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from lime.lime_text import LimeTextExplainer
from nltk.tokenize import sent_tokenize
from predictors import predict_for_explainanility
from predictors import update, correct_text, split_text
def explainer(text, model_type):
def predictor_wrapper(text):
return predict_for_explainanility(text=text, model_type=model_type)
class_names = ["negative", "positive"]
explainer_ = LimeTextExplainer(
class_names=class_names, split_expression=sent_tokenize
)
sentences = [sent for sent in sent_tokenize(text)]
num_sentences = len(sentences)
exp = explainer_.explain_instance(
text, predictor_wrapper, num_features=num_sentences, num_samples=2000
)
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, model_type)
return sentences_weights, exp
def analyze_and_highlight(text, bias_buster_selected, model_type):
if bias_buster_selected:
text = update(text)
highlighted_text = ""
sentences_weights, _ = explainer(text, model_type)
positive_weights = [weight for weight in sentences_weights.values() if weight >= 0]
negative_weights = [weight for weight in sentences_weights.values() if weight < 0]
smoothing_factor = 0.001 # we do this cos to avoid all white colors
min_positive_weight = min(positive_weights) if positive_weights else 0
max_positive_weight = max(positive_weights) if positive_weights else 0
min_negative_weight = min(negative_weights) if negative_weights else 0
max_negative_weight = max(negative_weights) if negative_weights else 0
max_positive_weight += smoothing_factor
min_negative_weight -= smoothing_factor
for sentence, weight in sentences_weights.items():
sentence = sentence.strip()
if not sentence:
continue
if weight >= 0 and max_positive_weight != min_positive_weight:
normalized_weight = (weight - min_positive_weight + smoothing_factor) / (
max_positive_weight - min_positive_weight
)
color = f"rgb(255, {int(255 * (1 - normalized_weight))}, {int(255 * (1 - normalized_weight))})"
elif weight < 0 and min_negative_weight != max_negative_weight:
normalized_weight = (weight - max_negative_weight - smoothing_factor) / (
min_negative_weight - max_negative_weight
)
color = f"rgb({int(255 * (1 - normalized_weight))}, 255, {int(255 * (1 - normalized_weight))})"
else:
color = "rgb(255, 255, 255)" # when no range
highlighted_sentence = (
f'<span style="background-color: {color}; color: black;">{sentence}</span> '
)
highlighted_text += highlighted_sentence
if model_type == "bc":
gradient_labels = ["HUMAN", "AI"]
elif model_type == "quillbot":
gradient_labels = ["ORIGINAL", "HUMANIZED"]
else:
raise ValueError(f"Invalid model type: {model_type}")
highlighted_text = (
"<div>"
+ highlighted_text
+ "<div style='margin-top: 20px; text-align: center;'>"
+ "<div style='position: relative; display: inline-block; width: 60%; height: 20px; background: linear-gradient(to right, #00FF00, #FFFFFF, #FF0000); font-family: \"Segoe UI\", Tahoma, Geneva, Verdana, sans-serif; font-size: 10px; font-weight: 600; color: #222; border-radius: 10px; box-shadow: 0px 2px 5px rgba(0, 0, 0, 0.1);'>"
+ f"<span style='position: absolute; left: 5px; top: 50%; transform: translateY(-50%); color: #000; font-weight: 600;'>{gradient_labels[0]}</span>"
+ f"<span style='position: absolute; right: 5px; top: 50%; transform: translateY(-50%); color: #000; font-weight: 600;'>{gradient_labels[1]}</span>"
+ "</div>"
+ "</div>"
+ "</div>"
)
return highlighted_text
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