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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 | |