"""
Distribution visualization components using Plotly
Creates charts for intent, language, and other distributions
"""
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
import pandas as pd
import json
from pathlib import Path


class DistributionCharts:
    """
    Creates distribution visualizations
    """

    def __init__(self, config_path=None):
        """
        Initialize with configuration

        Args:
            config_path: Path to configuration file
        """
        if config_path is None:
            config_path = Path(__file__).parent.parent / "config" / "viz_config.json"

        with open(config_path, 'r') as f:
            self.config = json.load(f)

        self.intent_colors = self.config['color_schemes']['intent']
        self.platform_colors = self.config['color_schemes']['platform']
        self.brand_colors = self.config['color_schemes']['brand']
        self.intent_order = self.config['intent_order']
        self.chart_height = self.config['dashboard']['chart_height']

    def create_intent_bar_chart(self, df, title="Intent Distribution", orientation='h'):
        """
        Create horizontal bar chart for intent distribution (handles multi-label)

        Args:
            df: Sentiment dataframe
            title: Chart title
            orientation: 'h' for horizontal, 'v' for vertical

        Returns:
            plotly.graph_objects.Figure
        """
        # Explode intents
        df_exploded = df.copy()
        df_exploded['intent'] = df_exploded['intent'].str.split(',')
        df_exploded = df_exploded.explode('intent')
        df_exploded['intent'] = df_exploded['intent'].str.strip()

        # Count intents
        intent_counts = df_exploded['intent'].value_counts()

        # Order by intent_order
        ordered_intents = [i for i in self.intent_order if i in intent_counts.index]
        intent_counts = intent_counts[ordered_intents]

        colors = [self.intent_colors.get(i, '#CCCCCC') for i in intent_counts.index]

        if orientation == 'h':
            fig = go.Figure(data=[go.Bar(
                y=intent_counts.index,
                x=intent_counts.values,
                orientation='h',
                marker=dict(color=colors),
                text=intent_counts.values,
                textposition='auto',
                hovertemplate='<b>%{y}</b><br>Count: %{x}<extra></extra>'
            )])

            fig.update_layout(
                title=title,
                xaxis_title="Number of Comments",
                yaxis_title="Intent",
                height=self.chart_height,
                yaxis={'categoryorder': 'total ascending'}
            )
        else:
            fig = go.Figure(data=[go.Bar(
                x=intent_counts.index,
                y=intent_counts.values,
                marker=dict(color=colors),
                text=intent_counts.values,
                textposition='auto',
                hovertemplate='<b>%{x}</b><br>Count: %{y}<extra></extra>'
            )])

            fig.update_layout(
                title=title,
                xaxis_title="Intent",
                yaxis_title="Number of Comments",
                height=self.chart_height
            )

        return fig

    def create_intent_pie_chart(self, df, title="Intent Distribution"):
        """
        Create pie chart for intent distribution

        Args:
            df: Sentiment dataframe
            title: Chart title

        Returns:
            plotly.graph_objects.Figure
        """
        # Explode intents
        df_exploded = df.copy()
        df_exploded['intent'] = df_exploded['intent'].str.split(',')
        df_exploded = df_exploded.explode('intent')
        df_exploded['intent'] = df_exploded['intent'].str.strip()

        intent_counts = df_exploded['intent'].value_counts()

        # Order by intent_order
        ordered_intents = [i for i in self.intent_order if i in intent_counts.index]
        intent_counts = intent_counts[ordered_intents]

        colors = [self.intent_colors.get(i, '#CCCCCC') for i in intent_counts.index]

        fig = go.Figure(data=[go.Pie(
            labels=intent_counts.index,
            values=intent_counts.values,
            marker=dict(colors=colors),
            textinfo='label+percent',
            textposition='auto',
            hovertemplate='<b>%{label}</b><br>Count: %{value}<br>Percentage: %{percent}<extra></extra>'
        )])

        fig.update_layout(
            title=title,
            height=self.chart_height,
            showlegend=True,
            legend=dict(orientation="v", yanchor="middle", y=0.5, xanchor="left", x=1.05)
        )

        return fig

    def create_platform_distribution(self, df, title="Comments by Platform"):
        """
        Create bar chart for platform distribution

        Args:
            df: Sentiment dataframe
            title: Chart title

        Returns:
            plotly.graph_objects.Figure
        """
        platform_counts = df['platform'].value_counts()

        colors = [self.platform_colors.get(p, self.platform_colors['default']) for p in platform_counts.index]

        fig = go.Figure(data=[go.Bar(
            x=platform_counts.index,
            y=platform_counts.values,
            marker=dict(color=colors),
            text=platform_counts.values,
            textposition='auto',
            hovertemplate='<b>%{x}</b><br>Comments: %{y}<extra></extra>'
        )])

        fig.update_layout(
            title=title,
            xaxis_title="Platform",
            yaxis_title="Number of Comments",
            height=self.chart_height
        )

        return fig

    def create_brand_distribution(self, df, title="Comments by Brand"):
        """
        Create bar chart for brand distribution

        Args:
            df: Sentiment dataframe
            title: Chart title

        Returns:
            plotly.graph_objects.Figure
        """
        brand_counts = df['brand'].value_counts()

        colors = [self.brand_colors.get(b, self.brand_colors['default']) for b in brand_counts.index]

        fig = go.Figure(data=[go.Bar(
            x=brand_counts.index,
            y=brand_counts.values,
            marker=dict(color=colors),
            text=brand_counts.values,
            textposition='auto',
            hovertemplate='<b>%{x}</b><br>Comments: %{y}<extra></extra>'
        )])

        fig.update_layout(
            title=title,
            xaxis_title="Brand",
            yaxis_title="Number of Comments",
            height=self.chart_height
        )

        return fig

    def create_language_distribution(self, df, top_n=10, title="Language Distribution"):
        """
        Create bar chart for language distribution

        Args:
            df: Sentiment dataframe
            top_n: Number of top languages to show
            title: Chart title

        Returns:
            plotly.graph_objects.Figure
        """
        if 'detected_language' not in df.columns:
            return go.Figure().add_annotation(
                text="No language data available",
                xref="paper", yref="paper",
                x=0.5, y=0.5, showarrow=False
            )

        lang_counts = df['detected_language'].value_counts().head(top_n)

        fig = go.Figure(data=[go.Bar(
            x=lang_counts.index,
            y=lang_counts.values,
            marker=dict(color='#2196F3'),
            text=lang_counts.values,
            textposition='auto',
            hovertemplate='<b>%{x}</b><br>Comments: %{y}<extra></extra>'
        )])

        fig.update_layout(
            title=title,
            xaxis_title="Language",
            yaxis_title="Number of Comments",
            height=self.chart_height
        )

        return fig

    def create_combined_distribution_sunburst(self, df, title="Hierarchical Distribution"):
        """
        Create sunburst chart showing hierarchical distribution
        (Brand > Platform > Sentiment)

        Args:
            df: Sentiment dataframe
            title: Chart title

        Returns:
            plotly.graph_objects.Figure
        """
        # Prepare data for sunburst
        sunburst_data = df.groupby(['brand', 'platform', 'sentiment_polarity']).size().reset_index(name='count')

        fig = px.sunburst(
            sunburst_data,
            path=['brand', 'platform', 'sentiment_polarity'],
            values='count',
            title=title,
            height=500
        )

        fig.update_layout(
            margin=dict(t=50, l=0, r=0, b=0)
        )

        return fig

    def create_brand_platform_matrix(self, df, title="Brand-Platform Comment Matrix"):
        """
        Create heatmap showing comment distribution across brands and platforms

        Args:
            df: Sentiment dataframe
            title: Chart title

        Returns:
            plotly.graph_objects.Figure
        """
        # Create pivot table
        matrix_data = pd.crosstab(df['brand'], df['platform'])

        fig = go.Figure(data=go.Heatmap(
            z=matrix_data.values,
            x=matrix_data.columns,
            y=matrix_data.index,
            colorscale='Blues',
            text=matrix_data.values,
            texttemplate='%{text}',
            textfont={"size": 14},
            hovertemplate='<b>%{y} - %{x}</b><br>Comments: %{z}<extra></extra>',
            colorbar=dict(title="Comments")
        ))

        fig.update_layout(
            title=title,
            xaxis_title="Platform",
            yaxis_title="Brand",
            height=self.chart_height
        )

        return fig

    def create_reply_required_chart(self, df, group_by='brand', title="Comments Requiring Reply"):
        """
        Create stacked bar chart showing reply requirements

        Args:
            df: Sentiment dataframe
            group_by: Column to group by
            title: Chart title

        Returns:
            plotly.graph_objects.Figure
        """
        # Create aggregation
        reply_data = df.groupby([group_by, 'requires_reply']).size().reset_index(name='count')
        reply_pivot = reply_data.pivot(index=group_by, columns='requires_reply', values='count').fillna(0)

        fig = go.Figure()

        if False in reply_pivot.columns:
            fig.add_trace(go.Bar(
                name='No Reply Needed',
                x=reply_pivot.index,
                y=reply_pivot[False],
                marker_color='#81C784',
                hovertemplate='<b>%{x}</b><br>No Reply: %{y}<extra></extra>'
            ))

        if True in reply_pivot.columns:
            fig.add_trace(go.Bar(
                name='Reply Required',
                x=reply_pivot.index,
                y=reply_pivot[True],
                marker_color='#FF7043',
                hovertemplate='<b>%{x}</b><br>Reply Required: %{y}<extra></extra>'
            ))

        fig.update_layout(
            title=title,
            xaxis_title=group_by.capitalize(),
            yaxis_title="Number of Comments",
            barmode='stack',
            height=self.chart_height,
            legend=dict(title="Reply Status", orientation="v", yanchor="top", y=1, xanchor="left", x=1.02)
        )

        return fig

    def create_engagement_scatter(self, content_summary_df, title="Content Engagement Analysis"):
        """
        Create scatter plot showing content engagement

        Args:
            content_summary_df: DataFrame with content summary statistics
            title: Chart title

        Returns:
            plotly.graph_objects.Figure
        """
        fig = px.scatter(
            content_summary_df,
            x='total_comments',
            y='negative_percentage',
            size='reply_required_count',
            color='negative_percentage',
            hover_data=['content_description'],
            title=title,
            labels={
                'total_comments': 'Total Comments',
                'negative_percentage': 'Negative Sentiment %',
                'reply_required_count': 'Replies Required'
            },
            color_continuous_scale='RdYlGn_r',
            height=self.chart_height
        )

        fig.update_layout(
            xaxis_title="Total Comments",
            yaxis_title="Negative Sentiment %",
            coloraxis_colorbar=dict(title="Negative %")
        )

        return fig