Create current_situation_analysis.py

modules/studentact/current_situation_analysis.py

@@ -0,0 +1,810 @@
+#v3/modules/studentact/current_situation_analysis.py
+
+import streamlit as st
+import matplotlib.pyplot as plt
+import networkx as nx
+import seaborn as sns
+from collections import Counter
+from itertools import combinations
+import numpy as np
+import matplotlib.patches as patches
+import logging
+
+# 2. Configuración básica del logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.StreamHandler(),
+        logging.FileHandler('app.log')
+    ]
+)
+
+# 3. Obtener el logger específico para este módulo
+logger = logging.getLogger(__name__)
+
+#########################################################################
+
+def correlate_metrics(scores):
+    """
+    Ajusta los scores para mantener correlaciones lógicas entre métricas.
+    
+    Args:
+        scores: dict con scores iniciales de vocabulario, estructura, cohesión y claridad
+        
+    Returns:
+        dict con scores ajustados
+    """
+    try:
+        # 1. Correlación estructura-cohesión
+        # La cohesión no puede ser menor que estructura * 0.7
+        min_cohesion = scores['structure']['normalized_score'] * 0.7
+        if scores['cohesion']['normalized_score'] < min_cohesion:
+            scores['cohesion']['normalized_score'] = min_cohesion
+
+        # 2. Correlación vocabulario-cohesión
+        # La cohesión léxica depende del vocabulario
+        vocab_influence = scores['vocabulary']['normalized_score'] * 0.6
+        scores['cohesion']['normalized_score'] = max(
+            scores['cohesion']['normalized_score'],
+            vocab_influence
+        )
+
+        # 3. Correlación cohesión-claridad
+        # La claridad no puede superar cohesión * 1.2
+        max_clarity = scores['cohesion']['normalized_score'] * 1.2
+        if scores['clarity']['normalized_score'] > max_clarity:
+            scores['clarity']['normalized_score'] = max_clarity
+
+        # 4. Correlación estructura-claridad
+        # La claridad no puede superar estructura * 1.1
+        struct_max_clarity = scores['structure']['normalized_score'] * 1.1
+        scores['clarity']['normalized_score'] = min(
+            scores['clarity']['normalized_score'],
+            struct_max_clarity
+        )
+
+        # Normalizar todos los scores entre 0 y 1
+        for metric in scores:
+            scores[metric]['normalized_score'] = max(0.0, min(1.0, scores[metric]['normalized_score']))
+
+        return scores
+
+    except Exception as e:
+        logger.error(f"Error en correlate_metrics: {str(e)}")
+        return scores
+
+##########################################################################
+
+def analyze_text_dimensions(doc):
+    """
+    Analiza las dimensiones principales del texto manteniendo correlaciones lógicas.
+    """
+    try:
+        # Obtener scores iniciales
+        vocab_score, vocab_details = analyze_vocabulary_diversity(doc)
+        struct_score = analyze_structure(doc)
+        cohesion_score = analyze_cohesion(doc)
+        clarity_score, clarity_details = analyze_clarity(doc)
+
+        # Crear diccionario de scores inicial
+        scores = {
+            'vocabulary': {
+                'normalized_score': vocab_score,
+                'details': vocab_details
+            },
+            'structure': {
+                'normalized_score': struct_score,
+                'details': None
+            },
+            'cohesion': {
+                'normalized_score': cohesion_score,
+                'details': None
+            },
+            'clarity': {
+                'normalized_score': clarity_score,
+                'details': clarity_details
+            }
+        }
+
+        # Ajustar correlaciones entre métricas
+        adjusted_scores = correlate_metrics(scores)
+
+        # Logging para diagnóstico
+        logger.info(f"""
+            Scores originales vs ajustados:
+            Vocabulario: {vocab_score:.2f} -> {adjusted_scores['vocabulary']['normalized_score']:.2f}
+            Estructura: {struct_score:.2f} -> {adjusted_scores['structure']['normalized_score']:.2f}
+            Cohesión: {cohesion_score:.2f} -> {adjusted_scores['cohesion']['normalized_score']:.2f}
+            Claridad: {clarity_score:.2f} -> {adjusted_scores['clarity']['normalized_score']:.2f}
+        """)
+
+        return adjusted_scores
+
+    except Exception as e:
+        logger.error(f"Error en analyze_text_dimensions: {str(e)}")
+        return {
+            'vocabulary': {'normalized_score': 0.0, 'details': {}},
+            'structure': {'normalized_score': 0.0, 'details': {}},
+            'cohesion': {'normalized_score': 0.0, 'details': {}},
+            'clarity': {'normalized_score': 0.0, 'details': {}}
+        }
+
+
+
+#############################################################################################
+
+def analyze_clarity(doc):
+    """
+    Analiza la claridad del texto considerando múltiples factores.
+    """
+    try:
+        sentences = list(doc.sents)
+        if not sentences:
+            return 0.0, {}
+            
+        # 1. Longitud de oraciones
+        sentence_lengths = [len(sent) for sent in sentences]
+        avg_length = sum(sentence_lengths) / len(sentences)
+        
+        # Normalizar usando los umbrales definidos para clarity
+        length_score = normalize_score(
+            value=avg_length,
+            metric_type='clarity',
+            optimal_length=20,  # Una oración ideal tiene ~20 palabras
+            min_threshold=0.60,  # Consistente con METRIC_THRESHOLDS
+            target_threshold=0.75  # Consistente con METRIC_THRESHOLDS
+        )
+        
+        # 2. Análisis de conectores
+        connector_count = 0
+        connector_weights = {
+            'CCONJ': 1.0,  # Coordinantes
+            'SCONJ': 1.2,  # Subordinantes
+            'ADV': 0.8     # Adverbios conectivos
+        }
+        
+        for token in doc:
+            if token.pos_ in connector_weights and token.dep_ in ['cc', 'mark', 'advmod']:
+                connector_count += connector_weights[token.pos_]
+                
+        # Normalizar conectores por oración
+        connectors_per_sentence = connector_count / len(sentences) if sentences else 0
+        connector_score = normalize_score(
+            value=connectors_per_sentence,
+            metric_type='clarity',
+            optimal_connections=1.5,  # ~1.5 conectores por oración es óptimo
+            min_threshold=0.60,
+            target_threshold=0.75
+        )
+        
+        # 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 sentences else 0
+        complexity_score = normalize_score(
+            value=complexity_raw,
+            metric_type='clarity',
+            optimal_depth=2.0,  # ~2 cláusulas por oración es óptimo
+            min_threshold=0.60,
+            target_threshold=0.75
+        )
+        
+        # 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 if token.is_alpha])
+        density = content_words / total_words if total_words > 0 else 0
+        
+        density_score = normalize_score(
+            value=density,
+            metric_type='clarity',
+            optimal_connections=0.6,  # 60% de palabras de contenido es óptimo
+            min_threshold=0.60,
+            target_threshold=0.75
+        )
+        
+        # Score final ponderado
+        weights = {
+            'length': 0.3,
+            'connectors': 0.3,
+            'complexity': 0.2,
+            'density': 0.2
+        }
+        
+        clarity_score = (
+            weights['length'] * length_score +
+            weights['connectors'] * connector_score +
+            weights['complexity'] * complexity_score +
+            weights['density'] * density_score
+        )
+
+        details = {
+            'length_score': length_score,
+            'connector_score': connector_score,
+            'complexity_score': complexity_score,
+            'density_score': density_score,
+            'avg_sentence_length': avg_length,
+            'connectors_per_sentence': connectors_per_sentence,
+            'density': density
+        }
+        
+        # Agregar logging para diagnóstico
+        logger.info(f"""
+            Scores de Claridad:
+            - Longitud: {length_score:.2f} (avg={avg_length:.1f} palabras)
+            - Conectores: {connector_score:.2f} (avg={connectors_per_sentence:.1f} por oración)
+            - Complejidad: {complexity_score:.2f} (avg={complexity_raw:.1f} cláusulas)
+            - Densidad: {density_score:.2f} ({density*100:.1f}% palabras de contenido)
+            - Score Final: {clarity_score:.2f}
+        """)
+        
+        return clarity_score, details
+
+    except Exception as e:
+        logger.error(f"Error en analyze_clarity: {str(e)}")
+        return 0.0, {}
+
+
+def analyze_vocabulary_diversity(doc):
+    """Análisis mejorado de la diversidad y calidad del vocabulario"""
+    try:
+        # 1. Análisis básico de diversidad
+        unique_lemmas = {token.lemma_ for token in doc if token.is_alpha}
+        total_words = len([token for token in doc if token.is_alpha])
+        basic_diversity = len(unique_lemmas) / total_words if total_words > 0 else 0
+        
+        # 2. Análisis de registro
+        academic_words = 0
+        narrative_words = 0
+        technical_terms = 0
+        
+        # Clasificar palabras por registro
+        for token in doc:
+            if token.is_alpha:
+                # Detectar términos académicos/técnicos
+                if token.pos_ in ['NOUN', 'VERB', 'ADJ']:
+                    if any(parent.pos_ == 'NOUN' for parent in token.ancestors):
+                        technical_terms += 1
+                # Detectar palabras narrativas
+                if token.pos_ in ['VERB', 'ADV'] and token.dep_ in ['ROOT', 'advcl']:
+                    narrative_words += 1
+                    
+        # 3. Análisis de complejidad sintáctica
+        avg_sentence_length = sum(len(sent) for sent in doc.sents) / len(list(doc.sents))
+        
+        # 4. Calcular score ponderado
+        weights = {
+            'diversity': 0.3,
+            'technical': 0.3,
+            'narrative': 0.2,
+            'complexity': 0.2
+        }
+        
+        scores = {
+            'diversity': basic_diversity,
+            'technical': technical_terms / total_words if total_words > 0 else 0,
+            'narrative': narrative_words / total_words if total_words > 0 else 0,
+            'complexity': min(1.0, avg_sentence_length / 20)  # Normalizado a 20 palabras
+        }
+        
+        # Score final ponderado
+        final_score = sum(weights[key] * scores[key] for key in weights)
+        
+        # Información adicional para diagnóstico
+        details = {
+            'text_type': 'narrative' if scores['narrative'] > scores['technical'] else 'academic',
+            'scores': scores
+        }
+        
+        return final_score, details
+        
+    except Exception as e:
+        logger.error(f"Error en analyze_vocabulary_diversity: {str(e)}")
+        return 0.0, {}
+
+def analyze_cohesion(doc):
+    """Analiza la cohesión textual"""
+    try:
+        sentences = list(doc.sents)
+        if len(sentences) < 2:
+            logger.warning("Texto demasiado corto para análisis de cohesión")
+            return 0.0
+            
+        # 1. Análisis de conexiones léxicas
+        lexical_connections = 0
+        total_possible_connections = 0
+        
+        for i in range(len(sentences)-1):
+            # Obtener lemmas significativos (no stopwords)
+            sent1_words = {token.lemma_ for token in sentences[i] 
+                         if token.is_alpha and not token.is_stop}
+            sent2_words = {token.lemma_ for token in sentences[i+1] 
+                         if token.is_alpha and not token.is_stop}
+            
+            if sent1_words and sent2_words:  # Verificar que ambos conjuntos no estén vacíos
+                intersection = len(sent1_words.intersection(sent2_words))
+                total_possible = min(len(sent1_words), len(sent2_words))
+                
+                if total_possible > 0:
+                    lexical_score = intersection / total_possible
+                    lexical_connections += lexical_score
+                    total_possible_connections += 1
+        
+        # 2. Análisis de conectores
+        connector_count = 0
+        connector_types = {
+            'CCONJ': 1.0,  # Coordinantes
+            'SCONJ': 1.2,  # Subordinantes
+            'ADV': 0.8     # Adverbios conectivos
+        }
+        
+        for token in doc:
+            if (token.pos_ in connector_types and 
+                token.dep_ in ['cc', 'mark', 'advmod'] and 
+                not token.is_stop):
+                connector_count += connector_types[token.pos_]
+        
+        # 3. Cálculo de scores normalizados
+        if total_possible_connections > 0:
+            lexical_cohesion = lexical_connections / total_possible_connections
+        else:
+            lexical_cohesion = 0
+            
+        if len(sentences) > 1:
+            connector_cohesion = min(1.0, connector_count / (len(sentences) - 1))
+        else:
+            connector_cohesion = 0
+        
+        # 4. Score final ponderado
+        weights = {
+            'lexical': 0.7,
+            'connectors': 0.3
+        }
+        
+        cohesion_score = (
+            weights['lexical'] * lexical_cohesion + 
+            weights['connectors'] * connector_cohesion
+        )
+        
+        # 5. Logging para diagnóstico
+        logger.info(f"""
+            Análisis de Cohesión:
+            - Conexiones léxicas encontradas: {lexical_connections}
+            - Conexiones posibles: {total_possible_connections}
+            - Lexical cohesion score: {lexical_cohesion}
+            - Conectores encontrados: {connector_count}
+            - Connector cohesion score: {connector_cohesion}
+            - Score final: {cohesion_score}
+        """)
+        
+        return cohesion_score
+
+    except Exception as e:
+        logger.error(f"Error en analyze_cohesion: {str(e)}")
+        return 0.0
+
+def analyze_structure(doc):
+    try:
+        if len(doc) == 0:
+            return 0.0
+            
+        structure_scores = []
+        for token in doc:
+            if token.dep_ == 'ROOT':
+                result = get_dependency_depths(token)
+                structure_scores.append(result['final_score'])
+                
+        if not structure_scores:
+            return 0.0
+            
+        return min(1.0, sum(structure_scores) / len(structure_scores))
+        
+    except Exception as e:
+        logger.error(f"Error en analyze_structure: {str(e)}")
+        return 0.0
+
+# Funciones auxiliares de análisis
+
+def get_dependency_depths(token, depth=0, analyzed_tokens=None):
+    """
+    Analiza la profundidad y calidad de las relaciones de dependencia.
+    
+    Args:
+        token: Token a analizar
+        depth: Profundidad actual en el árbol
+        analyzed_tokens: Set para evitar ciclos en el análisis
+    
+    Returns:
+        dict: Información detallada sobre las dependencias
+            - depths: Lista de profundidades
+            - relations: Diccionario con tipos de relaciones encontradas
+            - complexity_score: Puntuación de complejidad
+    """
+    if analyzed_tokens is None:
+        analyzed_tokens = set()
+        
+    # Evitar ciclos
+    if token.i in analyzed_tokens:
+        return {
+            'depths': [],
+            'relations': {},
+            'complexity_score': 0
+        }
+    
+    analyzed_tokens.add(token.i)
+    
+    # Pesos para diferentes tipos de dependencias
+    dependency_weights = {
+        # Dependencias principales
+        'nsubj': 1.2,    # Sujeto nominal
+        'obj': 1.1,      # Objeto directo
+        'iobj': 1.1,     # Objeto indirecto
+        'ROOT': 1.3,     # Raíz
+        
+        # Modificadores
+        'amod': 0.8,     # Modificador adjetival
+        'advmod': 0.8,   # Modificador adverbial
+        'nmod': 0.9,     # Modificador nominal
+        
+        # Estructuras complejas
+        'csubj': 1.4,    # Cláusula como sujeto
+        'ccomp': 1.3,    # Complemento clausal
+        'xcomp': 1.2,    # Complemento clausal abierto
+        'advcl': 1.2,    # Cláusula adverbial
+        
+        # Coordinación y subordinación
+        'conj': 1.1,     # Conjunción
+        'cc': 0.7,       # Coordinación
+        'mark': 0.8,     # Marcador
+        
+        # Otros
+        'det': 0.5,      # Determinante
+        'case': 0.5,     # Caso
+        'punct': 0.1     # Puntuación
+    }
+    
+    # Inicializar resultados
+    current_result = {
+        'depths': [depth],
+        'relations': {token.dep_: 1},
+        'complexity_score': dependency_weights.get(token.dep_, 0.5) * (depth + 1)
+    }
+    
+    # Analizar hijos recursivamente
+    for child in token.children:
+        child_result = get_dependency_depths(child, depth + 1, analyzed_tokens)
+        
+        # Combinar profundidades
+        current_result['depths'].extend(child_result['depths'])
+        
+        # Combinar relaciones
+        for rel, count in child_result['relations'].items():
+            current_result['relations'][rel] = current_result['relations'].get(rel, 0) + count
+            
+        # Acumular score de complejidad
+        current_result['complexity_score'] += child_result['complexity_score']
+        
+    # Calcular métricas adicionales
+    current_result['max_depth'] = max(current_result['depths'])
+    current_result['avg_depth'] = sum(current_result['depths']) / len(current_result['depths'])
+    current_result['relation_diversity'] = len(current_result['relations'])
+    
+    # Calcular score ponderado por tipo de estructura
+    structure_bonus = 0
+    
+    # Bonus por estructuras complejas
+    if 'csubj' in current_result['relations'] or 'ccomp' in current_result['relations']:
+        structure_bonus += 0.3
+    
+    # Bonus por coordinación balanceada
+    if 'conj' in current_result['relations'] and 'cc' in current_result['relations']:
+        structure_bonus += 0.2
+    
+    # Bonus por modificación rica
+    if len(set(['amod', 'advmod', 'nmod']) & set(current_result['relations'])) >= 2:
+        structure_bonus += 0.2
+        
+    current_result['final_score'] = (
+        current_result['complexity_score'] * (1 + structure_bonus)
+    )
+    
+    return current_result
+
+def normalize_score(value, metric_type, 
+                   min_threshold=0.0, target_threshold=1.0, 
+                   range_factor=2.0, optimal_length=None, 
+                   optimal_connections=None, optimal_depth=None):
+    """
+    Normaliza un valor considerando umbrales específicos por tipo de métrica.
+    
+    Args:
+        value: Valor a normalizar
+        metric_type: Tipo de métrica ('vocabulary', 'structure', 'cohesion', 'clarity')
+        min_threshold: Valor mínimo aceptable
+        target_threshold: Valor objetivo
+        range_factor: Factor para ajustar el rango
+        optimal_length: Longitud óptima (opcional)
+        optimal_connections: Número óptimo de conexiones (opcional)
+        optimal_depth: Profundidad óptima de estructura (opcional)
+    
+    Returns:
+        float: Valor normalizado entre 0 y 1
+    """
+    try:
+        # Definir umbrales por tipo de métrica
+        METRIC_THRESHOLDS = {
+            'vocabulary': {
+                'min': 0.60,
+                'target': 0.75,
+                'range_factor': 1.5
+            },
+            'structure': {
+                'min': 0.65,
+                'target': 0.80,
+                'range_factor': 1.8
+            },
+            'cohesion': {
+                'min': 0.55,
+                'target': 0.70,
+                'range_factor': 1.6
+            },
+            'clarity': {
+                'min': 0.60,
+                'target': 0.75,
+                'range_factor': 1.7
+            }
+        }
+
+        # Validar valores negativos o cero
+        if value < 0:
+            logger.warning(f"Valor negativo recibido: {value}")
+            return 0.0
+            
+        # Manejar caso donde el valor es cero
+        if value == 0:
+            logger.warning("Valor cero recibido")
+            return 0.0
+
+        # Obtener umbrales específicos para el tipo de métrica
+        thresholds = METRIC_THRESHOLDS.get(metric_type, {
+            'min': min_threshold,
+            'target': target_threshold,
+            'range_factor': range_factor
+        })
+        
+        # Identificar el valor de referencia a usar
+        if optimal_depth is not None:
+            reference = optimal_depth
+        elif optimal_connections is not None:
+            reference = optimal_connections
+        elif optimal_length is not None:
+            reference = optimal_length
+        else:
+            reference = thresholds['target']
+
+        # Validar valor de referencia
+        if reference <= 0:
+            logger.warning(f"Valor de referencia inválido: {reference}")
+            return 0.0
+
+        # Calcular score basado en umbrales
+        if value < thresholds['min']:
+            # Valor por debajo del mínimo
+            score = (value / thresholds['min']) * 0.5  # Máximo 0.5 para valores bajo el mínimo
+        elif value < thresholds['target']:
+            # Valor entre mínimo y objetivo
+            range_size = thresholds['target'] - thresholds['min']
+            progress = (value - thresholds['min']) / range_size
+            score = 0.5 + (progress * 0.5)  # Escala entre 0.5 y 1.0
+        else:
+            # Valor alcanza o supera el objetivo
+            score = 1.0
+            
+            # Penalizar valores muy por encima del objetivo
+            if value > (thresholds['target'] * thresholds['range_factor']):
+                excess = (value - thresholds['target']) / (thresholds['target'] * thresholds['range_factor'])
+                score = max(0.7, 1.0 - excess)  # No bajar de 0.7 para valores altos
+
+        # Asegurar que el resultado esté entre 0 y 1
+        return max(0.0, min(1.0, score))
+
+    except Exception as e:
+        logger.error(f"Error en normalize_score: {str(e)}")
+        return 0.0
+
+
+# Funciones de generación de gráficos
+def generate_sentence_graphs(doc):
+    """Genera visualizaciones de estructura de oraciones"""
+    fig, ax = plt.subplots(figsize=(10, 6))
+    # Implementar visualización
+    plt.close()
+    return fig
+
+def generate_word_connections(doc):
+    """Genera red de conexiones de palabras"""
+    fig, ax = plt.subplots(figsize=(10, 6))
+    # Implementar visualización
+    plt.close()
+    return fig
+
+def generate_connection_paths(doc):
+    """Genera patrones de conexión"""
+    fig, ax = plt.subplots(figsize=(10, 6))
+    # Implementar visualización
+    plt.close()
+    return fig
+
+def create_vocabulary_network(doc):
+    """
+    Genera el grafo de red de vocabulario.
+    """
+    G = nx.Graph()
+    
+    # Crear nodos para palabras significativas
+    words = [token.text.lower() for token in doc if token.is_alpha and not token.is_stop]
+    word_freq = Counter(words)
+    
+    # Añadir nodos con tamaño basado en frecuencia
+    for word, freq in word_freq.items():
+        G.add_node(word, size=freq)
+    
+    # Crear conexiones basadas en co-ocurrencia
+    window_size = 5
+    for i in range(len(words) - window_size):
+        window = words[i:i+window_size]
+        for w1, w2 in combinations(set(window), 2):
+            if G.has_edge(w1, w2):
+                G[w1][w2]['weight'] += 1
+            else:
+                G.add_edge(w1, w2, weight=1)
+    
+    # Crear visualización
+    fig, ax = plt.subplots(figsize=(12, 8))
+    pos = nx.spring_layout(G)
+    
+    # Dibujar nodos
+    nx.draw_networkx_nodes(G, pos, 
+                          node_size=[G.nodes[node]['size']*100 for node in G.nodes],
+                          node_color='lightblue',
+                          alpha=0.7)
+    
+    # Dibujar conexiones
+    nx.draw_networkx_edges(G, pos, 
+                          width=[G[u][v]['weight']*0.5 for u,v in G.edges],
+                          alpha=0.5)
+    
+    # Añadir etiquetas
+    nx.draw_networkx_labels(G, pos)
+    
+    plt.title("Red de Vocabulario")
+    plt.axis('off')
+    return fig
+
+def create_syntax_complexity_graph(doc):
+    """
+    Genera el diagrama de arco de complejidad sintáctica.
+    Muestra la estructura de dependencias con colores basados en la complejidad.
+    """
+    try:
+        # Preparar datos para la visualización
+        sentences = list(doc.sents)
+        if not sentences:
+            return None
+            
+        # Crear figura para el gráfico
+        fig, ax = plt.subplots(figsize=(12, len(sentences) * 2))
+        
+        # Colores para diferentes niveles de profundidad
+        depth_colors = plt.cm.viridis(np.linspace(0, 1, 6))
+        
+        y_offset = 0
+        max_x = 0
+        
+        for sent in sentences:
+            words = [token.text for token in sent]
+            x_positions = range(len(words))
+            max_x = max(max_x, len(words))
+            
+            # Dibujar palabras
+            plt.plot(x_positions, [y_offset] * len(words), 'k-', alpha=0.2)
+            plt.scatter(x_positions, [y_offset] * len(words), alpha=0)
+            
+            # Añadir texto
+            for i, word in enumerate(words):
+                plt.annotate(word, (i, y_offset), xytext=(0, -10), 
+                           textcoords='offset points', ha='center')
+            
+            # Dibujar arcos de dependencia
+            for token in sent:
+                if token.dep_ != "ROOT":
+                    # Calcular profundidad de dependencia
+                    depth = 0
+                    current = token
+                    while current.head != current:
+                        depth += 1
+                        current = current.head
+                    
+                    # Determinar posiciones para el arco
+                    start = token.i - sent[0].i
+                    end = token.head.i - sent[0].i
+                    
+                    # Altura del arco basada en la distancia entre palabras
+                    height = 0.5 * abs(end - start)
+                    
+                    # Color basado en la profundidad
+                    color = depth_colors[min(depth, len(depth_colors)-1)]
+                    
+                    # Crear arco
+                    arc = patches.Arc((min(start, end) + abs(end - start)/2, y_offset),
+                                    width=abs(end - start),
+                                    height=height,
+                                    angle=0,
+                                    theta1=0,
+                                    theta2=180,
+                                    color=color,
+                                    alpha=0.6)
+                    ax.add_patch(arc)
+            
+            y_offset -= 2
+        
+        # Configurar el gráfico
+        plt.xlim(-1, max_x)
+        plt.ylim(y_offset - 1, 1)
+        plt.axis('off')
+        plt.title("Complejidad Sintáctica")
+        
+        return fig
+        
+    except Exception as e:
+        logger.error(f"Error en create_syntax_complexity_graph: {str(e)}")
+        return None
+
+
+def create_cohesion_heatmap(doc):
+    """Genera un mapa de calor que muestra la cohesión entre párrafos/oraciones."""
+    try:
+        sentences = list(doc.sents)
+        n_sentences = len(sentences)
+        
+        if n_sentences < 2:
+            return None
+            
+        similarity_matrix = np.zeros((n_sentences, n_sentences))
+        
+        for i in range(n_sentences):
+            for j in range(n_sentences):
+                sent1_lemmas = {token.lemma_ for token in sentences[i] 
+                              if token.is_alpha and not token.is_stop}
+                sent2_lemmas = {token.lemma_ for token in sentences[j] 
+                              if token.is_alpha and not token.is_stop}
+                
+                if sent1_lemmas and sent2_lemmas:
+                    intersection = len(sent1_lemmas & sent2_lemmas)  # Corregido aquí
+                    union = len(sent1_lemmas | sent2_lemmas)  # Y aquí
+                    similarity_matrix[i, j] = intersection / union if union > 0 else 0
+        
+        # Crear visualización
+        fig, ax = plt.subplots(figsize=(10, 8))
+        
+        sns.heatmap(similarity_matrix,
+                   cmap='YlOrRd',
+                   square=True,
+                   xticklabels=False,
+                   yticklabels=False,
+                   cbar_kws={'label': 'Cohesión'},
+                   ax=ax)
+        
+        plt.title("Mapa de Cohesión Textual")
+        plt.xlabel("Oraciones")
+        plt.ylabel("Oraciones")
+        
+        plt.tight_layout()
+        return fig
+        
+    except Exception as e:
+        logger.error(f"Error en create_cohesion_heatmap: {str(e)}")
+        return None