src/application/highlight_text.py

import gradio as gr
import colorsys
from functools import partial
import random


def lighten_color(hex_color, factor=1.8):
    """Lightens a HEX color by increasing its brightness in HSV space."""

    hex_color = hex_color.lstrip("#")
    r, g, b = int(hex_color[0:2], 16), int(hex_color[2:4], 16), int(hex_color[4:6], 16)
    
    # Convert to HSV
    h, s, v = colorsys.rgb_to_hsv(r / 255.0, g / 255.0, b / 255.0)
    v = min(1.0, v * factor)  # Increase brightness
    
    # Convert back to HEX
    r, g, b = [int(c * 255) for c in colorsys.hsv_to_rgb(h, s, v)]
    return f'#{r:02x}{g:02x}{b:02x}'

def darken_color(hex_color, factor=0.7):
    """Darkens a hex color by reducing its brightness in the HSV space."""

    hex_color = hex_color.lstrip("#")
    r, g, b = int(hex_color[0:2], 16), int(hex_color[2:4], 16), int(hex_color[4:6], 16)
    
    # Convert to HSV to adjust brightness
    h, s, v = colorsys.rgb_to_hsv(r / 255.0, g / 255.0, b / 255.0)
    v = max(0, v * factor)  # Reduce brightness
    
    # Convert back to HEX
    r, g, b = [int(c * 255) for c in colorsys.hsv_to_rgb(h, s, v)]
    return f'#{r:02x}{g:02x}{b:02x}'

# Generate unique colors for pairs
def generate_color(index, total_colors=20):
    """Generates a unique, evenly spaced color for each index using HSL."""

    hue = index / total_colors  # Spread hues in range [0,1]
    saturation = 0.65  # Keep colors vivid
    lightness = 0.75   # Balanced brightness
    
    # Convert HSL to RGB
    r, g, b = colorsys.hls_to_rgb(hue, lightness, saturation)
    r, g, b = int(r * 255), int(g * 255), int(b * 255)
    
    return f'#{r:02x}{g:02x}{b:02x}'  # Convert to hex

def highlight_pairs(text1, text2):
    """Highlight matching pairs between two paragraphs"""
    # Predefined matching pairs
    match_pairs = [
        {"index": 1, "text1": "deep learning", "start1": 13, "end1": 26, 
                    "text2": "deep learning", "start2": 12, "end2": 25},
        {"index": 2, "text1": "neural networks", "start1": 56, "end1": 71, 
                    "text2": "neural networks", "start2": 68, "end2": 83},
        {"index": 3, "text1": "AI research", "start1": 86, "end1": 97, 
                    "text2": "AI research", "start2": 55, "end2": 66},
    ]

    # Assign unique colors to each index
    pair_colors = {pair["index"]: generate_color(pair["index"], total_colors=len(match_pairs)) for pair in match_pairs}
        
    
    def apply_highlight(text, pairs, key_start, key_end, key_index, pair_colors):
        highlighted_text = ""
        prev_end = 0
        
        for pair in sorted(pairs, key=lambda x: x[key_start]):
            start, end, index = pair[key_start], pair[key_end], pair[key_index]
            color = pair_colors.get(index, "#ddd")  # Default color if not found
            color = lighten_color(color, factor=2.2)  # Lightened color for background text
            label_color = darken_color(color, factor=0.7)  # Make label color darker

            # Style the index as a label
            index_label = (f'<span style="background-color:{label_color}; color:white; '
                            f'padding:1px 4px; border-radius:4px; font-size:12px; '
                            f'font-weight:bold; display:inline-block; margin-right:4px;">{index}</span>')

            # Append non-highlighted text
            highlighted_text += text[prev_end:start]
            # Append highlighted text with index label
            highlighted_text += (f'<span style="background-color:{color}; '
                                    f'border-radius:3px; font-size:14px; display:inline-block;">'
                                    f'{index_label} {text[start:end]}</span>')
            prev_end = end

        # Append remaining text
        highlighted_text += text[prev_end:]
        return highlighted_text

    # Apply highlighting to both paragraphs using the global MATCH_PAIRS
    highlighted_text1 = apply_highlight(text1, match_pairs, "start1", "end1", "index", pair_colors)
    highlighted_text2 = apply_highlight(text2, match_pairs, "start2", "end2", "index", pair_colors)

    return highlighted_text1, highlighted_text2

if __name__ == '__main__':
    # Create Gradio Interface
    text1 = ""
    
    with gr.Blocks() as demo:
        gr.Markdown("### Highlight Matching Parts Between Two Paragraphs")
        text1_input = gr.Textbox(
            label="Paragraph 1", 
            lines=5, 
            value="The field of deep learning is advancing rapidly. Modern neural networks are improving AI research significantly."
        )
        text2_input = gr.Textbox(
            label="Paragraph 2", 
            lines=5, 
            value="Advances in deep learning have led to breakthroughs in AI research. Neural networks are at the core of these innovations"
        )
        output1 = gr.HTML()
        output2 = gr.HTML()
        submit_button = gr.Button("Highlight Matches")
    
        submit_button.click(
            fn=highlight_pairs,
            inputs=[text1_input, text2_input],
            outputs=[output1, output2]
        )
    
    # Launch the Gradio app
    demo.launch()