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):
    """生成打印页面的 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")