Update modules/text_analysis/semantic_analysis.py

modules/text_analysis/semantic_analysis.py

@@ -1,251 +1,263 @@
-#semantic_analysis.py
-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"
-    }
-}
-
-##############################################################################################################
-def perform_semantic_analysis(text, nlp, lang_code):
-    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):
-    logger.info("Identifying key concepts")
-    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)
-    return [(concept, float(freq)) for concept, freq in key_concepts]
-
-
-def create_concept_graph(doc, key_concepts):
-    G = nx.Graph()
-    for concept, freq in key_concepts:
-        G.add_node(concept, weight=freq)
-    for sent in doc.sents:
-        sent_concepts = [token.lemma_.lower() for token in sent if token.lemma_.lower() in dict(key_concepts)]
-        for i, concept1 in enumerate(sent_concepts):
-            for concept2 in sent_concepts[i+1:]:
-                if G.has_edge(concept1, concept2):
-                    G[concept1][concept2]['weight'] += 1
-                else:
-                    G.add_edge(concept1, concept2, weight=1)
-    return G
-
-def visualize_concept_graph(G, lang_code):
-    fig, ax = plt.subplots(figsize=(12, 8))
-    pos = nx.spring_layout(G, k=0.5, iterations=50)
-    node_sizes = [G.nodes[node]['weight'] * 100 for node in G.nodes()]
-    nx.draw_networkx_nodes(G, pos, node_size=node_sizes, node_color='lightblue', 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)
-    title = {
-        'es': "Relaciones entre Conceptos Clave",
-        'en': "Key Concept Relations",
-        'fr': "Relations entre Concepts Clés"
-    }
-    ax.set_title(title[lang_code], fontsize=16)
-    ax.axis('off')
-    plt.tight_layout()
-    return fig
-
-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'
+# 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"
+    }
+}
+
+##############################################################################################################
+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):
+    logger.info("Identifying key concepts")
+    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)
+    return [(concept, float(freq)) for concept, freq in key_concepts]
+
+
+def create_concept_graph(doc, key_concepts):
+    G = nx.Graph()
+    for concept, freq in key_concepts:
+        G.add_node(concept, weight=freq)
+    for sent in doc.sents:
+        sent_concepts = [token.lemma_.lower() for token in sent if token.lemma_.lower() in dict(key_concepts)]
+        for i, concept1 in enumerate(sent_concepts):
+            for concept2 in sent_concepts[i+1:]:
+                if G.has_edge(concept1, concept2):
+                    G[concept1][concept2]['weight'] += 1
+                else:
+                    G.add_edge(concept1, concept2, weight=1)
+    return G
+
+def visualize_concept_graph(G, lang_code):
+    fig, ax = plt.subplots(figsize=(12, 8))
+    pos = nx.spring_layout(G, k=0.5, iterations=50)
+    node_sizes = [G.nodes[node]['weight'] * 100 for node in G.nodes()]
+    nx.draw_networkx_nodes(G, pos, node_size=node_sizes, node_color='lightblue', 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)
+    title = {
+        'es': "Relaciones entre Conceptos Clave",
+        'en': "Key Concept Relations",
+        'fr': "Relations entre Concepts Clés"
+    }
+    ax.set_title(title[lang_code], fontsize=16)
+    ax.axis('off')
+    plt.tight_layout()
+    return fig
+
+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'
 ]