import plotly.graph_objects as go

def generate_sankey_diagram():
    pipeline_metrics = {
        'masking_methods': ['random masking', 'pseudorandom masking', 'high-entropy masking'],
        'sampling_methods': ['inverse_transform sampling', 'exponential_minimum sampling', 'temperature sampling', 'greedy sampling'],
        'scores': {
            ('random masking', 'inverse_transform sampling'): {'detectability': 0.8, 'distortion': 0.2},
            ('random masking', 'exponential_minimum sampling'): {'detectability': 0.7, 'distortion': 0.3},
            ('random masking', 'temperature sampling'): {'detectability': 0.6, 'distortion': 0.4},
            ('random masking', 'greedy sampling'): {'detectability': 0.5, 'distortion': 0.5},
            ('pseudorandom masking', 'inverse_transform sampling'): {'detectability': 0.75, 'distortion': 0.25},
            ('pseudorandom masking', 'exponential_minimum sampling'): {'detectability': 0.65, 'distortion': 0.35},
            ('pseudorandom masking', 'temperature sampling'): {'detectability': 0.55, 'distortion': 0.45},
            ('pseudorandom masking', 'greedy sampling'): {'detectability': 0.45, 'distortion': 0.55},
            ('high-entropy masking', 'inverse_transform sampling'): {'detectability': 0.85, 'distortion': 0.15},
            ('high-entropy masking', 'exponential_minimum sampling'): {'detectability': 0.75, 'distortion': 0.25},
            ('high-entropy masking', 'temperature sampling'): {'detectability': 0.65, 'distortion': 0.35},
            ('high-entropy masking', 'greedy sampling'): {'detectability': 0.55, 'distortion': 0.45}
        }
    }
    
    # Find best combination
    best_score = 0
    best_combo = None
    for combo, metrics in pipeline_metrics['scores'].items():
        score = metrics['detectability'] * (1 - metrics['distortion'])
        if score > best_score:
            best_score = score
            best_combo = combo

    label_list = ['Input'] + pipeline_metrics['masking_methods'] + pipeline_metrics['sampling_methods'] + ['Output']
    
    source = []
    target = []
    value = []
    colors = [] 
    
    # Input to masking methods
    for i in range(len(pipeline_metrics['masking_methods'])):
        source.append(0)
        target.append(i + 1)
        value.append(1)
        colors.append('rgba(0,0,255,0.2)' if pipeline_metrics['masking_methods'][i] != best_combo[0] else 'rgba(255,0,0,0.8)')

    # Masking to sampling methods
    sampling_start = len(pipeline_metrics['masking_methods']) + 1
    for i, mask in enumerate(pipeline_metrics['masking_methods']):
        for j, sample in enumerate(pipeline_metrics['sampling_methods']):
            score = pipeline_metrics['scores'][(mask, sample)]['detectability'] * \
                   (1 - pipeline_metrics['scores'][(mask, sample)]['distortion'])
            source.append(i + 1)
            target.append(sampling_start + j)
            value.append(score)
            colors.append('rgba(0,0,255,0.2)' if (mask, sample) != best_combo else 'rgba(255,0,0,0.8)')

    # Sampling methods to output
    output_idx = len(label_list) - 1
    for i, sample in enumerate(pipeline_metrics['sampling_methods']):
        source.append(sampling_start + i)
        target.append(output_idx)
        value.append(1)
        colors.append('rgba(0,0,255,0.2)' if sample != best_combo[1] else 'rgba(255,0,0,0.8)')

    fig = go.Figure(data=[go.Sankey(
        node=dict(
            pad=15,
            thickness=20,
            line=dict(color="black", width=0.5),
            label=label_list,
            color="lightblue"
        ),
        link=dict(
            source=source,
            target=target,
            value=value,
            color=colors
        )
    )])
    
    fig.update_layout(
        title_text=f"Watermarking Pipeline Flow<br>Best Combination: {best_combo[0]} + {best_combo[1]}",
        font_size=12,
        height=500
    )
    return fig