# modules/semantic/semantic_live_interface.py import streamlit as st from streamlit_float import * from streamlit_antd_components import * import pandas as pd import logging # Configuración del logger logger = logging.getLogger(__name__) # Importaciones locales from .semantic_process import ( process_semantic_input, format_semantic_results ) from ..utils.widget_utils import generate_unique_key from ..database.semantic_mongo_db import store_student_semantic_result from ..database.chat_mongo_db import store_chat_history, get_chat_history def display_semantic_live_interface(lang_code, nlp_models, semantic_t): """ Interfaz para el análisis semántico en vivo con proporciones de columna ajustadas """ try: # 1. Inicializar el estado de la sesión de manera más robusta if 'semantic_live_state' not in st.session_state: st.session_state.semantic_live_state = { 'analysis_count': 0, 'current_text': '', 'last_result': None, 'text_changed': False } # 2. Función para manejar cambios en el texto def on_text_change(): current_text = st.session_state.semantic_live_text st.session_state.semantic_live_state['current_text'] = current_text st.session_state.semantic_live_state['text_changed'] = True # 3. Crear columnas con nueva proporción (1:3) input_col, result_col = st.columns([1, 3]) # Columna izquierda: Entrada de texto with input_col: st.subheader(semantic_t.get('enter_text', 'Ingrese su texto')) # Área de texto con manejo de eventos text_input = st.text_area( semantic_t.get('text_input_label', 'Escriba o pegue su texto aquí'), height=500, key="semantic_live_text", value=st.session_state.semantic_live_state.get('current_text', ''), on_change=on_text_change, label_visibility="collapsed" # Oculta el label para mayor estabilidad ) # Botón de análisis y procesamiento analyze_button = st.button( semantic_t.get('analyze_button', 'Analizar'), key="semantic_live_analyze", type="primary", icon="🔍", disabled=not text_input, use_container_width=True ) if analyze_button and text_input: try: with st.spinner(semantic_t.get('processing', 'Procesando...')): analysis_result = process_semantic_input( text_input, lang_code, nlp_models, semantic_t ) if analysis_result['success']: st.session_state.semantic_live_state['last_result'] = analysis_result st.session_state.semantic_live_state['analysis_count'] += 1 st.session_state.semantic_live_state['text_changed'] = False store_student_semantic_result( st.session_state.username, text_input, analysis_result['analysis'] ) else: st.error(analysis_result.get('message', 'Error en el análisis')) except Exception as e: logger.error(f"Error en análisis: {str(e)}") st.error(semantic_t.get('error_processing', 'Error al procesar el texto')) # Columna derecha: Visualización de resultados with result_col: st.subheader(semantic_t.get('live_results', 'Resultados en vivo')) if 'last_result' in st.session_state.semantic_live_state and \ st.session_state.semantic_live_state['last_result'] is not None: analysis = st.session_state.semantic_live_state['last_result']['analysis'] if 'key_concepts' in analysis and analysis['key_concepts'] and \ 'concept_graph' in analysis and analysis['concept_graph'] is not None: st.markdown(""" """, unsafe_allow_html=True) with st.container(): # Conceptos en una sola línea concepts_html = """