"""
Demographic visualization charts for sentiment analysis
Handles age, timezone, and experience level visualizations
"""
import plotly.graph_objects as go
import plotly.express as px
import json
from pathlib import Path


class DemographicCharts:
    """
    Creates demographic-related visualizations for musora_app data
    """

    def __init__(self):
        """Initialize with configuration"""
        config_path = Path(__file__).parent.parent / "config" / "viz_config.json"
        with open(config_path, 'r') as f:
            self.config = json.load(f)

        self.sentiment_colors = self.config['color_schemes']['sentiment_polarity']
        self.sentiment_order = self.config['sentiment_order']
        self.chart_height = self.config['dashboard']['chart_height']

    def create_age_distribution_chart(self, age_dist_df, title="Age Distribution"):
        """
        Create bar chart for age group distribution

        Args:
            age_dist_df: DataFrame with age_group, count, percentage columns
            title: Chart title

        Returns:
            plotly.graph_objects.Figure
        """
        if age_dist_df.empty:
            return self._create_empty_chart(title, "No demographic data available")

        # Define custom age group order
        age_order = ['18-24', '25-34', '35-44', '45-54', '55+']

        # Sort by custom order
        age_dist_df['age_group'] = pd.Categorical(
            age_dist_df['age_group'],
            categories=age_order,
            ordered=True
        )
        age_dist_df = age_dist_df.sort_values('age_group')

        fig = go.Figure()

        fig.add_trace(go.Bar(
            x=age_dist_df['age_group'],
            y=age_dist_df['count'],
            text=age_dist_df.apply(lambda row: f"{row['count']}<br>({row['percentage']:.1f}%)", axis=1),
            textposition='auto',
            marker=dict(
                color='#4A90E2',
                line=dict(color='#2E5C8A', width=1)
            ),
            hovertemplate='<b>%{x}</b><br>Comments: %{y}<br>Percentage: %{customdata:.1f}%<extra></extra>',
            customdata=age_dist_df['percentage']
        ))

        fig.update_layout(
            title=title,
            xaxis_title="Age Group",
            yaxis_title="Number of Comments",
            height=self.chart_height,
            showlegend=False,
            hovermode='x'
        )

        return fig

    def create_age_sentiment_chart(self, age_sentiment_df, title="Sentiment by Age Group"):
        """
        Create stacked bar chart showing sentiment distribution for each age group

        Args:
            age_sentiment_df: DataFrame with age_group, sentiment_polarity, count, percentage
            title: Chart title

        Returns:
            plotly.graph_objects.Figure
        """
        if age_sentiment_df.empty:
            return self._create_empty_chart(title, "No demographic data available")

        # Define custom age group order
        age_order = ['18-24', '25-34', '35-44', '45-54', '55+']

        fig = go.Figure()

        # Create a trace for each sentiment
        for sentiment in self.sentiment_order:
            sentiment_data = age_sentiment_df[age_sentiment_df['sentiment_polarity'] == sentiment]

            if not sentiment_data.empty:
                fig.add_trace(go.Bar(
                    name=sentiment.replace('_', ' ').title(),
                    x=sentiment_data['age_group'],
                    y=sentiment_data['percentage'],
                    marker=dict(color=self.sentiment_colors.get(sentiment, '#999999')),
                    hovertemplate='<b>%{fullData.name}</b><br>Age: %{x}<br>Percentage: %{y:.1f}%<extra></extra>'
                ))

        fig.update_layout(
            title=title,
            xaxis=dict(
                title="Age Group",
                categoryorder='array',
                categoryarray=age_order
            ),
            yaxis=dict(
                title="Percentage (%)",
                range=[0, 100]
            ),
            barmode='stack',
            height=self.chart_height,
            hovermode='x unified',
            legend=dict(
                orientation="h",
                yanchor="bottom",
                y=1.02,
                xanchor="right",
                x=1
            )
        )

        return fig

    def create_timezone_chart(self, timezone_df, title="Top Timezones", top_n=15):
        """
        Create horizontal bar chart for top timezones

        Args:
            timezone_df: DataFrame with timezone, count, percentage columns
            title: Chart title
            top_n: Number of top timezones to display

        Returns:
            plotly.graph_objects.Figure
        """
        if timezone_df.empty:
            return self._create_empty_chart(title, "No timezone data available")

        # Take top N and reverse for better display (highest at top)
        display_df = timezone_df.head(top_n).iloc[::-1]

        fig = go.Figure()

        fig.add_trace(go.Bar(
            y=display_df['timezone'],
            x=display_df['count'],
            orientation='h',
            text=display_df.apply(lambda row: f"{row['count']} ({row['percentage']:.1f}%)", axis=1),
            textposition='auto',
            marker=dict(
                color='#50C878',
                line=dict(color='#2E7D4E', width=1)
            ),
            hovertemplate='<b>%{y}</b><br>Comments: %{x}<br>Percentage: %{customdata:.1f}%<extra></extra>',
            customdata=display_df['percentage']
        ))

        fig.update_layout(
            title=title,
            xaxis_title="Number of Comments",
            yaxis_title="Timezone",
            height=max(self.chart_height, top_n * 25),  # Dynamic height based on number of timezones
            showlegend=False,
            hovermode='y'
        )

        return fig

    def create_region_distribution_chart(self, region_df, title="Distribution by Region"):
        """
        Create pie chart for timezone region distribution

        Args:
            region_df: DataFrame with timezone_region, count, percentage columns
            title: Chart title

        Returns:
            plotly.graph_objects.Figure
        """
        if region_df.empty:
            return self._create_empty_chart(title, "No region data available")

        # Define color palette for regions
        colors = px.colors.qualitative.Set3

        fig = go.Figure()

        fig.add_trace(go.Pie(
            labels=region_df['timezone_region'],
            values=region_df['count'],
            textinfo='label+percent',
            hovertemplate='<b>%{label}</b><br>Comments: %{value}<br>Percentage: %{percent}<extra></extra>',
            marker=dict(colors=colors)
        ))

        fig.update_layout(
            title=title,
            height=self.chart_height,
            showlegend=True,
            legend=dict(
                orientation="v",
                yanchor="middle",
                y=0.5,
                xanchor="left",
                x=1
            )
        )

        return fig

    def create_region_sentiment_chart(self, region_sentiment_df, title="Sentiment by Region"):
        """
        Create grouped bar chart showing sentiment distribution for each region

        Args:
            region_sentiment_df: DataFrame with timezone_region, sentiment_polarity, count, percentage
            title: Chart title

        Returns:
            plotly.graph_objects.Figure
        """
        if region_sentiment_df.empty:
            return self._create_empty_chart(title, "No region sentiment data available")

        fig = go.Figure()

        # Create a trace for each sentiment
        for sentiment in self.sentiment_order:
            sentiment_data = region_sentiment_df[region_sentiment_df['sentiment_polarity'] == sentiment]

            if not sentiment_data.empty:
                fig.add_trace(go.Bar(
                    name=sentiment.replace('_', ' ').title(),
                    x=sentiment_data['timezone_region'],
                    y=sentiment_data['percentage'],
                    marker=dict(color=self.sentiment_colors.get(sentiment, '#999999')),
                    hovertemplate='<b>%{fullData.name}</b><br>Region: %{x}<br>Percentage: %{y:.1f}%<extra></extra>'
                ))

        fig.update_layout(
            title=title,
            xaxis_title="Region",
            yaxis=dict(
                title="Percentage (%)",
                range=[0, 100]
            ),
            barmode='stack',
            height=self.chart_height,
            hovermode='x unified',
            legend=dict(
                orientation="h",
                yanchor="bottom",
                y=1.02,
                xanchor="right",
                x=1
            )
        )

        return fig

    def create_experience_distribution_chart(self, exp_df, title="Experience Level Distribution", use_groups=False):
        """
        Create bar chart for experience level distribution

        Args:
            exp_df: DataFrame with experience_level/experience_group, count, percentage columns
            title: Chart title
            use_groups: If True, display grouped experience levels

        Returns:
            plotly.graph_objects.Figure
        """
        if exp_df.empty:
            return self._create_empty_chart(title, "No experience data available")

        field = 'experience_group' if use_groups else 'experience_level'

        # Define custom order for grouped experience
        if use_groups:
            exp_order = ['Beginner (0-3)', 'Intermediate (4-7)', 'Advanced (8-10)']
            exp_df[field] = pd.Categorical(
                exp_df[field],
                categories=exp_order,
                ordered=True
            )
            exp_df = exp_df.sort_values(field)
        else:
            # Sort by experience level numerically
            exp_df = exp_df.sort_values(field)

        fig = go.Figure()

        fig.add_trace(go.Bar(
            x=exp_df[field],
            y=exp_df['count'],
            text=exp_df.apply(lambda row: f"{row['count']}<br>({row['percentage']:.1f}%)", axis=1),
            textposition='auto',
            marker=dict(
                color='#9B59B6',
                line=dict(color='#6C3483', width=1)
            ),
            hovertemplate='<b>%{x}</b><br>Comments: %{y}<br>Percentage: %{customdata:.1f}%<extra></extra>',
            customdata=exp_df['percentage']
        ))

        fig.update_layout(
            title=title,
            xaxis_title="Experience Level" if not use_groups else "Experience Group",
            yaxis_title="Number of Comments",
            height=self.chart_height,
            showlegend=False,
            hovermode='x'
        )

        return fig

    def create_experience_sentiment_chart(self, exp_sentiment_df, title="Sentiment by Experience Level", use_groups=False):
        """
        Create stacked bar chart showing sentiment distribution for each experience level

        Args:
            exp_sentiment_df: DataFrame with experience_level/experience_group, sentiment_polarity, count, percentage
            title: Chart title
            use_groups: If True, use grouped experience levels

        Returns:
            plotly.graph_objects.Figure
        """
        if exp_sentiment_df.empty:
            return self._create_empty_chart(title, "No experience sentiment data available")

        field = 'experience_group' if use_groups else 'experience_level'

        fig = go.Figure()

        # Create a trace for each sentiment
        for sentiment in self.sentiment_order:
            sentiment_data = exp_sentiment_df[exp_sentiment_df['sentiment_polarity'] == sentiment]

            if not sentiment_data.empty:
                fig.add_trace(go.Bar(
                    name=sentiment.replace('_', ' ').title(),
                    x=sentiment_data[field],
                    y=sentiment_data['percentage'],
                    marker=dict(color=self.sentiment_colors.get(sentiment, '#999999')),
                    hovertemplate='<b>%{fullData.name}</b><br>Experience: %{x}<br>Percentage: %{y:.1f}%<extra></extra>'
                ))

        # Define custom order for grouped experience
        if use_groups:
            exp_order = ['Beginner (0-3)', 'Intermediate (4-7)', 'Advanced (8-10)']
            xaxis_config = dict(
                title="Experience Group",
                categoryorder='array',
                categoryarray=exp_order
            )
        else:
            xaxis_config = dict(title="Experience Level")

        fig.update_layout(
            title=title,
            xaxis=xaxis_config,
            yaxis=dict(
                title="Percentage (%)",
                range=[0, 100]
            ),
            barmode='stack',
            height=self.chart_height,
            hovermode='x unified',
            legend=dict(
                orientation="h",
                yanchor="bottom",
                y=1.02,
                xanchor="right",
                x=1
            )
        )

        return fig

    def create_demographics_heatmap(self, df, row_field, col_field, title="Demographics Heatmap"):
        """
        Create heatmap for cross-demographic analysis

        Args:
            df: DataFrame with demographic fields and sentiment
            row_field: Field for rows (e.g., 'age_group')
            col_field: Field for columns (e.g., 'experience_group')
            title: Chart title

        Returns:
            plotly.graph_objects.Figure
        """
        if df.empty:
            return self._create_empty_chart(title, "No data available for heatmap")

        # Create pivot table
        pivot = df.pivot_table(
            index=row_field,
            columns=col_field,
            values='count',
            aggfunc='sum',
            fill_value=0
        )

        fig = go.Figure(data=go.Heatmap(
            z=pivot.values,
            x=pivot.columns,
            y=pivot.index,
            colorscale='Blues',
            text=pivot.values,
            texttemplate='%{text}',
            textfont={"size": 10},
            hovertemplate='<b>%{y}</b> × <b>%{x}</b><br>Comments: %{z}<extra></extra>'
        ))

        fig.update_layout(
            title=title,
            xaxis_title=col_field.replace('_', ' ').title(),
            yaxis_title=row_field.replace('_', ' ').title(),
            height=self.chart_height
        )

        return fig

    def _create_empty_chart(self, title, message):
        """
        Create an empty chart with a message

        Args:
            title: Chart title
            message: Message to display

        Returns:
            plotly.graph_objects.Figure
        """
        fig = go.Figure()

        fig.add_annotation(
            text=message,
            xref="paper",
            yref="paper",
            x=0.5,
            y=0.5,
            showarrow=False,
            font=dict(size=14, color="gray")
        )

        fig.update_layout(
            title=title,
            height=self.chart_height,
            xaxis=dict(visible=False),
            yaxis=dict(visible=False)
        )

        return fig


# Import pandas for use in methods (needed for Categorical)
import pandas as pd