"""
Visualization module for AMA-Bench leaderboard
Adapted from lmGAME_bench patterns with AMA-specific customizations
"""

import plotly.graph_objects as go
import numpy as np
import pandas as pd
import json
import os
from typing import Dict, List, Optional, Tuple


# Constants
METRICS = ["Recall", "Causal Inference", "State Updating", "State Abstraction"]
ALL_METRICS = METRICS + ["Average"]


def load_model_colors(filepath: str = "assets/model_colors.json") -> Dict[str, str]:
    """
    Load color scheme for models and methods from JSON file.

    Args:
        filepath: Path to color configuration JSON

    Returns:
        Dictionary mapping model/method names to hex colors
    """
    try:
        with open(filepath, 'r', encoding='utf-8') as f:
            color_data = json.load(f)

        # Merge models and methods into single dictionary
        colors = {}
        if 'models' in color_data:
            colors.update(color_data['models'])
        if 'methods' in color_data:
            colors.update(color_data['methods'])

        # Store fallback color
        fallback = color_data.get('fallback', '#808080')

        return colors, fallback
    except Exception as e:
        print(f"Warning: Could not load colors from {filepath}: {e}")
        return {}, '#808080'


def normalize_scores(values: List[float], mean: float, std: float) -> List[float]:
    """
    Normalize scores using z-score and scale to 0-100 range.
    Adapted from lmGAME_bench's normalize_values() function.

    Args:
        values: List of accuracy values (0-1 range)
        mean: Mean value for normalization
        std: Standard deviation for normalization

    Returns:
        List of normalized scores (0-100 range)

    Formula:
        z_score = (value - mean) / std
        normalized = clamp((z_score * 30) + 35, 0, 100)
    """
    # Handle zero std case (all values are the same)
    if std < 0.05:  # Minimum std threshold to prevent extreme values
        std = 0.05

    normalized = []
    for v in values:
        z_score = (v - mean) / std
        scaled = (z_score * 30) + 35
        clamped = max(0, min(100, scaled))
        normalized.append(clamped)

    return normalized


def filter_by_category(data: Dict, category: str) -> Dict:
    """
    Filter method data by category.

    Args:
        data: Full dataset with entries
        category: "All", "RAG", or "Agent Memory"

    Returns:
        Filtered data dictionary
    """
    if category == "All":
        return data

    filtered_data = data.copy()
    filtered_data['entries'] = [
        entry for entry in data['entries']
        if entry.get('category') == category
    ]

    return filtered_data


def prepare_dataframe_for_visualization(
    data: Dict,
    top_n: Optional[int] = None,
    category_filter: str = "All",
    selected_metrics: Optional[List[str]] = None
) -> pd.DataFrame:
    """
    Build DataFrame with both raw and normalized scores.

    Args:
        data: Raw data from model_data.json or method_data.json
        top_n: Number of top entries to include (None = all)
        category_filter: "All", "RAG", or "Agent Memory" (for methods only)
        selected_metrics: List of metrics to include (None = all)

    Returns:
        DataFrame with columns:
        - Method/Model (name)
        - Category (if applicable)
        - {Metric} (raw accuracy 0-1) for each metric
        - norm_{Metric} (normalized 0-100) for each metric
        - Avg Normalized Score (mean of normalized scores)
    """
    # Filter by category first
    if category_filter != "All":
        data = filter_by_category(data, category_filter)

    if not data['entries']:
        # Return empty DataFrame if no entries
        return pd.DataFrame()

    # Use all metrics if none specified
    if selected_metrics is None:
        selected_metrics = METRICS

    # Build basic DataFrame
    rows = []
    for entry in data['entries']:
        row = {
            'Name': entry['method'],
        }

        # Add category if present
        if entry.get('category') is not None:
            row['Category'] = entry['category']

        # Add raw scores
        for metric in selected_metrics:
            score_data = entry['scores'].get(metric, {})
            row[metric] = score_data.get('accuracy', 0.0)

        # Add average
        row['Average'] = entry['scores'].get('Average', {}).get('accuracy', 0.0)

        rows.append(row)

    df = pd.DataFrame(rows)

    # Sort by average accuracy (descending)
    df = df.sort_values(by='Average', ascending=False)

    # Calculate normalization parameters from FULL dataset (before limiting)
    norm_params = {}
    for metric in selected_metrics:
        values = df[metric].values
        mean = values.mean()
        std = values.std()
        norm_params[metric] = (mean, std)

    # Apply top_n limit if specified
    if top_n is not None and top_n > 0:
        df = df.head(top_n)

    # Add normalized scores
    for metric in selected_metrics:
        mean, std = norm_params[metric]
        values = df[metric].values
        df[f'norm_{metric}'] = normalize_scores(values.tolist(), mean, std)

    # Calculate average normalized score
    norm_cols = [f'norm_{metric}' for metric in selected_metrics]
    df['Avg Normalized Score'] = df[norm_cols].mean(axis=1)

    # Reset index
    df = df.reset_index(drop=True)

    return df


def hex_to_rgba(hex_color: str, alpha: float = 0.2) -> str:
    """
    Convert hex color to RGBA with specified alpha.

    Args:
        hex_color: Hex color code (e.g., "#FF0000")
        alpha: Alpha value (0-1)

    Returns:
        RGBA color string
    """
    hex_color = hex_color.lstrip('#')
    r = int(hex_color[0:2], 16)
    g = int(hex_color[2:4], 16)
    b = int(hex_color[4:6], 16)
    return f'rgba({r}, {g}, {b}, {alpha})'


def create_radar_chart(
    df: pd.DataFrame,
    selected_metrics: List[str],
    title: str = "Performance Across Metrics",
    color_map: Optional[Dict[str, str]] = None
) -> go.Figure:
    """
    Create radar chart with normalized scores.
    Adapted from lmGAME_bench's create_single_radar_chart().

    Args:
        df: DataFrame from prepare_dataframe_for_visualization()
        selected_metrics: List of metric names to include as axes
        title: Chart title
        color_map: Dictionary mapping names to colors

    Returns:
        Plotly Figure with radar chart

    Features:
        - Each axis = one metric
        - Each trace = one model/method
        - Range: 0-100 (normalized)
        - Interactive legend (click to isolate, double-click to toggle)
    """
    if df.empty:
        fig = go.Figure()
        fig.update_layout(title="No data available")
        return fig

    # Load colors if not provided
    if color_map is None:
        color_map, fallback_color = load_model_colors()
    else:
        fallback_color = '#808080'

    # Check if we have normalized columns
    norm_cols = [f'norm_{metric}' for metric in selected_metrics]
    if not all(col in df.columns for col in norm_cols):
        fig = go.Figure()
        fig.update_layout(title="Missing normalized data")
        return fig

    fig = go.Figure()

    # Add trace for each model/method
    for _, row in df.iterrows():
        name = row['Name']

        # Get normalized values for selected metrics
        r = [row[f'norm_{metric}'] for metric in selected_metrics]

        # Get color
        color = color_map.get(name, fallback_color)
        fillcolor = hex_to_rgba(color, 0.2)

        # Add trace
        fig.add_trace(go.Scatterpolar(
            r=r + [r[0]],  # Close the polygon
            theta=selected_metrics + [selected_metrics[0]],
            mode='lines+markers',
            fill='toself',
            name=name.lower(),  # Lowercase for legend
            line=dict(color=color, width=2),
            marker=dict(color=color, size=6),
            fillcolor=fillcolor,
            opacity=0.7,
            hovertemplate='<b>%{fullData.name}</b><br>%{theta}: %{r:.1f}<extra></extra>'
        ))

    # Update layout
    fig.update_layout(
        title=dict(
            text=title,
            x=0.5,
            xanchor='center',
            font=dict(size=18)
        ),
        polar=dict(
            radialaxis=dict(
                visible=True,
                range=[0, 100],
                tickfont=dict(size=11),
                gridcolor='lightgray',
                gridwidth=1
            ),
            angularaxis=dict(
                tickfont=dict(size=12, weight='bold')
            )
        ),
        legend=dict(
            font=dict(size=11),
            title=dict(text="Models/Methods 💡", font=dict(size=12)),
            itemsizing='trace',
            x=1.05,
            y=1,
            xanchor='left',
            yanchor='top',
            bgcolor='rgba(255,255,255,0.6)',
            bordercolor='gray',
            borderwidth=1,
            itemclick="toggleothers",
            itemdoubleclick="toggle"
        ),
        height=550,
        margin=dict(l=80, r=200, t=80, b=80)
    )

    return fig


def create_group_bar_chart(
    df: pd.DataFrame,
    selected_metrics: List[str],
    top_n: int = 5,
    color_map: Optional[Dict[str, str]] = None
) -> go.Figure:
    """
    Create grouped bar chart showing top N performers per metric.
    Adapted from lmGAME_bench's create_group_bar_chart().

    Args:
        df: DataFrame with normalized scores
        selected_metrics: List of metrics to display
        top_n: Number of top performers to show per metric
        color_map: Dictionary mapping names to colors

    Returns:
        Plotly Figure with grouped bar chart

    Structure:
        - X-axis: Metrics with rank positions (e.g., "Recall #1", "Recall #2")
        - Y-axis: Normalized score (0-100)
        - Bars: Grouped by model/method
    """
    if df.empty:
        fig = go.Figure()
        fig.update_layout(title="No data available")
        return fig

    # Load colors if not provided
    if color_map is None:
        color_map, fallback_color = load_model_colors()
    else:
        fallback_color = '#808080'

    # Check for normalized columns
    norm_cols = [f'norm_{metric}' for metric in selected_metrics]
    if not all(col in df.columns for col in norm_cols):
        fig = go.Figure()
        fig.update_layout(title="Missing normalized data")
        return fig

    # Build x-axis categories and data structure
    all_x_categories = []
    all_names = set()
    metric_rankings = {}

    for metric in selected_metrics:
        norm_col = f'norm_{metric}'

        # Get top N for this metric
        metric_df = df[df[norm_col].notna()].copy()
        metric_df = metric_df.sort_values(by=norm_col, ascending=False).head(top_n)

        metric_rankings[metric] = []
        for rank, (_, row) in enumerate(metric_df.iterrows(), 1):
            name = row['Name']
            score = row[norm_col]
            x_category = f"{metric}<br>#{rank}"

            metric_rankings[metric].append({
                'name': name,
                'score': score,
                'x_category': x_category,
                'rank': rank
            })

            all_x_categories.append(x_category)
            all_names.add(name)

    # Create traces for each model/method
    fig = go.Figure()

    for name in sorted(all_names):
        x_vals = []
        y_vals = []

        for metric in selected_metrics:
            # Find this model/method's data for this metric
            for data in metric_rankings[metric]:
                if data['name'] == name:
                    x_vals.append(data['x_category'])
                    y_vals.append(data['score'])
                    break

        if x_vals:  # Only add if has data
            color = color_map.get(name, fallback_color)
            fig.add_trace(go.Bar(
                name=name,
                x=x_vals,
                y=y_vals,
                marker_color=color,
                hovertemplate="<b>%{fullData.name}</b><br>Score: %{y:.1f}<extra></extra>"
            ))

    # Update layout
    fig.update_layout(
        title=dict(
            text=f"Top {top_n} Performers by Metric",
            x=0.5,
            xanchor='center',
            font=dict(size=18)
        ),
        xaxis_title="Metrics (Ranked by Performance)",
        yaxis_title="Normalized Score",
        xaxis=dict(
            categoryorder='array',
            categoryarray=all_x_categories,
            tickangle=0
        ),
        yaxis=dict(range=[0, 100]),
        barmode='group',
        bargap=0.15,
        bargroupgap=0.1,
        height=550,
        margin=dict(l=60, r=200, t=80, b=80),
        legend=dict(
            font=dict(size=11),
            title=dict(text="Models/Methods 💡", font=dict(size=12)),
            itemsizing='trace',
            x=1.05,
            y=1,
            xanchor='left',
            yanchor='top',
            bgcolor='rgba(255,255,255,0.6)',
            bordercolor='gray',
            borderwidth=1
        )
    )

    return fig


def create_horizontal_bar_chart(
    df: pd.DataFrame,
    metric: str,
    color_map: Optional[Dict[str, str]] = None
) -> go.Figure:
    """
    Create horizontal bar chart for single metric details view.
    Adapted from lmGAME_bench's create_horizontal_bar_chart().

    Args:
        df: DataFrame with scores
        metric: Metric name (e.g., "Recall")
        color_map: Dictionary mapping names to colors

    Returns:
        Plotly Figure with horizontal bar chart

    Features:
        - Y-axis: Model/method names (sorted by score, descending)
        - X-axis: Raw accuracy score (0-1 range)
        - Uses raw scores, not normalized
    """
    if df.empty or metric not in df.columns:
        fig = go.Figure()
        fig.update_layout(title=f"No data available for {metric}")
        return fig

    # Load colors if not provided
    if color_map is None:
        color_map, fallback_color = load_model_colors()
    else:
        fallback_color = '#808080'

    # Filter and sort
    metric_df = df[df[metric].notna()].copy()
    metric_df = metric_df.sort_values(by=metric, ascending=True)  # Lowest at top

    if metric_df.empty:
        fig = go.Figure()
        fig.update_layout(title=f"No valid data for {metric}")
        return fig

    # Create bar chart
    colors = [color_map.get(name, fallback_color) for name in metric_df['Name']]

    fig = go.Figure(
        go.Bar(
            y=metric_df['Name'],
            x=metric_df[metric],
            orientation='h',
            marker=dict(
                color=colors,
                line=dict(color='#2c3e50', width=1)
            ),
            hovertemplate='%{y}<br>Accuracy: %{x:.4f}<extra></extra>'
        )
    )

    # Update layout
    fig.update_layout(
        title=dict(
            text=f'{metric} - Detailed Rankings',
            x=0.5,
            xanchor='center',
            font=dict(size=18)
        ),
        xaxis_title="Accuracy",
        yaxis_title="Model/Method",
        xaxis=dict(
            range=[0, 1],
            gridcolor='#e0e0e0'
        ),
        plot_bgcolor='rgba(0,0,0,0)',
        paper_bgcolor='rgba(0,0,0,0)',
        font=dict(color='#2c3e50'),
        height=max(400, len(metric_df) * 30),  # Dynamic height based on entries
        margin=dict(l=200, r=40, t=80, b=60),
        showlegend=False
    )

    return fig


def create_multi_metric_bar_chart(
    df: pd.DataFrame,
    selected_metrics: List[str],
    color_map: Optional[Dict[str, str]] = None
) -> go.Figure:
    """
    Create grouped horizontal bar chart showing multiple metrics for each model/method.

    Args:
        df: DataFrame with scores
        selected_metrics: List of metrics to display (e.g., ["Recall", "Causal Inference"])
        color_map: Dictionary mapping names to colors

    Returns:
        Plotly Figure with grouped horizontal bar chart

    Features:
        - Y-axis: Model/method names
        - X-axis: Raw accuracy score (0-1 range)
        - Multiple bars per model/method (one per selected metric)
        - Sorted by average score across selected metrics
    """
    if df.empty or not selected_metrics:
        fig = go.Figure()
        fig.update_layout(title="No data available")
        return fig

    # Check if all selected metrics exist
    missing_metrics = [m for m in selected_metrics if m not in df.columns]
    if missing_metrics:
        fig = go.Figure()
        fig.update_layout(title=f"Missing metrics: {', '.join(missing_metrics)}")
        return fig

    # Filter to entries that have at least one selected metric
    metric_df = df.copy()
    metric_df = metric_df[metric_df[selected_metrics].notna().any(axis=1)]

    if metric_df.empty:
        fig = go.Figure()
        fig.update_layout(title="No valid data for selected metrics")
        return fig

    # Calculate average score across selected metrics for sorting
    metric_df['avg_score'] = metric_df[selected_metrics].mean(axis=1)
    metric_df = metric_df.sort_values(by='avg_score', ascending=True)  # Lowest at top

    # Use single base color with gradient based on capability
    base_color = "#636EFA"  # Blue color

    # Normalize avg_score to create gradient (0.3 to 1.0 range for visibility)
    min_score = metric_df['avg_score'].min()
    max_score = metric_df['avg_score'].max()
    score_range = max_score - min_score if max_score > min_score else 1

    # Create color gradient based on model capability (higher score = deeper color)
    def get_gradient_color(score, min_val, max_val, score_range):
        """Generate color with gradient based on score"""
        # Normalize to 0-1 range, then scale to 0.3-1.0 for better visibility
        normalized = (score - min_val) / score_range if score_range > 0 else 0.5
        intensity = 0.3 + (normalized * 0.7)  # Range: 0.3 (light) to 1.0 (deep)

        # Convert base color to RGB and apply intensity with 50% opacity
        hex_color = base_color.lstrip('#')
        r = int(hex_color[0:2], 16)
        g = int(hex_color[2:4], 16)
        b = int(hex_color[4:6], 16)

        # Apply intensity to RGB values
        r = int(255 - (255 - r) * intensity)
        g = int(255 - (255 - g) * intensity)
        b = int(255 - (255 - b) * intensity)

        return f'rgba({r}, {g}, {b}, 0.5)'  # 50% transparency

    # Create grouped bar chart
    fig = go.Figure()

    for metric in selected_metrics:
        # Create color array for each model based on their avg_score
        colors = [
            get_gradient_color(row['avg_score'], min_score, max_score, score_range)
            for _, row in metric_df.iterrows()
        ]

        fig.add_trace(go.Bar(
            name=metric,
            y=metric_df['Name'],
            x=metric_df[metric],
            orientation='h',
            marker=dict(
                color=colors,
                line=dict(color='#2c3e50', width=0.5)
            ),
            hovertemplate=f'<b>%{{y}}</b><br>{metric}: %{{x:.4f}}<extra></extra>'
        ))

    # Update layout
    fig.update_layout(
        title=dict(
            text=f'Detailed Comparison - {", ".join(selected_metrics)}',
            x=0.5,
            xanchor='center',
            font=dict(size=18)
        ),
        xaxis_title="Accuracy",
        yaxis_title="Model/Method",
        xaxis=dict(
            range=[0, 1],
            gridcolor='#e0e0e0'
        ),
        barmode='group',
        plot_bgcolor='rgba(0,0,0,0)',
        paper_bgcolor='rgba(0,0,0,0)',
        font=dict(color='#2c3e50'),
        height=max(500, len(metric_df) * 40),  # Dynamic height
        margin=dict(l=200, r=40, t=80, b=80),
        legend=dict(
            orientation="h",
            yanchor="bottom",
            y=1.02,
            xanchor="center",
            x=0.5,
            font=dict(size=12)
        )
    )

    return fig