes = int(parts[1])
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return hours * 60 + minutes
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except (ValueError, IndexError):
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return None
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# --- UPDATED Helper function to add TMS location column ---
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def add_tms_locations_to_analysis(analysis_df, tms_movie_list):
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locations = []
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for index, row in analysis_df.iterrows():
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movie_title = row['影片']
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found_versions = []
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for tms_movie in tms_movie_list:
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# FIX 3: Change matching from 'in' to 'startswith'
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if tms_movie['assert_name'].startswith(movie_title):
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version_name = tms_movie['assert_name'].replace(movie_title, '').strip()
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circled_halls = " ".join(sorted([get_circled_number(h) for h in tms_movie['halls']]))
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# FIX 2: Handle empty version name to remove colon
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if version_name:
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found_versions.append(f"{version_name}:{circled_halls}")
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else:
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found_versions.append(circled_halls)
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locations.append('|'.join(found_versions))
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analysis_df['影片所在影厅位置'] = locations
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return analysis_df
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# --- Streamlit Main UI ---
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st.title('影城排片效率与内容分析工具')
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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。")
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uploaded_file = st.file_uploader("请在此处上传 Excel 文件", type=['xlsx', 'xls', 'csv'])
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query_tms_for_location = st.checkbox("查询 TMS 找影片所在影厅")
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if uploaded_file is not None:
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try:
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'座次比': '{:.2%}', '场次比': '{:.2%}', '票房比': '{:.2%}', '座次效率': '{:.2f}',
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'场次效率': '{:.2f}'}
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full_day_analysis = process_and_analyze_data(df.copy())
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prime_time_analysis = process_and_analyze_data(
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df[df['放映时间'].between(pd.to_datetime('14:00:00').time(), pd.to_datetime('21:00:00').time())].copy())
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if query_tms_for_location:
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with st.spinner("正在关联查询 TMS 服务器..."):
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_, tms_movie_list = fetch_and_process_server_movies()
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full_day_analysis = add_tms_locations_to_analysis(full_day_analysis, tms_movie_list)
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prime_time_analysis = add_tms_locations_to_analysis(prime_time_analysis, tms_movie_list)
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# FIX 1: Reorder columns
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if '影片所在影厅位置' in full_day_analysis.columns:
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cols_full = full_day_analysis.columns.tolist()
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cols_full.insert(1, cols_full.pop(cols_full.index('影片所在影厅位置')))
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full_day_analysis = full_day_analysis[cols_full]
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if '影片所在影厅位置' in prime_time_analysis.columns:
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cols_prime = prime_time_analysis.columns.tolist()
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cols_prime.insert(1, cols_prime.pop(cols_prime.index('影片所在影厅位置')))
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prime_time_analysis = prime_time_analysis[cols_prime]
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st.toast("TMS 影片位置关联成功!", icon="🔗")
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st.markdown("### 全天排片效率分析")
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if not full_day_analysis.empty:
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st.dataframe(
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full_day_analysis.style.format(format_config),
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use_container_width=True, hide_index=True)
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st.markdown("#### 黄金时段排片效率分析 (14:00-21:00)")
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if not prime_time_analysis.empty:
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st.dataframe(
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prime_time_analysis.style.format(format_config),
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use_container_width=True, hide_index=True)
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if not full_day_analysis.empty:
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st.markdown("##### 复制当日排片列表")
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except Exception as e:
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st.error(f"处理文件时出错: {e}")
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st.divider()
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st.markdown("### TMS 服务器影片内容查询")
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if st.button('点击查询 TMS 服务器'):
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with st.spinner("正在从 TMS 服务器获取数据中..."):
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try:
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halls_data, movie_list_sorted = fetch_and_process_server_movies()
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st.toast("TMS 服务器数据获取成功!", icon="🎉")
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st.markdown("#### 按影片查看所在影厅")
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view2_data = [{'影片名称': item['assert_name'],
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'所在影厅': " ".join(sorted([get_circled_number(h) for h in item['halls']])),
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'文件名': item['content_name'], '时长': format_play_time(item['play_time'])} for item in
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movie_list_sorted]
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df_view2 = pd.DataFrame(view2_data)
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st.dataframe(df_view2, hide_index=True, use_container_width=True)
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st.markdown("#### 按影厅查看影片内容")
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hall_tabs = st.tabs(halls_data.keys())
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for tab, hall_name in zip(hall_tabs, halls_data.keys()):
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with tab:
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view1_data_for_tab = [{'影片名称': item['details']['assert_name'], '所在影厅': " ".join(
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sorted([get_circled_number(h) for h in item['details']['halls']])),
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'文件名': item['content_name'],
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'时长': format_play_time(item['details']['play_time'])} for item in
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halls_data[hall_name]]
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df_view1_tab = pd.DataFrame(view1_data_for_tab)
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st.dataframe(df_view1_tab, hide_index=True, use_container_width=True)
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except Exception as e:
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st.error(f"查询服务器时出错: {e}")
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