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# app.py
import os
os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
os.environ['KMP_DUPLICATE_LIB_OK']='TRUE'
import streamlit as st
import spacy
from spacy import displacy
import re
# Configure the page to use the full width
st.set_page_config(
page_title="AIdeaText",
layout="wide",
page_icon="random"
)
from modules.syntax_analysis import highlight_repeated_words, get_repeated_words_colors, POS_COLORS, POS_TRANSLATIONS, visualize_syntax
@st.cache_resource
def load_spacy_models():
return {
'es': spacy.load("es_core_news_lg"),
'en': spacy.load("en_core_web_lg"),
'fr': spacy.load("fr_core_news_lg")
}
# Load spaCy models
nlp_models = load_spacy_models()
# Language selection
languages = {
'Español': 'es',
'English': 'en',
'Français': 'fr'
}
selected_lang = st.sidebar.selectbox("Select Language / Seleccione el idioma / Choisissez la langue", list(languages.keys()))
lang_code = languages[selected_lang]
# Translations
translations = {
'es': {
'title': "AIdeaText - Análisis morfológico y sintáctico",
'input_label': "Ingrese un texto para analizar (máx. 5,000 palabras):",
'input_placeholder': "Escriba o pegue su texto aquí...",
'analyze_button': "Analizar texto",
'repeated_words': "Palabras repetidas",
'legend': "Leyenda: Categorías gramaticales",
'arc_diagram': "Análisis sintáctico: Diagrama de arco",
'network_diagram': "Análisis sintáctico: Diagrama de red",
'sentence': "Oración"
},
'en': {
'title': "AIdeaText - Morphological and Syntactic Analysis",
'input_label': "Enter a text to analyze (max 5,000 words):",
'input_placeholder': "Type or paste your text here...",
'analyze_button': "Analyze text",
'repeated_words': "Repeated words",
'legend': "Legend: Grammatical categories",
'arc_diagram': "Syntactic analysis: Arc diagram",
'network_diagram': "Syntactic analysis: Network diagram",
'sentence': "Sentence"
},
'fr': {
'title': "AIdeaText - Analyse morphologique et syntaxique",
'input_label': "Entrez un texte à analyser (max 5 000 mots) :",
'input_placeholder': "Tapez ou collez votre texte ici...",
'analyze_button': "Analyser le texte",
'repeated_words': "Mots répétés",
'legend': "Légende : Catégories grammaticales",
'arc_diagram': "Analyse syntaxique : Diagramme en arc",
'network_diagram': "Analyse syntaxique : Diagramme de réseau",
'sentence': "Phrase"
}
}
# Use translations
t = translations[lang_code]
st.markdown(f"### {t['title']}")
# Text Input with instructions
sentence_input = st.text_area(t['input_label'], height=150, placeholder=t['input_placeholder'])
if st.button(t['analyze_button']):
if sentence_input:
doc = nlp_models[lang_code](sentence_input)
# Highlighted Repeated Words
with st.expander(t['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)
# Legend for grammatical categories
st.markdown(f"##### {t['legend']}")
legend_html = "<div style='display: flex; flex-wrap: wrap;'>"
for pos, color in POS_COLORS.items():
if pos in POS_TRANSLATIONS:
legend_html += f"<div style='margin-right: 10px;'><span style='background-color: {color}; padding: 2px 5px;'>{POS_TRANSLATIONS[pos]}</span></div>"
legend_html += "</div>"
st.markdown(legend_html, unsafe_allow_html=True)
# Arc Diagram
with st.expander(t['arc_diagram'], expanded=True):
sentences = list(doc.sents)
for i, sent in enumerate(sentences):
st.subheader(f"{t['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)
# Network graph
with st.expander(t['network_diagram'], expanded=True):
fig = visualize_syntax(sentence_input, nlp_models[lang_code])
st.pyplot(fig) |