Create syntax_analysis.py

modules/syntax_analysis.py

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+# syntax_analysis.py
+import spacy
+import networkx as nx
+import matplotlib.pyplot as plt
+from collections import Counter
+
+# Load spaCy model
+nlp = spacy.load("es_core_news_lg")
+
+# 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 = {
+    'ADJ': 'Adjetivo',
+    'ADP': 'Advposición',
+    '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',
+}
+
+def count_pos(doc):
+    return Counter(token.pos_ for token in doc if token.pos_ != 'PUNCT')
+
+def create_syntax_graph(doc):
+    G = nx.DiGraph()
+    pos_counts = count_pos(doc)
+    word_nodes = {}
+    word_colors = {}
+
+    for token in doc:
+        if token.pos_ != 'PUNCT':
+            lower_text = token.text.lower()
+            if lower_text not in word_nodes:
+                node_id = len(word_nodes)
+                word_nodes[lower_text] = node_id
+                color = POS_COLORS.get(token.pos_, '#FFFFFF')
+                word_colors[lower_text] = color
+                G.add_node(node_id,
+                           label=f"{token.text}\n[{POS_TRANSLATIONS.get(token.pos_, token.pos_)}]",
+                           pos=token.pos_,
+                           size=pos_counts[token.pos_] * 500,
+                           color=color)
+
+            if token.dep_ != "ROOT" and token.head.pos_ != 'PUNCT':
+                head_id = word_nodes.get(token.head.text.lower())
+                if head_id is not None:
+                    G.add_edge(head_id, word_nodes[lower_text], label=token.dep_)
+
+    return G, word_colors
+
+def visualize_syntax_graph(doc):
+    G, word_colors = create_syntax_graph(doc)
+
+    plt.figure(figsize=(20, 15))
+    pos = nx.spring_layout(G, k=2, iterations=100)
+
+    node_colors = [data['color'] for _, data in G.nodes(data=True)]
+    node_sizes = [data['size'] for _, data in G.nodes(data=True)]
+
+    nx.draw(G, pos, with_labels=False, node_color=node_colors, node_size=node_sizes, arrows=True)
+
+    nx.draw_networkx_labels(G, pos, {node: data['label'] for node, data in G.nodes(data=True)}, font_size=8)
+
+    edge_labels = nx.get_edge_attributes(G, 'label')
+    nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_size=8)
+
+    plt.title("Análisis Sintáctico")
+    plt.axis('off')
+
+    legend_elements = [plt.Rectangle((0,0),1,1, facecolor=color, edgecolor='none', label=f"{POS_TRANSLATIONS[pos]} ({count_pos(doc)[pos]})")
+                       for pos, color in POS_COLORS.items() if pos in set(nx.get_node_attributes(G, 'pos').values())]
+    plt.legend(handles=legend_elements, loc='center left', bbox_to_anchor=(1, 0.5))
+
+    return plt
+
+def visualize_syntax(text):
+    max_tokens = 5000
+    doc = nlp(text)
+    if len(doc) > max_tokens:
+        doc = nlp(text[:max_tokens])
+        print(f"Warning: The input text is too long. Only the first {max_tokens} tokens will be visualized.")
+    return visualize_syntax_graph(doc)
+
+# Repeated words colors
+def get_repeated_words_colors(doc):
+    word_counts = Counter(token.text.lower() for token in doc if token.pos_ != 'PUNCT')
+    repeated_words = {word: count for word, count in word_counts.items() if count > 1}
+
+    word_colors = {}
+    for token in doc:
+        if token.text.lower() in repeated_words:
+            word_colors[token.text.lower()] = POS_COLORS.get(token.pos_, '#FFFFFF')
+
+    return word_colors
+
+def highlight_repeated_words(doc, word_colors):
+    highlighted_text = []
+    for token in doc:
+        if token.text.lower() in word_colors:
+            color = word_colors[token.text.lower()]
+            highlighted_text.append(f'<span style="background-color: {color};">{token.text}</span>')
+        else:
+            highlighted_text.append(token.text)
+    return ' '.join(highlighted_text)