File size: 10,586 Bytes
831e193 b6ee9f7 831e193 b6ee9f7 831e193 b6ee9f7 831e193 b6ee9f7 831e193 b6ee9f7 831e193 b6ee9f7 831e193 b6ee9f7 831e193 b6ee9f7 831e193 b6ee9f7 831e193 b6ee9f7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 |
import streamlit as st
from streamlit_float import *
from streamlit_antd_components import *
from streamlit.components.v1 import html
import spacy
from spacy import displacy
import spacy_streamlit
import pandas as pd
import base64
import re
from .morphosyntax_process import (
process_morphosyntactic_input,
format_analysis_results,
perform_advanced_morphosyntactic_analysis,
get_repeated_words_colors,
highlight_repeated_words,
POS_COLORS,
POS_TRANSLATIONS
)
from ..utils.widget_utils import generate_unique_key
from ..database.morphosintax_mongo_db import store_student_morphosyntax_result
from ..database.chat_mongo_db import store_chat_history, get_chat_history
import logging
logger = logging.getLogger(__name__)
def display_morphosyntax_interface(lang_code, nlp_models, morpho_t):
try:
# CSS para mejorar la estabilidad y prevenir saltos
st.markdown("""
<style>
.stTextArea textarea {
font-size: 1rem;
line-height: 1.5;
resize: vertical;
}
.block-container {
padding-top: 1rem;
padding-bottom: 1rem;
}
.stExpander {
border: none;
box-shadow: 0 1px 2px rgba(0,0,0,0.1);
margin-bottom: 1rem;
}
.legend-container {
position: sticky;
top: 0;
background: white;
z-index: 100;
padding: 0.5rem 0;
border-bottom: 1px solid #eee;
}
</style>
""", unsafe_allow_html=True)
# 1. Inicializar el estado
if 'morphosyntax_state' not in st.session_state:
st.session_state.morphosyntax_state = {
'input_text': "",
'analysis_count': 0,
'last_analysis': None,
'current_tab': 0
}
# 2. Contenedor principal con diseño sticky
with st.container():
# Campo de entrada de texto
input_key = f"morpho_input_{st.session_state.morphosyntax_state['analysis_count']}"
sentence_input = st.text_area(
morpho_t.get('morpho_input_label', 'Enter text to analyze'),
height=150,
placeholder=morpho_t.get('morpho_input_placeholder', 'Enter your text here...'),
key=input_key,
on_change=lambda: None # Previene recargas innecesarias
)
# 3. Botón de análisis centrado
col1, col2, col3 = st.columns([2,1,2])
with col1:
analyze_button = st.button(
morpho_t.get('morpho_analyze_button', 'Analyze Morphosyntax'),
key=f"morpho_button_{st.session_state.morphosyntax_state['analysis_count']}",
type="primary",
icon="🔍",
disabled=not bool(sentence_input.strip()),
use_container_width=True
)
# 4. Procesar análisis
if analyze_button and sentence_input.strip():
try:
with st.spinner(morpho_t.get('processing', 'Processing...')):
doc = nlp_models[lang_code](sentence_input)
advanced_analysis = perform_advanced_morphosyntactic_analysis(
sentence_input,
nlp_models[lang_code]
)
st.session_state.morphosyntax_result = {
'doc': doc,
'advanced_analysis': advanced_analysis
}
st.session_state.morphosyntax_state['analysis_count'] += 1
# Guardar resultado
if store_student_morphosyntax_result(
username=st.session_state.username,
text=sentence_input,
arc_diagrams=advanced_analysis['arc_diagrams']
):
st.success(morpho_t.get('success_message', 'Analysis saved successfully'))
st.session_state.morphosyntax_state['current_tab'] = 0
display_morphosyntax_results(
st.session_state.morphosyntax_result,
lang_code,
morpho_t
)
else:
st.error(morpho_t.get('error_message', 'Error saving analysis'))
except Exception as e:
logger.error(f"Error en análisis morfosintáctico: {str(e)}")
st.error(morpho_t.get('error_processing', f'Error processing text: {str(e)}'))
# 5. Mostrar resultados previos
elif 'morphosyntax_result' in st.session_state and st.session_state.morphosyntax_result:
display_morphosyntax_results(
st.session_state.morphosyntax_result,
lang_code,
morpho_t
)
elif not sentence_input.strip():
st.info(morpho_t.get('morpho_initial_message', 'Enter text to begin analysis'))
except Exception as e:
logger.error(f"Error general en display_morphosyntax_interface: {str(e)}")
st.error("Se produjo un error. Por favor, intente de nuevo.")
def display_morphosyntax_results(result, lang_code, morpho_t):
if result is None:
st.warning(morpho_t.get('no_results', 'No results available'))
return
doc = result['doc']
advanced_analysis = result['advanced_analysis']
# Leyenda fija en la parte superior
with st.container():
st.markdown(f"##### {morpho_t.get('legend', 'Legend: Grammatical categories')}")
legend_html = "<div class='legend-container'><div style='display: flex; flex-wrap: wrap;'>"
for pos, color in POS_COLORS.items():
if pos in POS_TRANSLATIONS[lang_code]:
legend_html += f"<div style='margin-right: 10px;'><span style='background-color: {color}; padding: 2px 5px;'>{POS_TRANSLATIONS[lang_code][pos]}</span></div>"
legend_html += "</div></div>"
st.markdown(legend_html, unsafe_allow_html=True)
# Palabras repetidas
with st.expander(morpho_t.get('repeated_words', 'Repeated words'), expanded=True):
word_colors = get_repeated_words_colors(doc)
highlighted_text = highlight_repeated_words(doc, word_colors)
st.markdown(highlighted_text, unsafe_allow_html=True)
# Análisis sintáctico (diagramas de arco)
with st.expander(morpho_t.get('arc_diagram', 'Syntactic analysis: Arc diagram'), expanded=True):
sentences = list(doc.sents)
for i, sent in enumerate(sentences):
st.subheader(f"{morpho_t.get('sentence', 'Sentence')} {i+1}")
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)
st.write(html, unsafe_allow_html=True)
# Estructura de oraciones
with st.expander(morpho_t.get('sentence_structure', 'Sentence structure'), expanded=True):
for i, sent_analysis in enumerate(advanced_analysis['sentence_structure']):
sentence_str = (
f"**{morpho_t.get('sentence', 'Sentence')} {i+1}** "
f"{morpho_t.get('root', 'Root')}: {sent_analysis['root']} ({sent_analysis['root_pos']}) -- "
f"{morpho_t.get('subjects', 'Subjects')}: {', '.join(sent_analysis['subjects'])} -- "
f"{morpho_t.get('objects', 'Objects')}: {', '.join(sent_analysis['objects'])} -- "
f"{morpho_t.get('verbs', 'Verbs')}: {', '.join(sent_analysis['verbs'])}"
)
st.markdown(sentence_str)
# Análisis de categorías gramaticales
with st.expander(morpho_t.get('pos_analysis', 'Part of speech'), expanded=True):
pos_df = pd.DataFrame(advanced_analysis['pos_analysis'])
pos_df['pos'] = pos_df['pos'].map(lambda x: POS_TRANSLATIONS[lang_code].get(x, x))
pos_df = pos_df.rename(columns={
'pos': morpho_t.get('grammatical_category', 'Grammatical category'),
'count': morpho_t.get('count', 'Count'),
'percentage': morpho_t.get('percentage', 'Percentage'),
'examples': morpho_t.get('examples', 'Examples')
})
st.dataframe(pos_df, use_container_width=True)
# Análisis morfológico
with st.expander(morpho_t.get('morphological_analysis', 'Morphological Analysis'), expanded=True):
morph_df = pd.DataFrame(advanced_analysis['morphological_analysis'])
column_mapping = {
'text': morpho_t.get('word', 'Word'),
'lemma': morpho_t.get('lemma', 'Lemma'),
'pos': morpho_t.get('grammatical_category', 'Grammatical category'),
'dep': morpho_t.get('dependency', 'Dependency'),
'morph': morpho_t.get('morphology', 'Morphology')
}
morph_df = morph_df.rename(columns=column_mapping)
# Traducir categorías gramaticales
grammatical_category = morpho_t.get('grammatical_category', 'Grammatical category')
morph_df[grammatical_category] = morph_df[grammatical_category].map(
lambda x: POS_TRANSLATIONS[lang_code].get(x, x)
)
# Aplicar traducciones de dependencias y morfología
dependency = morpho_t.get('dependency', 'Dependency')
morphology = morpho_t.get('morphology', 'Morphology')
def translate_morph(morph_string, lang_code):
for key, value in morph_translations[lang_code].items():
morph_string = morph_string.replace(key, value)
return morph_string
morph_df[dependency] = morph_df[dependency].map(
lambda x: dep_translations[lang_code].get(x, x)
)
morph_df[morphology] = morph_df[morphology].apply(
lambda x: translate_morph(x, lang_code)
)
st.dataframe(morph_df, use_container_width=True) |