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 matplotlib.font_manager as fm
import math

# --- CONSTANTS ---
SPLIT_TIME = "17:30"
BUSINESS_START = "09:30"
BUSINESS_END = "01:30"
BORDER_COLOR = '#A9A9A9'
DATE_COLOR = '#A9A9A9'
A5_WIDTH_IN = 5.83
A5_HEIGHT_IN = 8.27
DPI = 300
NUM_COLS = 3

# --- FONT SETUP ---
# Attempt to load the specified font, with a safe fallback.
try:
    # IMPORTANT: Place 'SimHei.ttf' in the same directory as the script.
    FONT_PROP = fm.FontProperties(fname='SimHei.ttf')
except FileNotFoundError:
    st.warning("SimHei.ttf not found. Using a default sans-serif font. Chinese characters may not display correctly.")
    FONT_PROP = fm.FontProperties(family='sans-serif')


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'])
        df = df[df['Hall'].astype(str).str.contains(r'\d', na=False)] # Ensure Hall has a digit

        # 转换影厅格式为 "#号" 格式
        df['Hall'] = df['Hall'].astype(str).str.extract(r'(\d+)').astype(str) + ' '

        # 转换时间为 datetime 对象
        base_date = datetime.today().date()
        df['StartTime'] = pd.to_datetime(df['StartTime'], errors='coerce').dt.time
        df['EndTime'] = pd.to_datetime(df['EndTime'], errors='coerce').dt.time

        df = df.dropna(subset=['StartTime', 'EndTime'])

        def combine_date_time(t):
            dt = datetime.combine(base_date, t)
            # Handle overnight shows (times past midnight like 1:30 AM are for the next day)
            if t.hour < int(BUSINESS_START.split(':')[0]) - 1:
                return dt + timedelta(days=1)
            return dt

        df['StartDateTime'] = df['StartTime'].apply(combine_date_time)
        df['EndDateTime'] = df['EndTime'].apply(combine_date_time)

        # 按散场时间排序
        df = df.sort_values('EndDateTime')

        # 分割数据
        split_dt = datetime.combine(base_date, datetime.strptime(SPLIT_TIME, "%H:%M").time())
        
        part1 = df[df['EndDateTime'] <= split_dt].copy()
        part2 = df[df['EndDateTime'] > split_dt].copy()

        # 格式化时间显示
        for part in [part1, part2]:
            part['EndTimeStr'] = part['EndDateTime'].dt.strftime('%-I:%M')

        # 关键修改：精确读取C6单元格
        date_df = pd.read_excel(
            file,
            skiprows=5,  # 跳过前5行（0-4）
            nrows=1,     # 只读1行
            usecols=[2], # 第三列（C列）
            header=None  # 无表头
        )
        date_cell = date_df.iloc[0, 0]

        try:
            # 处理不同日期格式
            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', 'EndTimeStr']], part2[['Hall', 'EndTimeStr']], 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):
    """创建符合新要求的打印布局 (PNG 和 PDF)"""
    if data.empty:
        return None

    # --- 1. 动态字体大小计算 ---
    # 目标：让最长的文本占单元格宽度的90%
    longest_text = ""
    if not data.empty:
        data['combined_text'] = data['Hall'] + data['EndTimeStr']
        if not data['combined_text'].empty:
            longest_text = data.loc[data['combined_text'].str.len().idxmax(), 'combined_text']

    # 估算单元格宽度 (A5纸张，减去边距和间距)
    fig_width_in = A5_WIDTH_IN
    margin_frac = 0.04  # 4% margin on each side
    wspace_frac = 0.05  # 5% space between columns
    
    # 可用绘图宽度
    drawable_width_in = fig_width_in * (1 - 2 * margin_frac)
    # 宽度被3个子图和2个间隙瓜分
    # 3*cell_w + 2*(cell_w*wspace) = drawable_width
    cell_width_in = drawable_width_in / (NUM_COLS + (NUM_COLS - 1) * wspace_frac)
    
    main_fontsize = 30  # Default start size
    if longest_text:
        # 估算文本宽度 (这是一个经验法则，0.7是中英混合字体的估算因子)
        # 文本宽度(点) ≈ 字符数 * 字体大小(点) * 因子
        estimated_text_width_pt = len(longest_text) * main_fontsize * 0.7
        target_width_pt = (cell_width_in * 0.9) * 72  # 目标宽度 (英寸*0.9 -> 点)
        
        if estimated_text_width_pt > 0:
            ratio = target_width_pt / estimated_text_width_pt
            main_fontsize *= ratio
    
    main_fontsize = max(10, min(main_fontsize, 50))  # 限制字体大小在合理范围
    index_fontsize = main_fontsize * 0.45 # 序号字体大小

    # --- 2. 准备绘图 ---
    def generate_figure():
        fig = plt.figure(figsize=(A5_WIDTH_IN, A5_HEIGHT_IN), dpi=DPI)
        fig.subplots_adjust(left=margin_frac, right=1-margin_frac, top=0.95, bottom=margin_frac, hspace=0.2, wspace=wspace_frac)

        total_items = len(data)
        num_rows = math.ceil(total_items / NUM_COLS)

        # 创建网格
        gs = gridspec.GridSpec(num_rows + 1, NUM_COLS, height_ratios=[0.2] + [1] * num_rows, figure=fig)

        # 绘制日期标题
        ax_date = fig.add_subplot(gs[0, :])
        ax_date.text(0, 0.5, f"{date_str} {title}", fontproperties=FONT_PROP,
                     fontsize=main_fontsize * 0.5, color=DATE_COLOR, fontweight='bold',
                     ha='left', va='center', transform=ax_date.transAxes)
        ax_date.set_axis_off()

        # 准备Z字形排列的数据
        data_values = data[['Hall', 'EndTimeStr']].values.tolist()
        while len(data_values) % NUM_COLS != 0:
            data_values.append(['', ''])
        
        rows_per_col_layout = math.ceil(len(data_values) / NUM_COLS)
        sorted_data = [['', '']] * len(data_values)
        for i, item in enumerate(data_values):
            if item[0] and item[1]:
                row_in_col = i % rows_per_col_layout
                col_idx = i // rows_per_col_layout
                new_index = row_in_col * NUM_COLS + col_idx
                if new_index < len(sorted_data):
                    sorted_data[new_index] = item
        
        item_counter = 0
        for idx, (hall, end_time) in enumerate(sorted_data):
            if hall and end_time:
                item_counter += 1
                row_grid = idx // NUM_COLS + 1
                col_grid = idx % NUM_COLS

                if row_grid < num_rows + 1:
                    ax = fig.add_subplot(gs[row_grid, col_grid])
                    
                    # --- 3. 绘制单元格 ---
                    # a. 设置点状虚线边框
                    for spine in ax.spines.values():
                        spine.set_linestyle((0, (1, 2)))  # (offset, (on, off)) tuple for dotted
                        spine.set_edgecolor(BORDER_COLOR)
                        spine.set_linewidth(1)

                    # b. 居中绘制主要文本
                    display_text = f"{hall}{end_time}"
                    ax.text(0.5, 0.5, display_text, fontproperties=FONT_PROP,
                            fontsize=main_fontsize, fontweight='bold',
                            ha='center', va='center', transform=ax.transAxes)
                    
                    # c. 在左上角添加序号
                    ax.text(0.05, 0.95, str(item_counter), fontproperties=FONT_PROP,
                            fontsize=index_fontsize, color='grey',
                            ha='left', va='top', transform=ax.transAxes)

                    ax.set_xticks([])
                    ax.set_yticks([])
        return fig

    # --- 4. 保存为 PNG 和 PDF ---
    fig_for_output = generate_figure()

    # 保存 PNG
    png_buffer = io.BytesIO()
    fig_for_output.savefig(png_buffer, format='png', dpi=DPI)
    png_buffer.seek(0)
    png_base64 = base64.b64encode(png_buffer.getvalue()).decode()
    
    # 保存 PDF
    pdf_buffer = io.BytesIO()
    fig_for_output.savefig(pdf_buffer, format='pdf', dpi=DPI)
    pdf_buffer.seek(0)
    pdf_base64 = base64.b64encode(pdf_buffer.getvalue()).decode()

    plt.close(fig_for_output)

    return {
        'png': f'data:image/png;base64,{png_base64}',
        'pdf': f'data:application/pdf;base64,{pdf_base64}'
    }

def display_pdf(base64_pdf):
    """在Streamlit中嵌入显示PDF"""
    pdf_display = f'<iframe src="data:application/pdf;base64,{base64_pdf}" width="100%" height="800" type="application/pdf"></iframe>'
    return pdf_display

# --- Streamlit UI ---
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:
    # Rename columns in process_schedule call to match new names
    part1_data, part2_data, date_str = process_schedule(uploaded_file)

    if part1_data is not None and part2_data is not None:
        # Pass the dataframes with renamed 'EndTimeStr' column
        part1_output = create_print_layout(part1_data, "A", date_str)
        part2_output = create_print_layout(part2_data, "C", date_str)

        col1, col2 = st.columns(2)

        with col1:
            st.subheader("白班散场预览（结束时间 ≤ 17:30）")
            if part1_output:
                tab1_1, tab1_2 = st.tabs(["PDF 预览", "PNG 预览"])
                with tab1_1:
                    st.markdown(display_pdf(part1_output['pdf']), unsafe_allow_html=True)
                with tab1_2:
                    st.image(part1_output['png'])
            else:
                st.info("白班部分没有数据")

        with col2:
            st.subheader("夜班散场预览（结束时间 > 17:30）")
            if part2_output:
                tab2_1, tab2_2 = st.tabs(["PDF 预览", "PNG 预览"])
                with tab2_1:
                    st.markdown(display_pdf(part2_output['pdf']), unsafe_allow_html=True)
                with tab2_2:
                    st.image(part2_output['png'])
            else:
                st.info("夜班部分没有数据")