Update app.py

app.py

@@ -94,7 +94,7 @@ class NounExtractor:
             # Choose a representative dependency if no clear subject is present
             return deps_in_phrase.pop() if deps_in_phrase else 'unknown'
     
-    def extract(self, sentence, action_verb):
+    def extract(self, sentence, causative_verb):
         """
         Extracts and returns noun phrases with their detailed dependency tags from the sentence.
         """
@@ -102,10 +102,10 @@ class NounExtractor:
         noun_phrases = self.get_noun_phrases(doc)
         result_dict = {phrase: dep for phrase, dep in noun_phrases}
 
-        # Check for the presence of any actionable verbs in the sentence
-        found_verbs = [v for v in action_verb if v.lower() in sentence.lower()]
+        # Check for the presence of causative verbs like 'cause',  in the sentence
+        found_verbs = [v for v in causative_verb if v.lower() in sentence.lower()]
         if found_verbs:
-            # Adjust dependency labels for noun phrases based on the presence of an actionable verb.
+            # Adjust dependency labels for noun phrases based on the presence of an causative verb.
             for phrase, dep in list(result_dict.items()):  # Work on a copy of items to safely modify the dict
                 if dep == 'ROOT':
                     result_dict[phrase] = 'dobj'
@@ -173,12 +173,12 @@ extractor = NounExtractor(nlp=nlp)
 
 # Example of how to use this function
 words_list = ["so", "because", "increase", "contribute", "due to"]
-action_verb = ['affect', 'influence', 'increase', 'against']
+causative_verb = ['affect', 'influence', 'increase', 'against']
 
 # Define the callback function for the GUI
 def CogMapAnalysis(text):
     if contains_words_or_phrases(words_list, text):
-        result = extractor.extract(text, action_verb)
+        result = extractor.extract(text, causative_verb)
         formatted_result = format_results(result)
         plot = visualize_cognitive_map(formatted_result)
         return formatted_result, plot