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import spacy
from spacy import displacy
from streamlit.components.v1 import html
import base64
from collections import Counter
import re
from ..utils.widget_utils import generate_unique_key
import logging
logger = logging.getLogger(__name__)
# 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': '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',
}
}
def generate_arc_diagram(doc):
arc_diagrams = []
for sent in doc.sents:
words = [token.text for token in sent]
# Calculamos el ancho del SVG basado en la longitud de la oración
svg_width = max(600, len(words) * 120)
# Altura fija para cada oración
svg_height = 350 # Controla la altura del SVG
# Renderizamos el diagrama de dependencias
html = displacy.render(sent, style="dep", options={
"add_lemma":False, # Introduced in version 2.2.4, this argument prints the lemma’s in a separate row below the token texts.
"arrow_spacing": 12, #This argument is used for adjusting the spacing between arrows in px to avoid overlaps.
"arrow_width": 2, #This argument is used for adjusting the width of arrow head in px.
"arrow_stroke": 2, #This argument is used for adjusting the width of arrow path in px.
"collapse_punct": True, #It attaches punctuation to the tokens.
"collapse_phrases": False, # This argument merges the noun phrases into one token.
"compact":False, # If you will take this argument as true, you will get the “Compact mode” with square arrows that takes up less space.
"color": "#ffffff",
"bg": "#0d6efd",
"compact": False, #Put the value of this argument True, if you want to use fine-grained part-of-speech tags (Token.tag_), instead of coarse-grained tags (Token.pos_).
"distance": 100, # Aumentamos la distancia entre palabras
"fine_grained": False, #Put the value of this argument True, if you want to use fine-grained part-of-speech tags (Token.tag_), instead of coarse-grained tags (Token.pos_).
"offset_x": 55, # This argument is used for spacing on left side of the SVG in px.
"word_spacing": 25, #This argument is used for adjusting the vertical spacing between words and arcs in px.
})
# Ajustamos el tamaño del SVG y el viewBox
html = re.sub(r'width="(\d+)"', f'width="{svg_width}"', html)
html = re.sub(r'height="(\d+)"', f'height="{svg_height}"', html)
html = re.sub(r'<svg', f'<svg viewBox="0 0 {svg_width} {svg_height}"', html)
#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)
# Movemos todo el contenido hacia abajo
#html = html.replace('<g', f'<g transform="translate(50, {svg_height - 200})"')
# Movemos todo el contenido hacia arriba para eliminar el espacio vacío en la parte superior
html = re.sub(r'<g transform="translate\((\d+),(\d+)\)"',
lambda m: f'<g transform="translate({m.group(1)},10)"', html)
# Ajustamos la posición de las etiquetas de las palabras
html = html.replace('dy="1em"', 'dy="-1em"')
# Ajustamos la posición de las etiquetas POS
html = html.replace('dy="0.25em"', 'dy="-3em"')
# Aumentamos el tamaño de la fuente para las etiquetas POS
html = html.replace('.displacy-tag {', '.displacy-tag { font-size: 14px;')
# Rotamos las etiquetas de las palabras para mejorar la legibilidad
#html = html.replace('class="displacy-label"', 'class="displacy-label" transform="rotate(30)"')
arc_diagrams.append(html)
return arc_diagrams
##################################################################################################################################
def perform_advanced_morphosyntactic_analysis(text, nlp):
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_diagrams': generate_arc_diagram(doc),
'repeated_words': get_repeated_words_colors(doc)
}
__all__ = ['perform_advanced_morphosyntactic_analysis'] |