scripts/utility_functions.py

import html
import os
import json
import re
from rapidfuzz import fuzz
import requests
from scripts.regulatory_change_foundation import (
    CLASSIFICATION_INFO,
    FEW_SHOT_EXAMPLES,
    BASE_PROMPT_TEMPLATE,
)

# Define hex colors as RGB tuples (0–1 range)
color_mapping = {
    "addition": (0, 0.4, 0),  # green
    "deletion": (1, 0, 0),  # red
    "modification": (0, 0.6, 1),  # blue
}


def to_rgb(color_tuple):
    return f"rgb({int(color_tuple[0] * 255)}, {int(color_tuple[1] * 255)}, {int(color_tuple[2] * 255)})"


css_styles = f"""
    <style>
        .custom-link {{
            display: inline-block;
            padding: 8px 16px;
            color: white !important;
            text-decoration: none;
            border-radius: 8px;
            transition: background-color 0.3s ease;
        }}
        .custom-link:hover {{
            background-color: #45a049;
        }}
        .tooltip {{
            font-weight: bold;
            cursor: help;
            background-color: white;
        }}
        .addition-tooltip {{
            color: {to_rgb(color_mapping["addition"])};
        }}
        .modification-tooltip {{
            color: {to_rgb(color_mapping["modification"])};
        }}
        .deletion-tooltip {{
            color: {to_rgb(color_mapping["deletion"])};
        }}
        .default-tooltip {{
            color: yellow;
        }}
    </style>
    """


def get_color_mapping_hex():
    return {key: tuple(int(c * 255) for c in rgb) for key, rgb in color_mapping.items()}


def get_tooltip_text(change):
    return (
        change.get("type", "Type unspecified")
        + " - "
        + change.get("category", "Category unspecified")
        + "\n"
        + change.get("context", "")
    )


def highlight_nth(text, change, skip_failed=False):
    n = change.get("occurrence_index", 0)
    target = re.sub(r"\\\s+", r".*?", change["text"])
    matches = list(re.finditer(target, text, flags=re.IGNORECASE | re.DOTALL))
    if len(matches) > n:
        match = matches[n]
        start, end = match.start(), match.end()
        tooltip_raw = get_tooltip_text(change)
        tooltip_escaped = html.escape(tooltip_raw, quote=True)
        highlighted_span = f"""<span id='marked_section' class='tooltip {change.get("type", "default")}-tooltip' title='{tooltip_escaped}'>
    {text[start:end]}
</span>"""
        return text[:start] + highlighted_span + text[end:]
    else:
        return highlight_fuzzy_match(text, change, n, skip_failed=skip_failed)


# TODO:check treshhold->51 would get always a result
# if we make it lower we get guaranteed matches but they might be different from the original target, but if threshold is too high we might not find any match eg when a word is missing
def highlight_fuzzy_match(text, change, n=0, threshold=80, skip_failed=False):
    target = change["text"]
    window_size = len(target)
    step = 1

    candidates = []
    for i in range(0, len(text) - window_size, step):
        window = text[i : i + window_size]
        score = fuzz.partial_ratio(window.lower(), target.lower())
        if score >= threshold:
            candidates.append((score, i, i + window_size))

    if not candidates and not skip_failed:
        return (
            f"""
        <span class='hover-tooltip' title='No match found'>
        <strong style='color: red;'>No match found for: "{target}"</strong> 
        <br>
        </span>
        <span style="color: red;">Please verify if it is part of the original text or if it was extracted incorrectly.</span><br>
        """
            + text
        )
    if not candidates and skip_failed:
        return text
    # Pick top-N match
    candidates.sort(reverse=True)
    _, start_norm, end_norm = candidates[min(n, len(candidates) - 1)]

    tooltip_raw = get_tooltip_text(change)
    tooltip_escaped = html.escape(tooltip_raw, quote=True)
    highlighted_span = f"""<span id='marked_section' class='tooltip {change.get("type", "default")}-tooltip' title='{tooltip_escaped}'>{text[start_norm:end_norm]}</span>"""
    return text[:start_norm] + highlighted_span + text[end_norm:]


# TODO:check treshhold->51 would get always a result
# if we make it lower we get guaranteed matches but they might be different from the original target, but if threshold is too high we might not find any match eg when a word is missing
def get_best_fuzzy_match(text, change, threshold=65):
    """Find the best fuzzy match for a change in the text and return the matched section
    Caller needs to account for potentially None return value"""
    n = change.get("occurrence_index", 0)
    target = change["text"]
    window_size = len(target)
    step = 1

    candidates = []
    for i in range(0, len(text) - window_size, step):
        window = text[i : i + window_size]
        score = fuzz.partial_ratio(window.lower(), target.lower())
        if score >= threshold:
            candidates.append((score, i, i + window_size))

    if not candidates:
        return None
    # Pick top-N match
    candidates.sort(reverse=True)
    _, start_norm, end_norm = candidates[min(n, len(candidates) - 1)]

    return text[start_norm:end_norm]


def render_prompt(text, include_nlp=False, preprocessed_data=None):
    classification_json = json.dumps(CLASSIFICATION_INFO, indent=2)
    few_shot_json = json.dumps(FEW_SHOT_EXAMPLES, indent=2)

    if include_nlp and preprocessed_data:
        chunk_entities = [
            ent for ent in preprocessed_data["entities"] if ent["text"] in text
        ]
        chunk_nouns = [
            nc for nc in preprocessed_data["noun_chunks"] if nc["text"] in text
        ]
        nlp_insights_json = json.dumps(
            {"entities": chunk_entities, "key_noun_phrases": chunk_nouns}, indent=2
        )

        nlp_section = ", and NLP insights"
        nlp_insights = f"\n\nNLP Insights:\n{nlp_insights_json}"
        evidence_block = ',\n            "evidence": {\n                "entities_involved": ["relevant named entities"],\n                "key_phrases": ["relevant noun phrases or key terms"]\n            }'
    else:
        nlp_section = ""
        nlp_insights = ""
        evidence_block = ""

    return BASE_PROMPT_TEMPLATE.format(
        classification_info=classification_json,
        few_shot_examples=few_shot_json,
        nlp_section=nlp_section,
        nlp_insights=nlp_insights,
        text=text,
        evidence_block=evidence_block,
    )


def save_json_to_file(data, output_dir, output_file):
    """Save the JSON data to a file and print the file path."""

    # Create output directory if it doesn't exist
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

    # Save JSON data to the specified file
    file_path = os.path.join(output_dir, output_file)
    with open(file_path, "w", encoding="utf-8") as f:
        json.dump(data, f, indent=4, ensure_ascii=False)

    # Print the location of the saved file
    print(f"JSON data saved successfully at: {file_path}")


def call_nlp_service(payload, method):
    url = f"https://amougou-fortiss-nlp-preprocessor.hf.space/{method}"

    # Make the request
    response = requests.post(url, data=payload)
    if response.status_code == 200:
        return response.json()
    else:
        raise Exception(f"NLP service error: {response.status_code} - {response.text}")
    
def lerp_color(value, start_color=(255, 0, 0), end_color=(0, 255, 0)):
    """
    Linearly interpolate between start_color and end_color by value.

    Parameters:
    - value: float between 0 and 1
    - start_color: tuple (r, g, b), default red
    - end_color: tuple (r, g, b), default green

    Returns:
    - CSS rgb color string, e.g. 'rgb(255, 0, 0)'
    """
    r = int(start_color[0] + (end_color[0] - start_color[0]) * value)
    g = int(start_color[1] + (end_color[1] - start_color[1]) * value)
    b = int(start_color[2] + (end_color[2] - start_color[2]) * value)
    return f"rgb({r}, {g}, {b})"