Create app.py

app.py

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+import pandas as pd
+import streamlit as st
+from datetime import datetime, timedelta
+import matplotlib.pyplot as plt
+import io
+import base64
+from matplotlib.patches import Rectangle
+import matplotlib.gridspec as gridspec
+
+# 定义全局常量
+SPLIT_TIME = "17:30"  # 分界线时间
+BUSINESS_START = "09:30"  # 营业开始时间
+BUSINESS_END = "01:30"  # 营业结束时间
+
+def process_schedule(file):
+    """处理上传的 Excel 文件，生成排序和分组后的打印内容"""
+    try:
+        # 读取 Excel，跳过前 8 行
+        df = pd.read_excel(file, skiprows=8)
+        
+        # 提取所需列 (G9, H9, J9)
+        df = df.iloc[:, [6, 7, 9]]  # G, H, J 列
+        df.columns = ['Hall', 'StartTime', 'EndTime']
+        
+        # 清理数据
+        df = df.dropna(subset=['Hall', 'StartTime', 'EndTime'])
+        
+        # 转换影厅格式为 "#号" 格式
+        df['Hall'] = df['Hall'].str.extract(r'(\d+)号').astype(str) + '#'
+        
+        # 转换时间格式
+        df['StartTime'] = pd.to_datetime(df['StartTime']).dt.strftime('%H:%M')
+        df['EndTime'] = pd.to_datetime(df['EndTime']).dt.strftime('%H:%M')
+        
+        # 将时间转换为 datetime 对象便于比较
+        base_date = datetime.today().date()
+        df['StartTime'] = pd.to_datetime(base_date.strftime('%Y-%m-%d ') + df['StartTime'])
+        df['EndTime'] = pd.to_datetime(base_date.strftime('%Y-%m-%d ') + df['EndTime'])
+        
+        # 处理跨天情况
+        df.loc[df['EndTime'] < df['StartTime'], 'EndTime'] += timedelta(days=1)
+        
+        # 筛选营业时间内的场次
+        business_start = datetime.strptime(base_date.strftime('%Y-%m-%d ') + BUSINESS_START, '%Y-%m-%d %H:%M')
+        business_end = datetime.strptime(base_date.strftime('%Y-%m-%d ') + BUSINESS_END, '%Y-%m-%d %H:%M')
+        if business_end < business_start:
+            business_end += timedelta(days=1)
+            
+        mask = (
+            (df['StartTime'] >= business_start) | 
+            (df['StartTime'] <= business_end)
+        )
+        df = df[mask]
+        
+        # 按散场时间排序
+        df = df.sort_values('EndTime')
+        
+        # 分割数据
+        split_time = datetime.strptime(base_date.strftime('%Y-%m-%d ') + SPLIT_TIME, '%Y-%m-%d %H:%M')
+        part1 = df[df['EndTime'] <= split_time].copy()
+        part2 = df[df['EndTime'] > split_time].copy()
+        
+        # 格式化时间显示
+        for part in [part1, part2]:
+            part['EndTime'] = part['EndTime'].dt.strftime('%-I:%M')
+            
+        return part1[['Hall', 'EndTime']], part2[['Hall', 'EndTime']]
+    
+    except Exception as e:
+        st.error(f"处理文件时出错: {str(e)}")
+        return None, None
+
+def create_print_layout(data, title):
+    """创建打印布局"""
+    if data.empty:
+        return None
+        
+    # 设置 A5 横向尺寸（单位：英寸）
+    fig = plt.figure(figsize=(8.27, 5.83), dpi=300)
+    
+    # 减小边距，最大化利用空间
+    plt.subplots_adjust(left=0.02, right=0.98, top=0.98, bottom=0.02)
+    
+    # 创建网格，减小间距
+    rows = (len(data) + 3) // 4  # 向上取整除法
+    gs = gridspec.GridSpec(rows, 4, hspace=0.1, wspace=0.1)  # 显著减小间距
+    
+    # 设置字体和样式
+    plt.rcParams['font.family'] = 'sans-serif'
+    plt.rcParams['font.sans-serif'] = ['SimHei']  # 中文字体
+    
+    # 计算适合的字体大小
+    base_fontsize = min(32, 180 / rows)  # 根据行数动态调整基础字体大小
+    
+    # 填充数据
+    for idx, (hall, end_time) in enumerate(data.values):
+        row = idx // 4
+        col = idx % 4
+        
+        # 创建子图
+        ax = plt.subplot(gs[row, col])
+        
+        # 设置边框样式
+        for spine in ax.spines.values():
+            spine.set_color('lightgray')
+            spine.set_linewidth(0.5)
+        
+        # 调整文本位置，增加上下间距
+        # 将影厅号向上移动，时间向下移动
+        ax.text(0.5, 0.65, hall,  # y 位置从 0.55 改为 0.65
+                fontsize=base_fontsize, 
+                fontweight='bold', 
+                ha='center', 
+                va='center')
+        ax.text(0.5, 0.2, end_time,  # y 位置从 0.25 改为 0.2
+                fontsize=base_fontsize, 
+                fontweight='bold', 
+                ha='center', 
+                va='center')
+        
+        # 设置更小的内边距
+        ax.set_xlim(-0.02, 1.02)
+        ax.set_ylim(-0.02, 1.02)
+        
+        # 移除坐标轴
+        ax.set_xticks([])
+        ax.set_yticks([])
+    
+    # 将图表转换为 base64 字符串
+    buffer = io.BytesIO()
+    plt.savefig(buffer, format='png', bbox_inches='tight', pad_inches=0.05)  # 减小边距
+    buffer.seek(0)
+    image_base64 = base64.b64encode(buffer.getvalue()).decode()
+    plt.close()
+    
+    return f'data:image/png;base64,{image_base64}'
+
+def get_html_content(image_base64):
+    """生成打印���面的 HTML 内容"""
+    return f"""
+    <!DOCTYPE html>
+    <html>
+    <head>
+        <style>
+            @page {{
+                size: A5 landscape;
+                margin: 0;
+            }}
+            body {{
+                margin: 0;
+                padding: 0;
+                display: flex;
+                justify-content: center;
+                align-items: center;
+                height: 100vh;
+            }}
+            img {{
+                width: 100%;
+                height: 100%;
+                object-fit: fill;
+            }}
+        </style>
+        <script>
+            window.onload = function() {{
+                window.print();
+            }}
+        </script>
+    </head>
+    <body>
+        <img src="{image_base64}">
+    </body>
+    </html>
+    """
+
+# Streamlit 界面
+st.set_page_config(page_title="散厅时间快捷打印", layout="wide")
+st.title("散厅时间快捷打印")
+
+uploaded_file = st.file_uploader("上传 放映场次核对表.xls 文件", type=["xls", "xlsx"])
+
+if uploaded_file:
+    part1, part2 = process_schedule(uploaded_file)
+    
+    if part1 is not None and part2 is not None:
+        # 创建打印布局
+        part1_image = create_print_layout(part1, "第一部分")
+        part2_image = create_print_layout(part2, "第二部分")
+        
+        # 显示预览
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            st.subheader("第一部分预览 (散场时间 ≤ 17:30)")
+            if part1_image:
+                st.image(part1_image)
+            else:
+                st.info("第一部分没有数据")
+        
+        with col2:
+            st.subheader("第二部分预览 (散场时间 > 17:30)")
+            if part2_image:
+                st.image(part2_image)
+            else:
+                st.info("第二部分没有数据")