Update modules/semantic/semantic_analysis.py

modules/semantic/semantic_analysis.py

@@ -94,7 +94,7 @@ def fig_to_bytes(fig):
         return None
         
 ###########################################################
-def perform_semantic_analysis(text, nlp, lang_code):
+def perform_semantic_analysis(text, nlp, lang_code, semantic_t):
     """
     Realiza el análisis semántico completo del texto.
     """
@@ -428,6 +428,7 @@ def create_topic_graph(topics, doc):
                 G.add_edge(topic1, topic2, weight=weight)
     return G
 
+##################################################
 def visualize_topic_graph(G, lang_code):
     fig, ax = plt.subplots(figsize=(12, 8))
     pos = nx.spring_layout(G)
@@ -447,6 +448,7 @@ def generate_summary(doc, lang_code):
     summary = sentences[:3]  # Toma las primeras 3 oraciones como resumen
     return " ".join([sent.text for sent in summary])
 
+##################################################
 def extract_entities(doc, lang_code):
     entities = defaultdict(list)
     for ent in doc.ents:
@@ -454,6 +456,7 @@ def extract_entities(doc, lang_code):
             entities[ent.label_].append(ent.text)
     return dict(entities)
 
+##################################################
 def analyze_sentiment(doc, lang_code):
     positive_words = sum(1 for token in doc if token.sentiment > 0)
     negative_words = sum(1 for token in doc if token.sentiment < 0)
@@ -465,12 +468,15 @@ def analyze_sentiment(doc, lang_code):
     else:
         return "Neutral"
 
+##################################################
 def extract_topics(doc, lang_code):
     vectorizer = TfidfVectorizer(stop_words='english', max_features=5)
     tfidf_matrix = vectorizer.fit_transform([doc.text])
     feature_names = vectorizer.get_feature_names_out()
     return list(feature_names)
 
+
+##################################################
 # Asegúrate de que todas las funciones necesarias estén exportadas
 __all__ = [
     'perform_semantic_analysis',