src/application/text/highlight_text.py

import colorsys

import gradio as gr


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>'  # noqa: E501
            )

            # 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()