from lime.lime_text import LimeTextExplainer
from nltk.tokenize import sent_tokenize
from predictors import predict_proba_quillbot


def explainer(text):
    class_names = ['negative', 'positive']
    explainer = LimeTextExplainer(class_names=class_names, split_expression=sent_tokenize)
    exp = explainer.explain_instance(text, predict_proba_quillbot, num_features=20, num_samples=300)
    sentences = [sent for sent in sent_tokenize(text)]
    weights_mapping = exp.as_map()[1]
    sentences_weights = {sentence: 0 for sentence in sentences}
    for idx, weight in weights_mapping:
        if 0 <= idx < len(sentences):
            sentences_weights[sentences[idx]] = weight
    print(sentences_weights)
    return sentences_weights


def analyze_and_highlight(text):
    highlighted_text = ""
    sentences_weights = explainer(text)
    min_weight = min(sentences_weights.values())
    max_weight = max(sentences_weights.values())

    for sentence, weight in sentences_weights.items():
        normalized_weight = (weight - min_weight) / (max_weight - min_weight)
        if weight >= 0:
            color = f'rgba(255, {255 * (1 - normalized_weight)}, {255 * (1 - normalized_weight)}, 1)'
        else:
            color = f'rgba({255 * normalized_weight}, 255, {255 * normalized_weight}, 1)'

        sentence = sentence.strip()
        if not sentence:
            continue
    
        highlighted_sentence = f'<span style="background-color: {color}; color: black;">{sentence}</span> '
        highlighted_text += highlighted_sentence
    
    return highlighted_text