Update modules/text_analysis/semantic_analysis.py

modules/text_analysis/semantic_analysis.py

@@ -63,10 +63,10 @@ ENTITY_LABELS = {
     }
 }
 
-def identify_key_concepts(doc, top_n=10):
-    # Identificar sustantivos, verbos y adjetivos más frecuentes
-    word_freq = Counter([token.lemma_.lower() for token in doc if token.pos_ in ['NOUN', 'VERB', 'ADJ'] and not token.is_stop])
-    return word_freq.most_common(top_n)
+def identify_key_concepts(doc):
+    word_freq = Counter([token.lemma_.lower() for token in doc if token.pos_ in ['NOUN', 'VERB'] and not token.is_stop])
+    key_concepts = word_freq.most_common(10)  # Top 10 conceptos clave
+    return [(concept, float(freq)) for concept, freq in key_concepts]  # Asegurarse de que las frecuencias sean float
 
 def create_concept_graph(doc, key_concepts):
     G = nx.Graph()