from datetime import datetime, timedelta
from dash.dependencies import Input, Output
from pkg.dash.app_init import app
from pkg.config import render_data
from pkg.tool import aggregate_data, generate_xticks, minutes_to_time, get_tweets_since_last_friday
from dash import dcc
import plotly.graph_objs as go
import pandas as pd


@app.callback(
    [Output('tabs-content', 'children'),
     Output('multi-day-warning', 'children'),
     Output('multi-tweet-summary', 'children')],
    [Input('tabs', 'value'),
     Input('multi-date-picker', 'value'),
     Input('multi-interval-picker', 'value'),
     Input('time-zone-checklist', 'value'),
     Input('days-display-picker', 'value')]
)
def render_tab_content(tab, selected_dates, interval, time_zones, days_to_display):
    warning = ""
    if tab == 'line':
        if not selected_dates:  # Handle None or empty list
            selected_dates = [datetime.now().date()]  # Default to today
            warning = "No dates selected. Showing today’s data."
        if len(selected_dates) > 10:
            selected_dates = selected_dates[:10]
            warning = "Maximum of 10 days can be selected. Showing first 10 selected days."
        selected_dates = [datetime.strptime(date, '%Y-%m-%d').date() for date in selected_dates]
    else:
        available_dates = sorted(render_data.global_agg_df['date'].unique(), reverse=True)
        selected_dates = available_dates[:days_to_display] if available_dates else [datetime.now().date()]
        if not available_dates:
            warning = "No data available. Showing today’s date with zero tweets."

    multi_data_agg = render_data.global_agg_df[render_data.global_agg_df['date'].isin(selected_dates)].copy()
    if multi_data_agg.empty:
        multi_data_agg = pd.DataFrame({
            'date': selected_dates,
            'minute_of_day': [0] * len(selected_dates),
        })
    tweet_count_total = multi_data_agg.get('tweet_count', pd.Series([0] * len(multi_data_agg))).sum()

    multi_data_raw = render_data.global_df[render_data.global_df['date'].isin(selected_dates)].copy()
    if multi_data_raw.empty:
        tweet_count_total = 0

    agg_data = aggregate_data(multi_data_agg, interval)
    xticks, xtick_labels = generate_xticks(interval)

    if tab == 'line':
        fig = go.Figure()
        for date in selected_dates:
            day_data = agg_data[agg_data['date'] == date]
            hover_times = [f"{date} {minutes_to_time(minute)} EST" for minute in day_data['interval_group']]
            fig.add_trace(go.Scatter(
                x=day_data['interval_group'],
                y=day_data['tweet_count'],
                mode='lines',
                name=str(date),
                customdata=hover_times,
                hovertemplate='%{customdata}<br>Tweets: %{y}<extra></extra>'
            ))

    elif tab == 'heatmap':
        pivot_data = agg_data.pivot(index='date', columns='interval_group', values='tweet_count').fillna(0)
        pivot_data.index = pivot_data.index.astype(str)
        fig = go.Figure(data=go.Heatmap(
            z=pivot_data.values,
            x=[minutes_to_time(m) for m in pivot_data.columns],
            y=pivot_data.index,
            colorscale='Viridis',
            hoverongaps=False,
            hovertemplate='%{y} %{x} EST<br>Tweets: %{z}<extra></extra>'
        ))

        for i, date_str in enumerate(pivot_data.index):
            date = datetime.strptime(date_str, '%Y-%m-%d').date()
            if date.weekday() == 4:  # Friday
                prev_date = date - timedelta(days=1)
                if str(prev_date) in pivot_data.index:
                    y_position = i / len(pivot_data.index)
                    fig.add_hline(
                        y=1 - y_position,
                        line_dash="dash",
                        line_color="white",
                        xref="x",
                        yref="paper"
                    )
        fig.update_layout(
            title=f'Tweet Heatmap (Interval: {interval} minutes, EST, {len(selected_dates)} days)',
            xaxis_title='Time of Day (HH:MM EST)',
            yaxis_title='Date',
            height=max(400, len(selected_dates) * 20),
            yaxis=dict(autorange='reversed')
        )

    elif tab == 'one_day_heatmap':
        one_day_data = agg_data.groupby('interval_group')['tweet_count'].sum().reset_index()

        hours = list(range(24))
        intervals_per_hour = 60 // interval
        z_values = [[0] * intervals_per_hour for _ in range(24)]

        for _, row in one_day_data.iterrows():
            minute = row['interval_group']
            hour = int(minute // 60)  # Convert to integer
            interval_idx = int((minute % 60) // interval)  # Convert to integer
            if hour < 24:
                z_values[hour][interval_idx] = row['tweet_count']

        x_labels = [f"{i * interval:02d}" for i in range(intervals_per_hour)]

        rate_values = [[z_values[h][i] / tweet_count_total if tweet_count_total > 0 else 0
                        for i in range(intervals_per_hour)]
                       for h in range(24)]

        fig = go.Figure(data=go.Heatmap(
            z=z_values,
            x=x_labels,
            y=[f"{h:02d}" for h in hours],
            colorscale='Viridis',
            hoverongaps=False,
            customdata=rate_values,
            hovertemplate='%{y}:%{x} EST<br>Tweets: %{z}<br>Rate: %{customdata:.2%}<extra></extra>'
        ))

    if tab in ['line', 'one_day_heatmap']:
        fig.update_layout(
            title=f'{"Line" if tab == "line" else "One-Day Heatmap"} Tweet Frequency (Interval: {interval} minutes, EST, {len(selected_dates)} days)',
            xaxis_title='Minutes' if tab == 'one_day_heatmap' else 'Eastern Time (HH:MM)',
            yaxis_title='Hour of Day' if tab == 'one_day_heatmap' else 'Tweet Count',
            xaxis=dict(
                range=[0, 1440] if tab == 'line' else None,
                tickvals=xticks if tab == 'line' else None,
                ticktext=xtick_labels if tab == 'line' else None,
                tickangle=45 if tab == 'line' else 0
            ),
            height=600,
            showlegend=(tab == 'line'),
            yaxis=dict(autorange='reversed') if tab == 'one_day_heatmap' else None
        )

    summary = f"Total tweets for selected dates: {int(tweet_count_total)}Total tweets: {get_tweets_since_last_friday()}"
    return dcc.Graph(figure=fig), warning, summary