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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 = [t[0] for t in exp.as_list()]
attributions = [t[1] for t in exp.as_list()]
l, weights = zip(*exp.local_exp[exp.available_labels()[0]])
sentences_weights = {sentences[i]: attributions[i] for i in l}
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
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