modules/studentact/current_situation_analysis.py

#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

logger = logging.getLogger(__name__)

def analyze_text_dimensions(doc):
    """
    Analiza las dimensiones principales del texto.
    
    Args:
        doc: Documento procesado por spaCy
        
    Returns:
        dict: Métricas del análisis
    """
    try:
        # Análisis de vocabulario
        vocab_score = analyze_vocabulary_diversity(doc)
        vocab_normalized = normalize_score(
            value=vocab_score,
            optimal_connections=len(doc) * 0.4  # 40% del total de palabras como conexiones óptimas
        )

        # Análisis de estructura
        struct_score = analyze_structure(doc)
        struct_normalized = normalize_score(
            value=struct_score,
            optimal_length=20  # Longitud óptima promedio de oración
        )

        # Análisis de cohesión
        cohesion_score = analyze_cohesion(doc)
        cohesion_normalized = normalize_score(
            value=cohesion_score,
            optimal_value=0.7  # 70% de cohesión como valor óptimo
        )

        # Análisis de claridad
        clarity_score = analyze_clarity(doc)
        clarity_normalized = normalize_score(
            value=clarity_score,
            optimal_value=0.8  # 80% de claridad como valor óptimo
        )

        return {
            'vocabulary': {
                'raw_score': vocab_score,
                'normalized_score': vocab_normalized
            },
            'structure': {
                'raw_score': struct_score,
                'normalized_score': struct_normalized
            },
            'cohesion': {
                'raw_score': cohesion_score,
                'normalized_score': cohesion_normalized
            },
            'clarity': {
                'raw_score': clarity_score,
                'normalized_score': clarity_normalized
            }
        }

    except Exception as e:
        logger.error(f"Error en analyze_text_dimensions: {str(e)}")
        raise

def analyze_clarity(doc):
    """
    Analiza la claridad del texto considerando múltiples factores:
    - Longitud y variación de oraciones
    - Uso de conectores
    - Complejidad estructural
    - Claridad referencial
    - Densidad léxica
    """
    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
        
        # Penalizar oraciones muy cortas o muy largas
        length_score = normalize_score(
            avg_length, 
            optimal_length=20,  # Longitud óptima
            range_factor=1.5    # Factor de tolerancia
        )
        
        # 2. Análisis de conectores
        connector_count = 0
        connector_types = {
            'CCONJ': 0.8,  # Coordinantes
            'SCONJ': 1.0,  # Subordinantes
            'ADV': 0.6     # Adverbios conectivos
        }
        
        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_score = normalize_score(
            clause_count / len(sentences),
            optimal_value=2.0,  # Promedio óptimo de cláusulas por oración
            range_factor=1.5
        )
        
        # 4. Claridad referencial
        reference_score = analyze_reference_clarity(doc)
        
        # 5. Densidad léxica
        content_words = len([token for token in doc if token.pos_ in ['NOUN', 'VERB', 'ADJ', 'ADV']])
        function_words = len([token for token in doc if token.pos_ not in ['NOUN', 'VERB', 'ADJ', 'ADV']])
        density_score = normalize_score(
            content_words / (content_words + function_words) if (content_words + function_words) > 0 else 0,
            optimal_value=0.6,  # 60% de palabras de contenido es óptimo
            range_factor=1.5
        )
        
        # Pesos para cada factor
        weights = {
            'length': 0.2,
            'connectors': 0.2,
            'complexity': 0.2,
            'reference': 0.2,
            'density': 0.2
        }
        
        # Cálculo del score final ponderado
        clarity_score = (
            weights['length'] * length_score +
            weights['connectors'] * connector_score +
            weights['complexity'] * complexity_score +
            weights['reference'] * reference_score +
            weights['density'] * density_score
        )
        
        # Información detallada para diagnóstico
        details = {
            'length_score': length_score,
            'connector_score': connector_score,
            'complexity_score': complexity_score,
            'reference_score': reference_score,
            'density_score': density_score,
            'avg_sentence_length': avg_length,
            'length_variation': length_variation,
            'connectors_per_sentence': connector_count / len(sentences)
        }
        
        return clarity_score, details
        
    except Exception as e:
        logger.error(f"Error en analyze_clarity: {str(e)}")
        return 0.0, {}

def analyze_reference_clarity(doc):
    """
    Analiza la claridad de las referencias en el texto
    """
    try:
        # Contar referencias anafóricas
        reference_count = 0
        unclear_references = 0
        
        for token in doc:
            # Detectar pronombres y determinantes
            if token.pos_ in ['PRON', 'DET']:
                reference_count += 1
                
                # Verificar si tiene antecedente claro
                has_antecedent = False
                for ancestor in token.ancestors:
                    if ancestor.pos_ == 'NOUN':
                        has_antecedent = True
                        break
                        
                if not has_antecedent:
                    unclear_references += 1
        
        # Calcular score
        if reference_count == 0:
            return 1.0  # No hay referencias = claridad máxima
            
        clarity = 1.0 - (unclear_references / reference_count)
        return max(0.0, min(1.0, clarity))
        
    except Exception as e:
        logger.error(f"Error en analyze_reference_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
            
        connections = 0
        for i in range(len(sentences)-1):
            sent1_words = {token.lemma_ for token in sentences[i]}
            sent2_words = {token.lemma_ for token in sentences[i+1]}
            connections += len(sent1_words.intersection(sent2_words))
            
        # Validar que haya conexiones antes de normalizar
        if connections == 0:
            logger.warning("No se encontraron conexiones entre oraciones")
            return 0.0
            
        return normalize_score(connections, optimal_connections=max(5, len(sentences) * 0.2))
    except Exception as e:
        logger.error(f"Error en analyze_cohesion: {str(e)}")
        return 0.0

def analyze_structure(doc):
    """Analiza la complejidad estructural"""
    try:
        if len(doc) == 0:
            logger.warning("Documento vacío")
            return 0.0
            
        root_distances = []
        for token in doc:
            if token.dep_ == 'ROOT':
                depths = get_dependency_depths(token)
                root_distances.extend(depths)
                
        if not root_distances:
            logger.warning("No se encontraron estructuras de dependencia")
            return 0.0
            
        avg_depth = sum(root_distances) / len(root_distances)
        return normalize_score(avg_depth, optimal_depth=max(3, len(doc) * 0.1))
    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):
    """Obtiene las profundidades de dependencia"""
    depths = [depth]
    for child in token.children:
        depths.extend(get_dependency_depths(child, depth + 1))
    return depths

def normalize_score(value, optimal_value=1.0, range_factor=2.0, optimal_length=None, 
                   optimal_connections=None, optimal_depth=None):
    """
    Normaliza un valor a una escala de 0-1 con manejo de casos extremos.
    
    Args:
        value: Valor a normalizar
        optimal_value: Valor óptimo de referencia
        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:
        # 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

        # 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 = optimal_value

        # Validar valor de referencia
        if reference <= 0:
            logger.warning(f"Valor de referencia inválido: {reference}")
            return 0.0

        # Calcular diferencia y máxima diferencia permitida
        diff = abs(value - reference)
        max_diff = reference * range_factor

        # Validar max_diff
        if max_diff <= 0:
            logger.warning(f"Máxima diferencia inválida: {max_diff}")
            return 0.0

        # Calcular score normalizado
        score = 1.0 - min(diff / max_diff, 1.0)
        
        # 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