Create semantic_analysis.py

modules/text_analysis/semantic_analysis.py

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+import streamlit as st
+import spacy
+import networkx as nx
+import matplotlib.pyplot as plt
+from collections import Counter
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+
+# ... (mantén las definiciones de POS_COLORS, POS_TRANSLATIONS, y ENTITY_LABELS como están)
+
+def identify_key_concepts(doc):
+    word_freq = Counter([token.lemma_.lower() for token in doc if token.pos_ in ['NOUN', 'VERB'] and not token.is_stop])
+    return word_freq.most_common(10)  # Top 10 conceptos clave
+
+def create_concept_graph(text, concepts):
+    vectorizer = TfidfVectorizer()
+    tfidf_matrix = vectorizer.fit_transform([text])
+    concept_vectors = vectorizer.transform([c[0] for c in concepts])
+    similarity_matrix = cosine_similarity(concept_vectors, concept_vectors)
+
+    G = nx.Graph()
+    for i, (concept, weight) in enumerate(concepts):
+        G.add_node(concept, weight=weight)
+        for j in range(i+1, len(concepts)):
+            if similarity_matrix[i][j] > 0.1:
+                G.add_edge(concept, concepts[j][0], weight=similarity_matrix[i][j])
+
+    return G
+
+def visualize_concept_graph(G, lang):
+    fig, ax = plt.subplots(figsize=(15, 10))
+    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)
+    nx.draw_networkx_edges(G, pos, width=1, alpha=0.5, ax=ax)
+    
+    edge_labels = nx.get_edge_attributes(G, 'weight')
+    nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_size=8, ax=ax)
+
+    title = {
+        'es': "Relaciones entre Conceptos Clave",
+        'en': "Key Concept Relations",
+        'fr': "Relations entre Concepts Clés"
+    }
+    ax.set_title(title[lang], fontsize=16)
+    ax.axis('off')
+
+    plt.tight_layout()
+    return fig
+
+def perform_semantic_analysis(text, nlp, lang):
+    doc = nlp(text)
+
+    # Identificar conceptos clave
+    key_concepts = identify_key_concepts(doc)
+
+    # Crear y visualizar grafo de conceptos
+    concept_graph = create_concept_graph(text, key_concepts)
+    relations_graph = visualize_concept_graph(concept_graph, lang)
+    
+    return {
+        'key_concepts': key_concepts,
+        'relations_graph': relations_graph
+    }
+
+__all__ = ['perform_semantic_analysis', 'ENTITY_LABELS', 'POS_TRANSLATIONS']