app3.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
from matplotlib.backends.backend_pdf import PdfPages
from matplotlib.patches import Rectangle # Replaced FancyBboxPatch

# --- Constants ---
SPLIT_TIME = "17:30"
BUSINESS_START = "09:30"
BUSINESS_END = "01:30"
BORDER_COLOR = '#A9A9A9'
DATE_COLOR = '#A9A9A9'
SEQ_COLOR = '#A9A9A9' # Color for the new serial number

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) + ' '

        # 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()
        # Using errors='coerce' will turn unparseable times into NaT (Not a Time)
        df['StartTime'] = pd.to_datetime(df['StartTime'], errors='coerce')
        df['EndTime'] = pd.to_datetime(df['EndTime'], errors='coerce')
        df = df.dropna(subset=['StartTime', 'EndTime']) # Drop rows where time conversion failed

        # 设置基准时间
        business_start_time = datetime.strptime(BUSINESS_START, "%H:%M").time()
        business_end_time = datetime.strptime(BUSINESS_END, "%H:%M").time()

        # 处理跨天情况：结束时间小于开始时间，则结束时间加一天
        # This logic handles cases like 9:30 AM to 1:30 AM (next day)
        df['EndTime_adjusted'] = df.apply(
            lambda row: row['EndTime'] + timedelta(days=1) if row['EndTime'].time() < row['StartTime'].time() else row['EndTime'],
            axis=1
        )
        
        # 按散场时间排序 (using the adjusted time)
        df = df.sort_values('EndTime_adjusted')

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

        # 格式化时间显示 (use original EndTime for display)
        for part in [part1, part2]:
            part['EndTime_formatted'] = part['EndTime'].dt.strftime('%-I:%M')

        # 读取日期单元格 C6
        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):
                # Assuming format like '2023-10-27'
                date_str = datetime.strptime(date_cell, '%Y-%m-%d').strftime('%Y-%m-%d')
            else:
                # Assuming it's a datetime object
                date_str = pd.to_datetime(date_cell).strftime('%Y-%m-%d')
        except:
            date_str = datetime.today().strftime('%Y-%m-%d')

        return part1[['Hall', 'EndTime_formatted']], part2[['Hall', 'EndTime_formatted']], 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)。
    1. 动态计算边距。
    2. 使用灰色虚线圆点作为单元格边框。
    3. 单元格内容区域为单元格的90%。
    4. 在左上角添加灰色序号。
    """
    if data.empty:
        return None

    # --- Constants ---
    A5_WIDTH_IN = 5.83
    A5_HEIGHT_IN = 8.27
    DPI = 300
    NUM_COLS = 3

    # --- Setup Figure ---
    fig = plt.figure(figsize=(A5_WIDTH_IN, A5_HEIGHT_IN), dpi=DPI)
    
    # --- Font Setup ---
    plt.rcParams['font.family'] = 'sans-serif'
    plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'Heiti TC', 'sans-serif']


    # --- Data Preparation ---
    total_items = len(data)
    # Augment data with an original index for numbering
    data_values_with_index = [(i, row) for i, row in enumerate(data.values.tolist())]
    
    # Pad data to be a multiple of NUM_COLS
    padded_total = math.ceil(total_items / NUM_COLS) * NUM_COLS
    while len(data_values_with_index) < padded_total:
        data_values_with_index.append((None, ['', '']))

    num_rows = padded_total // NUM_COLS

    # --- Layout Calculation (Request 1) ---
    if num_rows > 0:
        # "A5 paper height / num_rows / 4 is the padding for all sides"
        padding_in = (A5_HEIGHT_IN / num_rows / 4)
        # Cap padding to prevent it from being excessively large
        padding_in = min(padding_in, 0.5) 
    else:
        padding_in = 0.25 # Default padding if no rows

    # Convert padding to relative figure coordinates for subplots_adjust
    left_margin = padding_in / A5_WIDTH_IN
    right_margin = 1 - left_margin
    bottom_margin = padding_in / A5_HEIGHT_IN
    top_margin = 1 - bottom_margin
    
    # Adjust overall figure margins
    fig.subplots_adjust(left=left_margin, right=right_margin, top=top_margin, bottom=bottom_margin, hspace=0.4, wspace=0.4)

    # --- Grid & Font Size ---
    gs = gridspec.GridSpec(num_rows + 1, NUM_COLS, height_ratios=[0.2] + [1] * num_rows, figure=fig)

    if num_rows > 0:
        content_area_height_in = A5_HEIGHT_IN * (top_margin - bottom_margin)
        cell_height_in = content_area_height_in / num_rows * (1 - fig.subplotpars.hspace)
        base_fontsize = min(40, max(10, cell_height_in * 72 * 0.4)) # 72 pt/inch, 40% of cell height
    else:
        base_fontsize = 20

    # --- Z-Sort (Column-major) Data for Layout ---
    rows_per_col_layout = num_rows
    sorted_data = [(None, ['',''])] * padded_total
    for i, item_tuple in enumerate(data_values_with_index):
        if item_tuple[0] is not None:
            original_data_index = i # Index from the time-sorted list
            row_in_col = original_data_index % rows_per_col_layout
            col_idx = original_data_index // rows_per_col_layout
            new_grid_index = row_in_col * NUM_COLS + col_idx
            if new_grid_index < len(sorted_data):
                sorted_data[new_grid_index] = item_tuple

    # --- Drawing Logic ---
    for grid_idx, item_tuple in enumerate(sorted_data):
        original_index, (hall, end_time) = item_tuple
        
        if original_index is not None:
            row_grid = grid_idx // NUM_COLS + 1 # +1 because date is in row 0
            col_grid = grid_idx % NUM_COLS
            
            ax = fig.add_subplot(gs[row_grid, col_grid])
            ax.set_axis_off()

            # --- Cell Border (Request 2) & Content Area (Request 3) ---
            # Draw a dotted rectangle. Content will be placed inside this.
            # Making the rect slightly smaller creates a visual 90% area.
            cell_border = Rectangle((0.05, 0.05), 0.9, 0.9,
                                    edgecolor=BORDER_COLOR,
                                    facecolor='none',
                                    linestyle=(0, (1, 1.5)), # Dotted line with round caps
                                    linewidth=1,
                                    transform=ax.transAxes,
                                    clip_on=False)
            ax.add_patch(cell_border)

            # --- Cell Content ---
            display_text = f"{hall}{end_time}"
            ax.text(0.5, 0.5, display_text,
                    fontsize=base_fontsize,
                    fontweight='bold',
                    ha='center', va='center',
                    transform=ax.transAxes)

            # --- Cell Numbering (Request 4) ---
            # Serial number is original_index + 1
            ax.text(0.12, 0.82, str(original_index + 1),
                    fontsize=base_fontsize * 0.5,
                    color=SEQ_COLOR,
                    fontweight='normal',
                    ha='center', va='center',
                    transform=ax.transAxes)

    # --- Date Header ---
    ax_date = fig.add_subplot(gs[0, :])
    ax_date.set_axis_off()
    ax_date.text(0, 0.5, f"{date_str} {title}",
                 fontsize=base_fontsize * 0.6,
                 color=DATE_COLOR,
                 fontweight='bold',
                 ha='left', va='center',
                 transform=ax_date.transAxes)

    # --- Save to Buffers ---
    # Save PNG
    png_buffer = io.BytesIO()
    fig.savefig(png_buffer, format='png', bbox_inches='tight', pad_inches=0.02)
    png_buffer.seek(0)
    png_base64 = base64.b64encode(png_buffer.getvalue()).decode()

    # Save PDF
    pdf_buffer = io.BytesIO()
    fig.savefig(pdf_buffer, format='pdf', bbox_inches='tight', pad_inches=0.02)
    pdf_buffer.seek(0)
    pdf_base64 = base64.b64encode(pdf_buffer.getvalue()).decode()
    
    plt.close(fig)

    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="{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:
    # Use new column name 'EndTime_formatted' for display
    part1, part2, date_str = process_schedule(uploaded_file)
    if part1 is not None and part2 is not None:
        part1_data_for_layout = part1[['Hall', 'EndTime_formatted']]
        part2_data_for_layout = part2[['Hall', 'EndTime_formatted']]
        
        part1_output = create_print_layout(part1_data_for_layout, "A", date_str)
        part2_output = create_print_layout(part2_data_for_layout, "C", date_str)

        col1, col2 = st.columns(2)

        with col1:
            st.subheader("白班散场预览（散场时间 ≤ 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（散场时间 > 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")