app.py

import pandas as pd
import streamlit as st
from datetime import datetime, timedelta
import matplotlib.pyplot as plt
import io
import base64
import matplotlib.gridspec as gridspec
import math
import re

SPLIT_TIME = "17:30"
BUSINESS_START = "09:30"
BUSINESS_END = "01:30"
BORDER_COLOR = '#A9A9A9'
DATE_COLOR = '#A9A9A9'

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'])
        
        # 转换影厅格式为带LaTeX上标井号
        df['Hall'] = df['Hall'].str.extract(r'(\d+)号').astype(str) + r'$^{\#}$'
        
        # 保存原始时间字符串用于诊断
        df['original_end'] = df['EndTime']
        
        # 转换时间为 datetime 对象
        base_date = datetime.today().date()
        df['StartTime'] = pd.to_datetime(df['StartTime'])
        df['EndTime'] = pd.to_datetime(df['EndTime'])
        
        # 设置基准时间
        business_start = datetime.strptime(f"{base_date} {BUSINESS_START}", "%Y-%m-%d %H:%M")
        business_end = datetime.strptime(f"{base_date} {BUSINESS_END}", "%Y-%m-%d %H:%M")
        
        # 处理跨天情况
        if business_end < business_start:
            business_end += timedelta(days=1)
        
        # 标准化所有时间到同一天
        for idx, row in df.iterrows():
            end_time = row['EndTime']
            if end_time.hour < 9:
                df.at[idx, 'EndTime'] = end_time + timedelta(days=1)
            
            if row['StartTime'].hour >= 21 and end_time.hour < 9:
                df.at[idx, 'EndTime'] = end_time + timedelta(days=1)
        
        # 筛选营业时间内的场次
        df['time_for_comparison'] = df['EndTime'].apply(
            lambda x: datetime.combine(base_date, x.time())
        )
        
        df.loc[df['time_for_comparison'].dt.hour < 9, 'time_for_comparison'] += timedelta(days=1)
        
        valid_times = (
            ((df['time_for_comparison'] >= datetime.combine(base_date, business_start.time())) &
            (df['time_for_comparison'] <= datetime.combine(base_date + timedelta(days=1), business_end.time()))
        )
        
        df = df[valid_times]
        
        # 按散场时间排序
        df = df.sort_values('EndTime')
        
        # 分割数据
        split_time = datetime.strptime(f"{base_date} {SPLIT_TIME}", "%Y-%m-%d %H:%M")
        split_time_for_comparison = df['time_for_comparison'].apply(
            lambda x: datetime.combine(base_date, split_time.time())
        )
        
        part1 = df[df['time_for_comparison'] <= split_time_for_comparison].copy()
        part2 = df[df['time_for_comparison'] > split_time_for_comparison].copy()
        
        # 格式化时间显示
        for part in [part1, part2]:
            part['EndTime'] = part['EndTime'].dt.strftime('%-I:%M')
        
        # 读取日期信息
        date_df = pd.read_excel(
            file,
            skiprows=5,
            nrows=1,
            usecols=[2],
            header=None
        )
        date_cell = date_df.iloc[0, 0]
        
        try:
            if isinstance(date_cell, str):
                date_str = datetime.strptime(date_cell, '%Y-%m-%d').strftime('%Y-%m-%d')
            else:
                date_str = pd.to_datetime(date_cell).strftime('%Y-%m-%d')
        except:
            date_str = datetime.today().strftime('%Y-%m-%d')
        
        return part1[['Hall', 'EndTime']], part2[['Hall', 'EndTime']], date_str
    
    except Exception as e:
        st.error(f"处理文件时出错: {str(e)}")
        return None, None, None

def create_print_layout(data, title, date_str):
    """创建打印布局"""
    if data.empty:
        return None
        
    # 设置 A5 纸张横向尺寸
    fig = plt.figure(figsize=(5.83, 8.27), dpi=300)
    plt.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05)
    
    # 设置字体
    plt.rcParams['font.family'] = 'sans-serif'
    plt.rcParams['font.sans-serif'] = ['Arial Unicode MS']
    
    # 计算行数和总数
    total_items = len(data)
    num_cols = 3
    num_rows = math.ceil(total_items / num_cols)
    
    # 创建网格（优化间距参数）
    gs = gridspec.GridSpec(num_rows + 1, num_cols, 
                         hspace=0.2, wspace=0.2,  # 增加行列间距
                         height_ratios=[1.2] * num_rows + [0.2])
    
    # 计算最大字符数
    max_char_count = 0
    for hall, end_time in data.values:
        clean_hall = re.sub(r'\$.*?\$', '#', hall)
        clean_text = f"{clean_hall}{end_time}"
        current_count = len(clean_text)
        max_char_count = max(max_char_count, current_count)
    
    # 动态计算基础字号（优化计算参数）
    cell_width_inches = 5.83 / 3  # 每列宽度（A5横向）
    available_width = cell_width_inches * 0.65 * 72  # 可用宽度减少到65%
    avg_char_width = 0.8  # 加粗字体宽度系数
    base_fontsize = available_width / (max_char_count * avg_char_width)
    base_fontsize = min(26, base_fontsize)  # 设置最大字号限制
    
    # 填充数据
    data_values = data.values.tolist()
    while len(data_values) % 3 != 0:
        data_values.append(['', ''])
        
    sorted_data = [['', '']] * len(data_values)
    
    for i, item in enumerate(data_values):
        if item[0] and item[1]:
            row = i % math.ceil(len(data_values)/3)
            col = i // math.ceil(len(data_values)/3)
            new_index = row * 3 + col
            if new_index < len(sorted_data):
                sorted_data[new_index] = item
    
    for idx, (hall, end_time) in enumerate(sorted_data):
        if hall and end_time:
            row = idx // 3
            col = idx % 3
            
            ax = plt.subplot(gs[row, col])
            
            # 设置单元格边界范围（增加内边距）
            ax.set_xlim(0.1, 0.9)  # 左右各留10%边距
            ax.set_ylim(0.1, 0.9)  # 上下各留10%边距
            
            for spine in ax.spines.values():
                spine.set_color(BORDER_COLOR)
                spine.set_linewidth(0.5)
            
            # 添加文本（字号缩小5%）
            ax.text(0.5, 0.5, f"{hall}{end_time}",
                    fontsize=base_fontsize*0.95,
                    fontweight='bold',
                    ha='center',
                    va='center',
                    transform=ax.transAxes)
            
            ax.set_xticks([])
            ax.set_yticks([])
    
    # 添加日期信息
    ax_date = plt.subplot(gs[0, 0])
    ax_date.text(0.05, 0.95, f"{date_str} {title}",
                 fontsize=base_fontsize * 0.4,
                 color=DATE_COLOR,
                 fontweight='bold',
                 ha='left',
                 va='top')
    
    for spine in ax_date.spines.values():
        spine.set_visible(False)
    ax_date.set_xticks([])
    ax_date.set_yticks([])
    
    # 转换为图片
    buffer = io.BytesIO()
    plt.savefig(buffer, format='png', bbox_inches='tight', pad_inches=0.05)
    buffer.seek(0)
    image_base64 = base64.b64encode(buffer.getvalue()).decode()
    plt.close()
    
    return f'data:image/png;base64,{image_base64}'

# 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, date_str = process_schedule(uploaded_file)
    
    if part1 is not None and part2 is not None:
        part1_image = create_print_layout(part1, "A", date_str)
        part2_image = create_print_layout(part2, "C", date_str)
        
        col1, col2 = st.columns(2)
        
        with col1:
            st.subheader("白班散场预览（时间 ≤ 17:30）")
            if part1_image:
                st.image(part1_image)
            else:
                st.info("白班部分没有数据")
        
        with col2:
            st.subheader("夜班散场预览（时间 > 17:30）")
            if part2_image:
                st.image(part2_image)
            else:
                st.info("夜班部分没有数据")