Create semantic_analysis.py

modules/text_analysis/semantic_analysis.py

@@ -0,0 +1,446 @@
+# modules/text_analysis/semantic_analysis.py
+# [Mantener todas las importaciones y constantes existentes...]
+
+import streamlit as st
+import spacy
+import networkx as nx
+import matplotlib.pyplot as plt
+import io
+import base64
+from collections import Counter, defaultdict
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+# Define colors for grammatical categories
+POS_COLORS = {
+    'ADJ': '#FFA07A', 'ADP': '#98FB98', 'ADV': '#87CEFA', 'AUX': '#DDA0DD',
+    'CCONJ': '#F0E68C', 'DET': '#FFB6C1', 'INTJ': '#FF6347', 'NOUN': '#90EE90',
+    'NUM': '#FAFAD2', 'PART': '#D3D3D3', 'PRON': '#FFA500', 'PROPN': '#20B2AA',
+    'SCONJ': '#DEB887', 'SYM': '#7B68EE', 'VERB': '#FF69B4', 'X': '#A9A9A9',
+}
+
+POS_TRANSLATIONS = {
+    'es': {
+        'ADJ': 'Adjetivo', 'ADP': 'Preposición', 'ADV': 'Adverbio', 'AUX': 'Auxiliar',
+        'CCONJ': 'Conjunción Coordinante', 'DET': 'Determinante', 'INTJ': 'Interjección',
+        'NOUN': 'Sustantivo', 'NUM': 'Número', 'PART': 'Partícula', 'PRON': 'Pronombre',
+        'PROPN': 'Nombre Propio', 'SCONJ': 'Conjunción Subordinante', 'SYM': 'Símbolo',
+        'VERB': 'Verbo', 'X': 'Otro',
+    },
+    'en': {
+        'ADJ': 'Adjective', 'ADP': 'Preposition', 'ADV': 'Adverb', 'AUX': 'Auxiliary',
+        'CCONJ': 'Coordinating Conjunction', 'DET': 'Determiner', 'INTJ': 'Interjection',
+        'NOUN': 'Noun', 'NUM': 'Number', 'PART': 'Particle', 'PRON': 'Pronoun',
+        'PROPN': 'Proper Noun', 'SCONJ': 'Subordinating Conjunction', 'SYM': 'Symbol',
+        'VERB': 'Verb', 'X': 'Other',
+    },
+    'fr': {
+        'ADJ': 'Adjectif', 'ADP': 'Préposition', 'ADV': 'Adverbe', 'AUX': 'Auxiliaire',
+        'CCONJ': 'Conjonction de Coordination', 'DET': 'Déterminant', 'INTJ': 'Interjection',
+        'NOUN': 'Nom', 'NUM': 'Nombre', 'PART': 'Particule', 'PRON': 'Pronom',
+        'PROPN': 'Nom Propre', 'SCONJ': 'Conjonction de Subordination', 'SYM': 'Symbole',
+        'VERB': 'Verbe', 'X': 'Autre',
+    }
+}
+
+ENTITY_LABELS = {
+    'es': {
+        "Personas": "lightblue",
+        "Lugares": "lightcoral",
+        "Inventos": "lightgreen",
+        "Fechas": "lightyellow",
+        "Conceptos": "lightpink"
+    },
+    'en': {
+        "People": "lightblue",
+        "Places": "lightcoral",
+        "Inventions": "lightgreen",
+        "Dates": "lightyellow",
+        "Concepts": "lightpink"
+    },
+    'fr': {
+        "Personnes": "lightblue",
+        "Lieux": "lightcoral",
+        "Inventions": "lightgreen",
+        "Dates": "lightyellow",
+        "Concepts": "lightpink"
+    }
+}
+
+CUSTOM_STOPWORDS = {
+    'es': {
+        # Artículos
+        'el', 'la', 'los', 'las', 'un', 'una', 'unos', 'unas',
+        # Preposiciones comunes
+        'a', 'ante', 'bajo', 'con', 'contra', 'de', 'desde', 'en',
+        'entre', 'hacia', 'hasta', 'para', 'por', 'según', 'sin',
+        'sobre', 'tras', 'durante', 'mediante',
+        # Conjunciones
+        'y', 'e', 'ni', 'o', 'u', 'pero', 'sino', 'porque',
+        # Pronombres
+        'yo', 'tú', 'él', 'ella', 'nosotros', 'vosotros', 'ellos',
+        'ellas', 'este', 'esta', 'ese', 'esa', 'aquel', 'aquella',
+        # Verbos auxiliares comunes
+        'ser', 'estar', 'haber', 'tener',
+        # Palabras comunes en textos académicos
+        'además', 'también', 'asimismo', 'sin embargo', 'no obstante',
+        'por lo tanto', 'entonces', 'así', 'luego', 'pues',
+        # Números escritos
+        'uno', 'dos', 'tres', 'primer', 'primera', 'segundo', 'segunda',
+        # Otras palabras comunes
+        'cada', 'todo', 'toda', 'todos', 'todas', 'otro', 'otra',
+        'donde', 'cuando', 'como', 'que', 'cual', 'quien',
+        'cuyo', 'cuya', 'hay', 'solo', 'ver', 'si', 'no',
+        # Símbolos y caracteres especiales
+        '#', '@', '/', '*', '+', '-', '=', '$', '%'
+    },
+    'en': {
+        # Articles
+        'the', 'a', 'an',
+        # Common prepositions
+        'in', 'on', 'at', 'by', 'for', 'with', 'about', 'against',
+        'between', 'into', 'through', 'during', 'before', 'after',
+        'above', 'below', 'to', 'from', 'up', 'down', 'of',
+        # Conjunctions
+        'and', 'or', 'but', 'nor', 'so', 'for', 'yet',
+        # Pronouns
+        'i', 'you', 'he', 'she', 'it', 'we', 'they', 'this',
+        'that', 'these', 'those', 'my', 'your', 'his', 'her',
+        # Auxiliary verbs
+        'be', 'am', 'is', 'are', 'was', 'were', 'been', 'have',
+        'has', 'had', 'do', 'does', 'did',
+        # Common academic words
+        'therefore', 'however', 'thus', 'hence', 'moreover',
+        'furthermore', 'nevertheless',
+        # Numbers written
+        'one', 'two', 'three', 'first', 'second', 'third',
+        # Other common words
+        'where', 'when', 'how', 'what', 'which', 'who',
+        'whom', 'whose', 'there', 'here', 'just', 'only',
+        # Symbols and special characters
+        '#', '@', '/', '*', '+', '-', '=', '$', '%'
+    },
+    'fr': {
+        # Articles
+        'le', 'la', 'les', 'un', 'une', 'des',
+        # Prepositions
+        'à', 'de', 'dans', 'sur', 'en', 'par', 'pour', 'avec',
+        'sans', 'sous', 'entre', 'derrière', 'chez', 'avant',
+        # Conjunctions
+        'et', 'ou', 'mais', 'donc', 'car', 'ni', 'or',
+        # Pronouns
+        'je', 'tu', 'il', 'elle', 'nous', 'vous', 'ils',
+        'elles', 'ce', 'cette', 'ces', 'celui', 'celle',
+        # Auxiliary verbs
+        'être', 'avoir', 'faire',
+        # Academic words
+        'donc', 'cependant', 'néanmoins', 'ainsi', 'toutefois',
+        'pourtant', 'alors',
+        # Numbers
+        'un', 'deux', 'trois', 'premier', 'première', 'second',
+        # Other common words
+        'où', 'quand', 'comment', 'que', 'qui', 'quoi',
+        'quel', 'quelle', 'plus', 'moins',
+        # Symbols
+        '#', '@', '/', '*', '+', '-', '=', '$', '%'
+    }
+}
+
+##############################################################################################################
+def get_stopwords(lang_code):
+    """
+    Obtiene el conjunto de stopwords para un idioma específico.
+    Combina las stopwords de spaCy con las personalizadas.
+    """
+    try:
+        nlp = spacy.load(f'{lang_code}_core_news_sm')
+        spacy_stopwords = nlp.Defaults.stop_words
+        custom_stopwords = CUSTOM_STOPWORDS.get(lang_code, set())
+        return spacy_stopwords.union(custom_stopwords)
+    except:
+        return CUSTOM_STOPWORDS.get(lang_code, set())
+
+
+def perform_semantic_analysis(text, nlp, lang_code):
+    """
+    Realiza el análisis semántico completo del texto.
+    Args:
+        text: Texto a analizar
+        nlp: Modelo de spaCy
+        lang_code: Código del idioma
+    Returns:
+        dict: Resultados del análisis
+    """
+    
+    logger.info(f"Starting semantic analysis for language: {lang_code}")
+    try:
+        doc = nlp(text)
+        key_concepts = identify_key_concepts(doc)
+        concept_graph = create_concept_graph(doc, key_concepts)
+        concept_graph_fig = visualize_concept_graph(concept_graph, lang_code)
+        entities = extract_entities(doc, lang_code)
+        entity_graph = create_entity_graph(entities)
+        entity_graph_fig = visualize_entity_graph(entity_graph, lang_code)
+
+        # Convertir figuras a bytes
+        concept_graph_bytes = fig_to_bytes(concept_graph_fig)
+        entity_graph_bytes = fig_to_bytes(entity_graph_fig)
+
+        logger.info("Semantic analysis completed successfully")
+        return {
+            'key_concepts': key_concepts,
+            'concept_graph': concept_graph_bytes,
+            'entities': entities,
+            'entity_graph': entity_graph_bytes
+        }
+    except Exception as e:
+        logger.error(f"Error in perform_semantic_analysis: {str(e)}")
+        raise
+
+
+def fig_to_bytes(fig):
+    buf = io.BytesIO()
+    fig.savefig(buf, format='png')
+    buf.seek(0)
+    return buf.getvalue()
+
+
+def fig_to_html(fig):
+    buf = io.BytesIO()
+    fig.savefig(buf, format='png')
+    buf.seek(0)
+    img_str = base64.b64encode(buf.getvalue()).decode()
+    return f'<img src="data:image/png;base64,{img_str}" />'
+
+
+
+def identify_key_concepts(doc, min_freq=2, min_length=3):
+    """
+    Identifica conceptos clave en el texto.
+    Args:
+        doc: Documento procesado por spaCy
+        min_freq: Frecuencia mínima para considerar un concepto
+        min_length: Longitud mínima de palabra para considerar
+    Returns:
+        list: Lista de tuplas (concepto, frecuencia)
+    """
+    try:
+        # Obtener stopwords para el idioma
+        stopwords = get_stopwords(doc.lang_)
+        
+        # Contar frecuencias de palabras
+        word_freq = Counter()
+        
+        for token in doc:
+            if (token.lemma_.lower() not in stopwords and
+                len(token.lemma_) >= min_length and
+                token.is_alpha and
+                not token.is_punct and
+                not token.like_num):
+                
+                word_freq[token.lemma_.lower()] += 1
+        
+        # Filtrar por frecuencia mínima
+        concepts = [(word, freq) for word, freq in word_freq.items() 
+                   if freq >= min_freq]
+        
+        # Ordenar por frecuencia
+        concepts.sort(key=lambda x: x[1], reverse=True)
+        
+        return concepts[:10]  # Retornar los 10 conceptos más frecuentes
+        
+    except Exception as e:
+        logger.error(f"Error en identify_key_concepts: {str(e)}")
+        return []  # Retornar lista vacía en caso de error
+
+
+def create_concept_graph(doc, key_concepts):
+    """
+    Crea un grafo de relaciones entre conceptos.
+    Args:
+        doc: Documento procesado por spaCy
+        key_concepts: Lista de tuplas (concepto, frecuencia)
+    Returns:
+        nx.Graph: Grafo de conceptos
+    """
+    try:
+        G = nx.Graph()
+        
+        # Crear un conjunto de conceptos clave para búsqueda rápida
+        concept_words = {concept[0].lower() for concept in key_concepts}
+        
+        # Añadir nodos al grafo
+        for concept, freq in key_concepts:
+            G.add_node(concept.lower(), weight=freq)
+        
+        # Analizar cada oración
+        for sent in doc.sents:
+            # Obtener conceptos en la oración actual
+            current_concepts = []
+            for token in sent:
+                if token.lemma_.lower() in concept_words:
+                    current_concepts.append(token.lemma_.lower())
+            
+            # Crear conexiones entre conceptos en la misma oración
+            for i, concept1 in enumerate(current_concepts):
+                for concept2 in current_concepts[i+1:]:
+                    if concept1 != concept2:
+                        # Si ya existe la arista, incrementar el peso
+                        if G.has_edge(concept1, concept2):
+                            G[concept1][concept2]['weight'] += 1
+                        # Si no existe, crear nueva arista con peso 1
+                        else:
+                            G.add_edge(concept1, concept2, weight=1)
+        
+        return G
+        
+    except Exception as e:
+        logger.error(f"Error en create_concept_graph: {str(e)}")
+        # Retornar un grafo vacío en caso de error
+        return nx.Graph()
+
+def visualize_concept_graph(G, lang_code):
+    """
+    Visualiza el grafo de conceptos.
+    Args:
+        G: Grafo de networkx
+        lang_code: Código del idioma
+    Returns:
+        matplotlib.figure.Figure: Figura con el grafo visualizado
+    """
+    try:
+        plt.figure(figsize=(12, 8))
+        
+        # Calcular el layout del grafo
+        pos = nx.spring_layout(G)
+        
+        # Obtener pesos de nodos y aristas
+        node_weights = [G.nodes[node].get('weight', 1) * 500 for node in G.nodes()]
+        edge_weights = [G[u][v].get('weight', 1) for u, v in G.edges()]
+        
+        # Dibujar el grafo
+        nx.draw_networkx_nodes(G, pos, 
+                             node_size=node_weights,
+                             node_color='lightblue',
+                             alpha=0.6)
+        
+        nx.draw_networkx_edges(G, pos,
+                             width=edge_weights,
+                             alpha=0.5,
+                             edge_color='gray')
+        
+        nx.draw_networkx_labels(G, pos,
+                              font_size=10,
+                              font_weight='bold')
+        
+        plt.title("Red de conceptos relacionados")
+        plt.axis('off')
+        
+        return plt.gcf()
+        
+    except Exception as e:
+        logger.error(f"Error en visualize_concept_graph: {str(e)}")
+        # Retornar una figura vacía en caso de error
+        return plt.figure()
+
+def create_entity_graph(entities):
+    G = nx.Graph()
+    for entity_type, entity_list in entities.items():
+        for entity in entity_list:
+            G.add_node(entity, type=entity_type)
+        for i, entity1 in enumerate(entity_list):
+            for entity2 in entity_list[i+1:]:
+                G.add_edge(entity1, entity2)
+    return G
+
+def visualize_entity_graph(G, lang_code):
+    fig, ax = plt.subplots(figsize=(12, 8))
+    pos = nx.spring_layout(G)
+    for entity_type, color in ENTITY_LABELS[lang_code].items():
+        node_list = [node for node, data in G.nodes(data=True) if data['type'] == entity_type]
+        nx.draw_networkx_nodes(G, pos, nodelist=node_list, node_color=color, node_size=500, alpha=0.8, ax=ax)
+    nx.draw_networkx_edges(G, pos, width=1, alpha=0.5, ax=ax)
+    nx.draw_networkx_labels(G, pos, font_size=8, font_weight="bold", ax=ax)
+    ax.set_title(f"Relaciones entre Entidades ({lang_code})", fontsize=16)
+    ax.axis('off')
+    plt.tight_layout()
+    return fig
+
+
+#################################################################################
+def create_topic_graph(topics, doc):
+    G = nx.Graph()
+    for topic in topics:
+        G.add_node(topic, weight=doc.text.count(topic))
+    for i, topic1 in enumerate(topics):
+        for topic2 in topics[i+1:]:
+            weight = sum(1 for sent in doc.sents if topic1 in sent.text and topic2 in sent.text)
+            if weight > 0:
+                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)
+    node_sizes = [G.nodes[node]['weight'] * 100 for node in G.nodes()]
+    nx.draw_networkx_nodes(G, pos, node_size=node_sizes, node_color='lightgreen', alpha=0.8, ax=ax)
+    nx.draw_networkx_labels(G, pos, font_size=10, font_weight="bold", ax=ax)
+    edge_weights = [G[u][v]['weight'] for u, v in G.edges()]
+    nx.draw_networkx_edges(G, pos, width=edge_weights, alpha=0.5, ax=ax)
+    ax.set_title(f"Relaciones entre Temas ({lang_code})", fontsize=16)
+    ax.axis('off')
+    plt.tight_layout()
+    return fig
+
+###########################################################################################
+def generate_summary(doc, lang_code):
+    sentences = list(doc.sents)
+    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:
+        if ent.label_ in ENTITY_LABELS[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)
+    total_words = len(doc)
+    if positive_words > negative_words:
+        return "Positivo"
+    elif negative_words > positive_words:
+        return "Negativo"
+    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',
+    'identify_key_concepts',
+    'create_concept_graph',
+    'visualize_concept_graph',
+    'create_entity_graph',
+    'visualize_entity_graph',
+    'generate_summary',
+    'extract_entities',
+    'analyze_sentiment',
+    'create_topic_graph',
+    'visualize_topic_graph',
+    'extract_topics',
+    'ENTITY_LABELS',
+    'POS_COLORS',
+    'POS_TRANSLATIONS'
+]