visualization/visualizations/sentiment_charts.py

"""
Sentiment visualization components using Plotly
Creates interactive charts for sentiment analysis
"""
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 SentimentCharts:
    """
    Creates sentiment-related 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.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_sentiment_pie_chart(self, df, title="Sentiment Distribution"):
        """
        Create pie chart for sentiment distribution

        Args:
            df: Sentiment dataframe
            title: Chart title

        Returns:
            plotly.graph_objects.Figure
        """
        sentiment_counts = df['sentiment_polarity'].value_counts()

        # Order by sentiment_order
        ordered_sentiments = [s for s in self.sentiment_order if s in sentiment_counts.index]
        sentiment_counts = sentiment_counts[ordered_sentiments]

        colors = [self.sentiment_colors.get(s, '#CCCCCC') for s in sentiment_counts.index]

        fig = go.Figure(data=[go.Pie(
            labels=sentiment_counts.index,
            values=sentiment_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_sentiment_bar_chart(self, df, group_by, title="Sentiment Distribution"):
        """
        Create stacked bar chart for sentiment distribution by group

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

        Returns:
            plotly.graph_objects.Figure
        """
        # Create pivot table
        sentiment_pivot = pd.crosstab(df[group_by], df['sentiment_polarity'])

        # Reorder columns by sentiment_order
        ordered_columns = [s for s in self.sentiment_order if s in sentiment_pivot.columns]
        sentiment_pivot = sentiment_pivot[ordered_columns]

        fig = go.Figure()

        for sentiment in sentiment_pivot.columns:
            fig.add_trace(go.Bar(
                name=sentiment,
                x=sentiment_pivot.index,
                y=sentiment_pivot[sentiment],
                marker_color=self.sentiment_colors.get(sentiment, '#CCCCCC'),
                hovertemplate='<b>%{x}</b><br>%{y} comments<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="Sentiment", orientation="v", yanchor="top", y=1, xanchor="left", x=1.02)
        )

        return fig

    def create_sentiment_percentage_bar_chart(self, df, group_by, title="Sentiment Distribution (%)"):
        """
        Create 100% stacked bar chart for sentiment distribution

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

        Returns:
            plotly.graph_objects.Figure
        """
        # Create pivot table with percentages
        sentiment_pivot = pd.crosstab(df[group_by], df['sentiment_polarity'], normalize='index') * 100

        # Reorder columns by sentiment_order
        ordered_columns = [s for s in self.sentiment_order if s in sentiment_pivot.columns]
        sentiment_pivot = sentiment_pivot[ordered_columns]

        fig = go.Figure()

        for sentiment in sentiment_pivot.columns:
            fig.add_trace(go.Bar(
                name=sentiment,
                x=sentiment_pivot.index,
                y=sentiment_pivot[sentiment],
                marker_color=self.sentiment_colors.get(sentiment, '#CCCCCC'),
                hovertemplate='<b>%{x}</b><br>%{y:.1f}%<extra></extra>'
            ))

        fig.update_layout(
            title=title,
            xaxis_title=group_by.capitalize(),
            yaxis_title="Percentage (%)",
            barmode='stack',
            height=self.chart_height,
            yaxis=dict(range=[0, 100]),
            legend=dict(title="Sentiment", orientation="v", yanchor="top", y=1, xanchor="left", x=1.02)
        )

        return fig

    def create_sentiment_heatmap(self, df, row_dimension, col_dimension, title="Sentiment Heatmap"):
        """
        Create heatmap showing sentiment distribution across two dimensions

        Args:
            df: Sentiment dataframe
            row_dimension: Row dimension
            col_dimension: Column dimension
            title: Chart title

        Returns:
            plotly.graph_objects.Figure
        """
        # Create pivot table for negative sentiment percentage
        negative_sentiments = self.config['negative_sentiments']
        df_negative = df[df['sentiment_polarity'].isin(negative_sentiments)]

        heatmap_data = pd.crosstab(
            df[row_dimension],
            df[col_dimension],
            values=(df['sentiment_polarity'].isin(negative_sentiments)).astype(int),
            aggfunc='mean'
        ) * 100

        fig = go.Figure(data=go.Heatmap(
            z=heatmap_data.values,
            x=heatmap_data.columns,
            y=heatmap_data.index,
            colorscale='RdYlGn_r',
            text=heatmap_data.values.round(1),
            texttemplate='%{text}%',
            textfont={"size": 12},
            hovertemplate='<b>%{y} - %{x}</b><br>Negative: %{z:.1f}%<extra></extra>',
            colorbar=dict(title="Negative %")
        ))

        fig.update_layout(
            title=title,
            xaxis_title=col_dimension.capitalize(),
            yaxis_title=row_dimension.capitalize(),
            height=self.chart_height
        )

        return fig

    def create_sentiment_timeline(self, df, freq='D', title="Sentiment Over Time"):
        """
        Create line chart showing sentiment trends over time

        Args:
            df: Sentiment dataframe with comment_timestamp
            freq: Frequency for aggregation ('D', 'W', 'M')
            title: Chart title

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

        df_temp = df.copy()
        df_temp['date'] = pd.to_datetime(df_temp['comment_timestamp']).dt.to_period(freq).dt.to_timestamp()

        # Aggregate by date and sentiment
        timeline_data = df_temp.groupby(['date', 'sentiment_polarity']).size().reset_index(name='count')

        fig = go.Figure()

        for sentiment in self.sentiment_order:
            sentiment_data = timeline_data[timeline_data['sentiment_polarity'] == sentiment]
            if not sentiment_data.empty:
                fig.add_trace(go.Scatter(
                    x=sentiment_data['date'],
                    y=sentiment_data['count'],
                    name=sentiment,
                    mode='lines+markers',
                    line=dict(color=self.sentiment_colors.get(sentiment, '#CCCCCC'), width=2),
                    marker=dict(size=6),
                    hovertemplate='<b>%{x}</b><br>Count: %{y}<extra></extra>'
                ))

        fig.update_layout(
            title=title,
            xaxis_title="Date",
            yaxis_title="Number of Comments",
            height=self.chart_height,
            legend=dict(title="Sentiment", orientation="v", yanchor="top", y=1, xanchor="left", x=1.02),
            hovermode='x unified'
        )

        return fig

    def create_sentiment_score_gauge(self, avg_score, title="Overall Sentiment Score"):
        """
        Create gauge chart for average sentiment score

        Args:
            avg_score: Average sentiment score (-2 to +2)
            title: Chart title

        Returns:
            plotly.graph_objects.Figure
        """
        # Normalize score to 0-100 scale
        normalized_score = ((avg_score + 2) / 4) * 100

        fig = go.Figure(go.Indicator(
            mode="gauge+number+delta",
            value=normalized_score,
            domain={'x': [0, 1], 'y': [0, 1]},
            title={'text': title, 'font': {'size': 20}},
            number={'suffix': '', 'font': {'size': 40}},
            gauge={
                'axis': {'range': [0, 100], 'tickwidth': 1, 'tickcolor': "darkblue"},
                'bar': {'color': "darkblue"},
                'bgcolor': "white",
                'borderwidth': 2,
                'bordercolor': "gray",
                'steps': [
                    {'range': [0, 20], 'color': '#D32F2F'},
                    {'range': [20, 40], 'color': '#FF6F00'},
                    {'range': [40, 60], 'color': '#FFB300'},
                    {'range': [60, 80], 'color': '#7CB342'},
                    {'range': [80, 100], 'color': '#00C851'}
                ],
                'threshold': {
                    'line': {'color': "black", 'width': 4},
                    'thickness': 0.75,
                    'value': normalized_score
                }
            }
        ))

        fig.update_layout(
            height=300,
            margin=dict(l=20, r=20, t=60, b=20)
        )

        return fig