Delete modules/semantic_analysis.py

modules/semantic_analysis.py

@@ -1,230 +0,0 @@
-#semantic_analysis.py
-import streamlit as st
-import spacy
-import networkx as nx
-import matplotlib.pyplot as plt
-from collections import Counter
-from collections import defaultdict
-
-# Remove the global nlp model loading
-
-# Define colors for grammatical categories
-POS_COLORS = {
-    'ADJ': '#FFA07A',    # Light Salmon
-    'ADP': '#98FB98',    # Pale Green
-    'ADV': '#87CEFA',    # Light Sky Blue
-    'AUX': '#DDA0DD',    # Plum
-    'CCONJ': '#F0E68C',  # Khaki
-    'DET': '#FFB6C1',    # Light Pink
-    'INTJ': '#FF6347',   # Tomato
-    'NOUN': '#90EE90',   # Light Green
-    'NUM': '#FAFAD2',    # Light Goldenrod Yellow
-    'PART': '#D3D3D3',   # Light Gray
-    'PRON': '#FFA500',   # Orange
-    'PROPN': '#20B2AA',  # Light Sea Green
-    'SCONJ': '#DEB887',  # Burlywood
-    'SYM': '#7B68EE',    # Medium Slate Blue
-    'VERB': '#FF69B4',   # Hot Pink
-    'X': '#A9A9A9',      # Dark Gray
-}
-
-POS_TRANSLATIONS = {
-    'es': {
-        'ADJ': 'Adjetivo',
-        'ADP': 'Adposición',
-        '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': 'Adposition',
-        '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': 'Adposition',
-        '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',
-    }
-}
-########################################################################################################################################
-
-# Definimos las etiquetas y colores para cada idioma
-ENTITY_LABELS = {
-    'es': {
-        "Personas": "lightblue",
-        "Conceptos": "lightgreen",
-        "Lugares": "lightcoral",
-        "Fechas": "lightyellow"
-    },
-    'en': {
-        "People": "lightblue",
-        "Concepts": "lightgreen",
-        "Places": "lightcoral",
-        "Dates": "lightyellow"
-    },
-    'fr': {
-        "Personnes": "lightblue",
-        "Concepts": "lightgreen",
-        "Lieux": "lightcoral",
-        "Dates": "lightyellow"
-    }
-}
-
-#########################################################################################################
-def count_pos(doc):
-    return Counter(token.pos_ for token in doc if token.pos_ != 'PUNCT')
-
-#####################################################################################################################
-
-def create_semantic_graph(doc, lang):
-    G = nx.Graph()
-    word_freq = defaultdict(int)
-    lemma_to_word = {}
-    lemma_to_pos = {}
-
-    # Count frequencies of lemmas and map lemmas to their most common word form and POS
-    for token in doc:
-        if token.pos_ in ['NOUN', 'VERB']:
-            lemma = token.lemma_.lower()
-            word_freq[lemma] += 1
-            if lemma not in lemma_to_word or token.text.lower() == lemma:
-                lemma_to_word[lemma] = token.text
-            lemma_to_pos[lemma] = token.pos_
-
-    # Get top 20 most frequent lemmas
-    top_lemmas = [lemma for lemma, _ in sorted(word_freq.items(), key=lambda x: x[1], reverse=True)[:20]]
-
-    # Add nodes
-    for lemma in top_lemmas:
-        word = lemma_to_word[lemma]
-        G.add_node(word, pos=lemma_to_pos[lemma])
-
-    # Add edges
-    for token in doc:
-        if token.lemma_.lower() in top_lemmas:
-            if token.head.lemma_.lower() in top_lemmas:
-                source = lemma_to_word[token.lemma_.lower()]
-                target = lemma_to_word[token.head.lemma_.lower()]
-                if source != target:  # Avoid self-loops
-                    G.add_edge(source, target, label=token.dep_)
-
-    return G, word_freq
-
-############################################################################################################################################
-
-def visualize_semantic_relations(doc, lang):
-    G = nx.Graph()
-    word_freq = defaultdict(int)
-    lemma_to_word = {}
-    lemma_to_pos = {}
-
-    # Count frequencies of lemmas and map lemmas to their most common word form and POS
-    for token in doc:
-        if token.pos_ in ['NOUN', 'VERB']:
-            lemma = token.lemma_.lower()
-            word_freq[lemma] += 1
-            if lemma not in lemma_to_word or token.text.lower() == lemma:
-                lemma_to_word[lemma] = token.text
-            lemma_to_pos[lemma] = token.pos_
-
-    # Get top 20 most frequent lemmas
-    top_lemmas = [lemma for lemma, _ in sorted(word_freq.items(), key=lambda x: x[1], reverse=True)[:20]]
-
-    # Add nodes
-    for lemma in top_lemmas:
-        word = lemma_to_word[lemma]
-        G.add_node(word, pos=lemma_to_pos[lemma])
-
-    # Add edges
-    for token in doc:
-        if token.lemma_.lower() in top_lemmas:
-            if token.head.lemma_.lower() in top_lemmas:
-                source = lemma_to_word[token.lemma_.lower()]
-                target = lemma_to_word[token.head.lemma_.lower()]
-                if source != target:  # Avoid self-loops
-                    G.add_edge(source, target, label=token.dep_)
-
-    fig, ax = plt.subplots(figsize=(36, 27))
-    pos = nx.spring_layout(G, k=0.7, iterations=50)
-
-    node_colors = [POS_COLORS.get(G.nodes[node]['pos'], '#CCCCCC') for node in G.nodes()]
-
-    nx.draw(G, pos, node_color=node_colors, with_labels=True, 
-            node_size=10000, 
-            font_size=16, 
-            font_weight='bold', 
-            arrows=True, 
-            arrowsize=30, 
-            width=3, 
-            edge_color='gray',
-            ax=ax)
-
-    edge_labels = nx.get_edge_attributes(G, 'label')
-    nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_size=14, ax=ax)
-
-    title = {
-        'es': "Relaciones Semánticas Relevantes",
-        'en': "Relevant Semantic Relations",
-        'fr': "Relations Sémantiques Pertinentes"
-    }
-    ax.set_title(title[lang], fontsize=24, fontweight='bold')
-    ax.axis('off')
-
-    legend_elements = [plt.Rectangle((0,0),1,1,fc=POS_COLORS.get(pos, '#CCCCCC'), edgecolor='none', 
-                       label=f"{POS_TRANSLATIONS[lang].get(pos, pos)}")
-                       for pos in ['NOUN', 'VERB']]
-    ax.legend(handles=legend_elements, loc='center left', bbox_to_anchor=(1, 0.5), fontsize=16)
-
-    return fig
-
-############################################################################################################################################
-def perform_semantic_analysis(text, nlp, lang):
-    doc = nlp(text)
-    
-    # Imprimir entidades para depuración
-    print(f"Entidades encontradas ({lang}):")
-    for ent in doc.ents:
-        print(f"{ent.text} - {ent.label_}")
-    
-    relations_graph = visualize_semantic_relations(doc, lang)
-    return relations_graph  # Ahora solo devuelve un único gráfico
-
-__all__ = ['visualize_semantic_relations', 'create_semantic_graph', 'POS_COLORS', 'POS_TRANSLATIONS']