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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']) | |