Update highlighter.py

highlighter.py

@@ -0,0 +1,40 @@
+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
+
+
+