from pkg.dash.func.info_func import *
from pkg.dash.app_init import app
from dash.dependencies import Input, Output
from dash import html
import pandas as pd
import re
from datetime import timedelta

@app.callback(
    [Output('test-info-tooltip', 'children')],
    [Input('test-button', 'n_clicks'),
     Input('test-date-input', 'date'),
     Input('test-time-input', 'value')]
)
def update_test_info(n_clicks, test_date, test_time):
    if n_clicks == 0:
        return [html.Div("Click 'Test' to see historical probability results.")]

    est = pytz.timezone('US/Eastern')
    data = render_data.global_agg_df.copy()

    # 调试：打印输入值
    print(f"test_date: {test_date}, test_time: {test_time}")

    # 检查输入是否为空
    if not test_date or not test_time:
        return [html.Div("Date or time input is empty. Please provide both date (YYYY-MM-DD) and time (HH:MM:SS).")]

    # 验证时间格式
    time_pattern = r'^(?:[01]\d|2[0-3]):[0-5]\d:[0-5]\d$'  # HH:MM:SS (00:00:00 to 23:59:59)
    if not re.match(time_pattern, test_time):
        return [html.Div("Invalid time format. Use HH:MM:SS (e.g., 12:00:00) with hours 00-23, minutes 00-59, seconds 00-59.")]

    # 重构 datetime_est，处理夏令时模糊时间
    data['hours'] = data['minute_of_day'] // 60
    data['minutes'] = data['minute_of_day'] % 60
    data['datetime_est'] = pd.to_datetime(
        data['date'].astype(str) + ' ' +
        data['hours'].astype(str) + ':' +
        data['minutes'].astype(str) + ':00',
        errors='coerce'
    ).dt.tz_localize(est, ambiguous='NaT')

    if data['datetime_est'].isna().any():
        print("Warning: Some datetime_est values are NaT due to ambiguous time handling")

    # 解析测试日期和时间
    try:
        test_date = pd.to_datetime(test_date, format='%Y-%m-%d').date()
        test_datetime = pd.to_datetime(f"{test_date} {test_time}", format='%Y-%m-%d %H:%M:%S').tz_localize(est, ambiguous=True)
    except ValueError as e:
        print(f"Error parsing date/time: {e}")
        return [html.Div("Invalid date or time format. Use YYYY-MM-DD and HH:MM:SS (e.g., 12:00:00).")]

    # 计算周期开始时间（上一个周五 12:00 PM）
    test_date_only = test_datetime.replace(hour=0, minute=0, second=0, microsecond=0)  # 只考虑日期部分
    days_to_last_friday = (test_date_only.weekday() - 4) % 7  # 4 表示周五
    cycle_start = test_date_only - timedelta(days=days_to_last_friday)
    cycle_start = cycle_start.replace(hour=12, minute=0, second=0, microsecond=0)  # 已经是 tz-aware，直接调整时间

    # 确保周期结束时间（下周五 12:00 PM EDT）考虑夏令时
    cycle_end = cycle_start + timedelta(days=7)
    if cycle_end.month == 3 and 8 <= cycle_end.day <= 14:  # 粗略检查夏令时开始（3月第二个星期日）
        cycle_end = cycle_end.tz_convert(est)  # 转换为 EDT
    else:
        cycle_end = cycle_end.tz_convert(est)  # 保持一致

    # 调试：打印周期信息
    print(f"Cycle Start: {cycle_start}, Cycle End: {cycle_end}")

    # 过滤周期内的数据
    cycle_data = data[(data['datetime_est'] >= cycle_start) & (data['datetime_est'] <= test_datetime)]
    if cycle_data.empty:
        return [html.Div(f"No data available in cycle from {cycle_start} to {test_datetime}")]
    tweet_count = cycle_data['tweet_count'].sum()

    # 计算实际最终推文数（周期结束时的总数）
    actual_data = data[(data['datetime_est'] >= cycle_start) & (data['datetime_est'] <= cycle_end)]
    if actual_data.empty:
        return [html.Div(f"No data available for cycle ending {cycle_end}")]
    actual_end_count = actual_data['tweet_count'].sum()

    # 计算 days_to_next_friday（从 test_datetime 到周期结束）
    days_to_next_friday = (cycle_end - test_datetime).total_seconds() / (24 * 60 * 60)

    # 设置预测范围
    prob_start = actual_end_count * 0.9
    prob_end = actual_end_count * 1.1

    # 计算概率
    probability = calculate_tweet_probability(tweet_count, days_to_next_friday, prob_start, prob_end)
    prob_min, prob_max = map(float, probability.split(" - "))
    formatted_probability = f"{prob_min * 100:.2f}% - {prob_max * 100:.2f}%"

    # 构建测试结果表格
    test_table_rows = [
        html.Tr([html.Th("Test Date and Time:", colSpan=2), html.Td(str(test_datetime), colSpan=6)]),
        html.Tr([html.Th("Tweet Count at Test Time:", colSpan=2), html.Td(str(tweet_count), colSpan=6)]),
        html.Tr([html.Th("Actual Final Tweet Count:", colSpan=2), html.Td(str(actual_end_count), colSpan=6)]),
        html.Tr([html.Th(f"Predicted Range ({int(prob_start)}-{int(prob_end)}):", colSpan=2), html.Td(formatted_probability, colSpan=6)]),
        html.Tr([html.Th("Does Actual Fall in Range?", colSpan=2),
                 html.Td("Yes" if prob_start <= actual_end_count <= prob_end else "No",
                         colSpan=6, style={'color': 'green' if prob_start <= actual_end_count <= prob_end else 'red'})])
    ]
    if prob_start <= actual_end_count <= prob_end:
        expected_prob = (prob_max + prob_min) / 2
        test_table_rows.append(html.Tr([html.Th("Expected Probability:", colSpan=2),
                                        html.Td(f"~{expected_prob * 100:.2f}% (should be high if model fits)", colSpan=6)]))
    else:
        test_table_rows.append(html.Tr([html.Th("Note:", colSpan=2),
                                        html.Td("Model prediction does not match actual outcome.", colSpan=6, style={'color': 'red'})]))

    test_table = html.Table(test_table_rows, style={'width': '100%', 'textAlign': 'left', 'borderCollapse': 'collapse'})
    return [test_table]