Update modules/studentact/current_situation_analysis.py

modules/studentact/current_situation_analysis.py

@@ -70,81 +70,8 @@ def analyze_text_dimensions(doc):
         }
 
 ####################################################################
-def analyze_clarity(doc):
-    """
-    Analiza la claridad del texto considerando múltiples factores.
-    """
-    try:
-        # 1. Análisis de oraciones
-        sentences = list(doc.sents)
-        if not sentences:
-            return 0.0, {}
-            
-        # Longitud de oraciones
-        sentence_lengths = [len(sent) for sent in sentences]
-        avg_length = sum(sentence_lengths) / len(sentences)
-        length_variation = np.std(sentence_lengths) if len(sentences) > 1 else 0
-        
-        # Normalizar longitud
-        length_score = normalize_score(avg_length, optimal_length=20)
-        
-        # 2. Análisis de conectores
-        connector_count = 0
-        connector_types = {
-            'CCONJ': 0.8,
-            'SCONJ': 1.0,
-            'ADV': 0.6
-        }
-        
-        for token in doc:
-            if token.pos_ in connector_types and token.dep_ in ['cc', 'mark', 'advmod']:
-                connector_count += connector_types[token.pos_]
-                
-        connector_score = min(1.0, connector_count / (len(sentences) * 0.8))
-        
-        # 3. Complejidad estructural
-        clause_count = 0
-        for sent in sentences:
-            verbs = [token for token in sent if token.pos_ == 'VERB']
-            clause_count += len(verbs)
-            
-        complexity_raw = clause_count / len(sentences) if len(sentences) > 0 else 0
-        complexity_score = normalize_score(complexity_raw, optimal_value=2.0)
-        
-        # 4. Densidad léxica
-        content_words = len([token for token in doc if token.pos_ in ['NOUN', 'VERB', 'ADJ', 'ADV']])
-        total_words = len([token for token in doc])
-        density_score = normalize_score(
-            content_words / total_words if total_words > 0 else 0,
-            optimal_value=0.6
-        )
-        
-        # Cálculo del score final
-        clarity_score = (
-            0.3 * length_score +
-            0.3 * connector_score +
-            0.2 * complexity_score +
-            0.2 * density_score
-        )
-        
-        details = {
-            'length_score': length_score,
-            'connector_score': connector_score,
-            'complexity_score': complexity_score,
-            'density_score': density_score,
-            'avg_sentence_length': avg_length,
-            'length_variation': length_variation,
-            'connectors_per_sentence': connector_count / len(sentences) if len(sentences) > 0 else 0
-        }
-        
-        return clarity_score, details
-        
-    except Exception as e:
-        logger.error(f"Error en analyze_clarity: {str(e)}")
-        return 0.0, {}
 
-###################################################################################3
-def analyze_clarity(doc):
+def analyze_reference_clarity'(doc):
     """
     Analiza la claridad del texto considerando múltiples factores.
     """