Create morpho_analysis.py

modules/text_analysis/morpho_analysis.py

@@ -0,0 +1,191 @@
+# /modules/morpho_analysis.py
+import spacy
+from collections import Counter
+from spacy import displacy
+import re
+
+# 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',
+    }
+}
+
+#############################################################################################
+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)
+    
+#################################################################################################
+def generate_arc_diagram(doc, lang_code):
+    sentences = list(doc.sents)
+    arc_diagrams = []
+    for sent in sentences:
+        html = displacy.render(sent, style="dep", options={"distance": 100})
+        html = html.replace('height="375"', 'height="200"')
+        html = re.sub(r'<svg[^>]*>', lambda m: m.group(0).replace('height="450"', 'height="300"'), html)
+        html = re.sub(r'<g [^>]*transform="translate\((\d+),(\d+)\)"', lambda m: f'<g transform="translate({m.group(1)},50)"', html)
+        arc_diagrams.append(html)
+    return arc_diagrams
+
+#################################################################################################
+def get_detailed_pos_analysis(doc):
+    """
+    Realiza un análisis detallado de las categorías gramaticales (POS) en el texto.
+    """
+    pos_counts = Counter(token.pos_ for token in doc)
+    total_tokens = len(doc)
+    pos_analysis = []
+    for pos, count in pos_counts.items():
+        percentage = (count / total_tokens) * 100
+        pos_analysis.append({
+            'pos': pos,
+            'count': count,
+            'percentage': round(percentage, 2),
+            'examples': [token.text for token in doc if token.pos_ == pos][:5]  # Primeros 5 ejemplos
+        })
+    return sorted(pos_analysis, key=lambda x: x['count'], reverse=True)
+
+#################################################################################################
+def get_morphological_analysis(doc):
+    """
+    Realiza un análisis morfológico detallado de las palabras en el texto.
+    """
+    morphology_analysis = []
+    for token in doc:
+        if token.pos_ in ['NOUN', 'VERB', 'ADJ', 'ADV']:  # Enfocarse en categorías principales
+            morphology_analysis.append({
+                'text': token.text,
+                'lemma': token.lemma_,
+                'pos': token.pos_,
+                'tag': token.tag_,
+                'dep': token.dep_,
+                'shape': token.shape_,
+                'is_alpha': token.is_alpha,
+                'is_stop': token.is_stop,
+                'morph': str(token.morph)
+            })
+    return morphology_analysis
+
+#################################################################################################
+def get_sentence_structure_analysis(doc):
+    """
+    Analiza la estructura de las oraciones en el texto.
+    """
+    sentence_analysis = []
+    for sent in doc.sents:
+        sentence_analysis.append({
+            'text': sent.text,
+            'root': sent.root.text,
+            'root_pos': sent.root.pos_,
+            'num_tokens': len(sent),
+            'num_words': len([token for token in sent if token.is_alpha]),
+            'subjects': [token.text for token in sent if "subj" in token.dep_],
+            'objects': [token.text for token in sent if "obj" in token.dep_],
+            'verbs': [token.text for token in sent if token.pos_ == "VERB"]
+        })
+    return sentence_analysis
+    
+#################################################################################################
+def perform_advanced_morphosyntactic_analysis(text, nlp):
+    """
+    Realiza un análisis morfosintáctico avanzado del texto.
+    """
+    doc = nlp(text)
+    return {
+        'pos_analysis': get_detailed_pos_analysis(doc),
+        'morphological_analysis': get_morphological_analysis(doc),
+        'sentence_structure': get_sentence_structure_analysis(doc),
+        'arc_diagram': generate_arc_diagram(doc, nlp.lang)
+    }
+
+# Al final del archivo morph_analysis.py
+__all__ = ['get_repeated_words_colors', 'highlight_repeated_words', 'generate_arc_diagram', 'perform_advanced_morphosyntactic_analysis', 'POS_COLORS', 'POS_TRANSLATIONS']