tree.py

import plotly.graph_objects as go
import textwrap
import re
from collections import defaultdict

def generate_subplot1(paraphrased_sentence, scheme_sentences, highlight_info, common_grams):
    # Combine nodes into one list with appropriate labels
    nodes = [paraphrased_sentence] + scheme_sentences
    nodes[0] += ' L0'  # Paraphrased sentence is level 0
    for i in range(1, len(nodes)):
        nodes[i] += ' L1'  # Scheme sentences are level 1

    # Function to apply LCS numbering based on common_grams
    def apply_lcs_numbering(sentence, common_grams):
        for idx, lcs in common_grams:
            # Only replace if the LCS is a whole word (not part of another word)
            sentence = re.sub(rf"\b{lcs}\b", f"({idx}){lcs}", sentence)
        return sentence

    # Apply LCS numbering
    nodes = [apply_lcs_numbering(node, common_grams) for node in nodes]

    # Define the highlight_words function
    def highlight_words(sentence, color_map):
        for word, color in color_map.items():
            sentence = re.sub(f"\\b{word}\\b", f"{{{{{word}}}}}", sentence, flags=re.IGNORECASE)
        return sentence

    # Clean and wrap nodes, and highlight specified words globally
    cleaned_nodes = [re.sub(r'\sL[0-9]$', '', node) for node in nodes]
    global_color_map = dict(highlight_info)
    highlighted_nodes = [highlight_words(node, global_color_map) for node in cleaned_nodes]
    wrapped_nodes = ['<br>'.join(textwrap.wrap(node, width=55)) for node in highlighted_nodes]

    # Function to determine tree levels and create edges dynamically
    def get_levels_and_edges(nodes):
        levels = {}
        edges = []
        for i, node in enumerate(nodes):
            level = int(node.split()[-1][1])
            levels[i] = level

        # Add edges from L0 to all L1 nodes
        root_node = next(i for i, level in levels.items() if level == 0)
        for i, level in levels.items():
            if level == 1:
                edges.append((root_node, i))

        return levels, edges

    # Get levels and dynamic edges
    levels, edges = get_levels_and_edges(nodes)
    max_level = max(levels.values(), default=0)

    # Calculate positions
    positions = {}
    level_heights = defaultdict(int)
    for node, level in levels.items():
        level_heights[level] += 1

    y_offsets = {level: - (height - 1) / 2 for level, height in level_heights.items()}
    x_gap = 2
    l1_y_gap = 10

    for node, level in levels.items():
        if level == 1:
            positions[node] = (-level * x_gap, y_offsets[level] * l1_y_gap)
        else:
            positions[node] = (-level * x_gap, y_offsets[level] * l1_y_gap)
        y_offsets[level] += 1

    # Function to highlight words in a wrapped node string
    def color_highlighted_words(node, color_map):
        parts = re.split(r'(\{\{.*?\}\})', node)
        colored_parts = []
        for part in parts:
            match = re.match(r'\{\{(.*?)\}\}', part)
            if match:
                word = match.group(1)
                color = color_map.get(word, 'black')
                colored_parts.append(f"<span style='color: {color};'>{word}</span>")
            else:
                colored_parts.append(part)
        return ''.join(colored_parts)

    # Define the text for each edge
    edge_texts = [
        "Highest Entropy Masking",
        "Pseudo-random Masking",
        "Random Masking",
        "Greedy Sampling",
        "Temperature Sampling",
        "Exponential Minimum Sampling",
        "Inverse Transform Sampling",
        "Greedy Sampling",
        "Temperature Sampling",
        "Exponential Minimum Sampling",
        "Inverse Transform Sampling",
        "Greedy Sampling",
        "Temperature Sampling",
        "Exponential Minimum Sampling",
        "Inverse Transform Sampling"
    ]

    # Create figure
    fig1 = go.Figure()

    # Add nodes to the figure
    for i, node in enumerate(wrapped_nodes):
        colored_node = color_highlighted_words(node, global_color_map)
        x, y = positions[i]
        fig1.add_trace(go.Scatter(
            x=[-x],  # Reflect the x coordinate
            y=[y],
            mode='markers',
            marker=dict(size=10, color='blue'),
            hoverinfo='none'
        ))
        fig1.add_annotation(
            x=-x,  # Reflect the x coordinate
            y=y,
            text=colored_node,
            showarrow=False,
            xshift=15,
            align="center",
            font=dict(size=12),
            bordercolor='black',
            borderwidth=1,
            borderpad=2,
            bgcolor='white',
            width=300,
            height=120
        )

    # Add edges and text above each edge
    for i, edge in enumerate(edges):
        x0, y0 = positions[edge[0]]
        x1, y1 = positions[edge[1]]
        fig1.add_trace(go.Scatter(
            x=[-x0, -x1],  # Reflect the x coordinates
            y=[y0, y1],
            mode='lines',
            line=dict(color='black', width=1)
        ))

        # Calculate the midpoint of the edge
        mid_x = (-x0 + -x1) / 2
        mid_y = (y0 + y1) / 2

        # Adjust y position to shift text upwards
        text_y_position = mid_y + 0.8  # Increase this value to shift the text further upwards

        # Add text annotation above the edge
        fig1.add_annotation(
            x=mid_x,
            y=text_y_position,
            text=edge_texts[i],  # Use the text specific to this edge
            showarrow=False,
            font=dict(size=12),
            align="center"
        )

    fig1.update_layout(
        showlegend=False,
        margin=dict(t=20, b=20, l=20, r=20),
        xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
        yaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
        width=1435,  # Adjusted width to accommodate more levels
        height=1000   # Adjusted height to accommodate more levels
    )

    return fig1

def generate_subplot2(scheme_sentences, sampled_sentence, highlight_info, common_grams):
    # Combine nodes into one list with appropriate labels
    nodes = scheme_sentences + sampled_sentence
    para_len = len(scheme_sentences)

    # Reassign levels: L1 -> L0, L2 -> L1
    for i in range(para_len):
        nodes[i] += ' L0'  # Scheme sentences are now level 0
    for i in range(para_len, len(nodes)):
        nodes[i] += ' L1'  # Sampled sentences are now level 1

    # Function to apply LCS numbering based on common_grams
    def apply_lcs_numbering(sentence, common_grams):
        for idx, lcs in common_grams:
            # Only replace if the LCS is a whole word (not part of another word)
            sentence = re.sub(rf"\b{lcs}\b", f"({idx}){lcs}", sentence)
        return sentence

    # Apply LCS numbering
    nodes = [apply_lcs_numbering(node, common_grams) for node in nodes]

    # Define the highlight_words function
    def highlight_words(sentence, color_map):
        for word, color in color_map.items():
            sentence = re.sub(f"\\b{word}\\b", f"{{{{{word}}}}}", sentence, flags=re.IGNORECASE)
        return sentence

    # Clean and wrap nodes, and highlight specified words globally
    cleaned_nodes = [re.sub(r'\sL[0-9]$', '', node) for node in nodes]
    global_color_map = dict(highlight_info)
    highlighted_nodes = [highlight_words(node, global_color_map) for node in cleaned_nodes]
    wrapped_nodes = ['<br>'.join(textwrap.wrap(node, width=80)) for node in highlighted_nodes]

    # Function to determine tree levels and create edges dynamically
    def get_levels_and_edges(nodes):
        levels = {}
        edges = []
        for i, node in enumerate(nodes):
            level = int(node.split()[-1][1])
            levels[i] = level

        # Add edges from L0 to all L1 nodes
        l0_indices = [i for i, level in levels.items() if level == 0]
        l1_indices = [i for i, level in levels.items() if level == 1]

        # Ensure there are exactly 3 L0 nodes
        if len(l0_indices) < 3:
            raise ValueError("There should be exactly 3 L0 nodes to attach edges correctly.")

        # Split L1 nodes into 3 groups of 4 for attaching to L0 nodes
        for i, l1_node in enumerate(l1_indices):
            if i < 4:
                edges.append((l0_indices[0], l1_node))  # Connect to the first L0 node
            elif i < 8:
                edges.append((l0_indices[1], l1_node))  # Connect to the second L0 node
            else:
                edges.append((l0_indices[2], l1_node))  # Connect to the third L0 node

        return levels, edges

    # Get levels and dynamic edges
    levels, edges = get_levels_and_edges(nodes)
    max_level = max(levels.values(), default=0)

    # Calculate positions
    positions = {}
    level_heights = defaultdict(int)
    for node, level in levels.items():
        level_heights[level] += 1

    y_offsets = {level: - (height - 1) / 2 for level, height in level_heights.items()}
    x_gap = 2
    l1_y_gap = 10

    for node, level in levels.items():
        if level == 1:
            positions[node] = (-level * x_gap, y_offsets[level] * l1_y_gap)
        else:
            positions[node] = (-level * x_gap, y_offsets[level] * l1_y_gap)
        y_offsets[level] += 1

    # Function to highlight words in a wrapped node string
    def color_highlighted_words(node, color_map):
        parts = re.split(r'(\{\{.*?\}\})', node)
        colored_parts = []
        for part in parts:
            match = re.match(r'\{\{(.*?)\}\}', part)
            if match:
                word = match.group(1)
                color = color_map.get(word, 'black')
                colored_parts.append(f"<span style='color: {color};'>{word}</span>")
            else:
                colored_parts.append(part)
        return ''.join(colored_parts)

    # Define the text for each edge
    edge_texts = [
        "Highest Entropy Masking",
        "Pseudo-random Masking",
        "Random Masking",
        "Greedy Sampling",
        "Temperature Sampling",
        "Exponential Minimum Sampling",
        "Inverse Transform Sampling",
        "Greedy Sampling",
        "Temperature Sampling",
        "Exponential Minimum Sampling",
        "Inverse Transform Sampling",
        "Greedy Sampling",
        "Temperature Sampling",
        "Exponential Minimum Sampling",
        "Inverse Transform Sampling"
    ]

    # Create figure
    fig2 = go.Figure()

    # Add nodes to the figure
    for i, node in enumerate(wrapped_nodes):
        colored_node = color_highlighted_words(node, global_color_map)
        x, y = positions[i]
        fig2.add_trace(go.Scatter(
            x=[-x],  # Reflect the x coordinate
            y=[y],
            mode='markers',
            marker=dict(size=10, color='blue'),
            hoverinfo='none'
        ))
        fig2.add_annotation(
            x=-x,  # Reflect the x coordinate
            y=y,
            text=colored_node,
            showarrow=False,
            xshift=15,
            align="center",
            font=dict(size=12),
            bordercolor='black',
            borderwidth=1,
            borderpad=2,
            bgcolor='white',
            width=450,
            height=65
        )

    # Add edges and text above each edge
    for i, edge in enumerate(edges):
        x0, y0 = positions[edge[0]]
        x1, y1 = positions[edge[1]]
        fig2.add_trace(go.Scatter(
            x=[-x0, -x1],  # Reflect the x coordinates
            y=[y0, y1],
            mode='lines',
            line=dict(color='black', width=1)
        ))

        # Calculate the midpoint of the edge
        mid_x = (-x0 + -x1) / 2
        mid_y = (y0 + y1) / 2

        # Adjust y position to shift text upwards
        text_y_position = mid_y + 0.8  # Increase this value to shift the text further upwards

        # Add text annotation above the edge
        # Use a fallback text if we exceed the length of edge_texts
        text = edge_texts[i] if i < len(edge_texts) else f"Edge {i+1}"
        fig2.add_annotation(
            x=mid_x,
            y=text_y_position,
            text=text,  # Use the text specific to this edge
            showarrow=False,
            font=dict(size=12),
            align="center"
        )

    fig2.update_layout(
        showlegend=False,
        margin=dict(t=20, b=20, l=20, r=20),
        xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
        yaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
        width=1435,  # Adjusted width to accommodate more levels
        height=1000   # Adjusted height to accommodate more levels
    )

    return fig2