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# Importaciones generales
import streamlit as st
import re
import io
from io import BytesIO
import base64
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import pandas as pd
import numpy as np
import time
from datetime import datetime
from streamlit_player import st_player # Necesitarás instalar esta librería: pip install streamlit-player
from spacy import displacy
import logging
import random
from ..utils.widget_utils import generate_unique_key
from ..database.morphosintax_mongo_db import store_student_morphosyntax_result
from ..database.chat_db import store_chat_history
from ..database.morphosintaxis_export import export_user_interactions
import logging
logger = logging.getLogger(__name__)
def display_semantic_analysis_interface(nlp_models, lang_code):
t = translations[lang_code]
st.header(t['title'])
# Opción para introducir texto
text_input = st.text_area(
t['text_input_label'],
height=150,
placeholder=t['text_input_placeholder'],
)
# Opción para cargar archivo
uploaded_file = st.file_uploader(t['file_uploader'], type=['txt'])
if st.button(t['analyze_button']):
if text_input or uploaded_file is not None:
if uploaded_file:
text_content = uploaded_file.getvalue().decode('utf-8')
else:
text_content = text_input
# Realizar el análisis
analysis_result = perform_semantic_analysis(text_content, nlp_models[lang_code], lang_code)
# Guardar el resultado en el estado de la sesión
st.session_state.semantic_result = analysis_result
# Mostrar resultados
display_semantic_results(st.session_state.semantic_result, lang_code, t)
# Guardar el resultado del análisis
if store_semantic_result(st.session_state.username, text_content, analysis_result):
st.success(t['success_message'])
else:
st.error(t['error_message'])
else:
st.warning(t['warning_message'])
elif 'semantic_result' in st.session_state:
# Si hay un resultado guardado, mostrarlo
display_semantic_results(st.session_state.semantic_result, lang_code, t)
else:
st.info(t['initial_message']) # Asegúrate de que 'initial_message' esté en tus traducciones
def display_semantic_results(result, lang_code, t):
if result is None:
st.warning(t['no_results']) # Asegúrate de que 'no_results' esté en tus traducciones
return
# Mostrar conceptos clave
with st.expander(t['key_concepts'], expanded=True):
concept_text = " | ".join([f"{concept} ({frequency:.2f})" for concept, frequency in result['key_concepts']])
st.write(concept_text)
# Mostrar el gráfico de relaciones conceptuales
with st.expander(t['conceptual_relations'], expanded=True):
st.pyplot(result['relations_graph'])
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