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Delete modules/semantic/semantic_interface.py
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modules/semantic/semantic_interface.py
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#modules/semantic/semantic_interface.py
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import streamlit as st
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from streamlit_float import *
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from streamlit_antd_components import *
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from streamlit.components.v1 import html
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import spacy_streamlit
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import io
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from io import BytesIO
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import base64
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import matplotlib.pyplot as plt
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import pandas as pd
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import re
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import logging
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# Configuración del logger
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logger = logging.getLogger(__name__)
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# Importaciones locales
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from .semantic_process import (
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process_semantic_input,
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format_semantic_results
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)
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from ..utils.widget_utils import generate_unique_key
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from ..database.semantic_mongo_db import store_student_semantic_result
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from ..database.chat_mongo_db import store_chat_history, get_chat_history
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# from ..database.semantic_export import export_user_interactions
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###############################
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# En semantic_interface.py
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def display_semantic_interface(text, lang_code, nlp_models, t, semantic_t):
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try:
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# 1. Inicializar el estado de la sesión
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if 'semantic_state' not in st.session_state:
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st.session_state.semantic_state = {
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'analysis_count': 0,
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'last_analysis': None,
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'current_file': None,
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'pending_analysis': False # Nuevo flag para controlar el análisis pendiente
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}
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# 2. Área de carga de archivo con mensaje informativo
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st.info(semantic_t.get('initial_instruction',
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'Para comenzar un nuevo análisis semántico, cargue un archivo de texto (.txt)'))
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uploaded_file = st.file_uploader(
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semantic_t.get('semantic_file_uploader', 'Upload a text file for semantic analysis'),
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type=['txt'],
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key=f"semantic_file_uploader_{st.session_state.semantic_state['analysis_count']}"
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)
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# 2.1 Verificar si hay un archivo cargado y un análisis pendiente
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if uploaded_file is not None and st.session_state.semantic_state.get('pending_analysis', False):
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try:
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with st.spinner(semantic_t.get('processing', 'Processing...')):
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# Realizar análisis
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text = uploaded_file.getvalue().decode('utf-8')
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analysis_result = process_semantic_input(
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text,
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lang_code,
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nlp_models,
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t,
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semantic_t
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)
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if analysis_result['success']:
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# Guardar resultado
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st.session_state.semantic_result = analysis_result
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st.session_state.semantic_state['analysis_count'] += 1
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st.session_state.semantic_state['current_file'] = uploaded_file.name
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# Guardar en base de datos
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storage_success = store_student_semantic_result(
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st.session_state.username,
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text_content,
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analysis_result['analysis']
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)
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if storage_success:
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st.success(
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semantic_t.get('analysis_complete',
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'Análisis completado y guardado. Para realizar un nuevo análisis, cargue otro archivo.')
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)
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else:
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st.error(semantic_t.get('error_message', 'Error saving analysis'))
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else:
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st.error(analysis_result['message'])
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# Restablecer el flag de análisis pendiente
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st.session_state.semantic_state['pending_analysis'] = False
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except Exception as e:
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logger.error(f"Error en análisis semántico: {str(e)}")
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st.error(semantic_t.get('error_processing', f'Error processing text: {str(e)}'))
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# Restablecer el flag de análisis pendiente en caso de error
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st.session_state.semantic_state['pending_analysis'] = False
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# 3. Columnas para los botones y mensajes
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col1, col2 = st.columns([1,4])
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# 4. Botón de análisis
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with col1:
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analyze_button = st.button(
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semantic_t.get('semantic_analyze_button', 'Analyze'),
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key=f"semantic_analyze_button_{st.session_state.semantic_state['analysis_count']}",
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type="primary",
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icon="🔍",
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disabled=uploaded_file is None,
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use_container_width=True
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)
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# 5. Procesar análisis
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if analyze_button and uploaded_file is not None:
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# En lugar de realizar el análisis inmediatamente, establecer el flag
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st.session_state.semantic_state['pending_analysis'] = True
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# Forzar la recarga de la aplicación
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st.rerun()
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# 6. Mostrar resultados previos o mensaje inicial
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elif 'semantic_result' in st.session_state and st.session_state.semantic_result is not None:
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# Mostrar mensaje sobre el análisis actual
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st.info(
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semantic_t.get('current_analysis_message',
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'Mostrando análisis del archivo: {}. Para realizar un nuevo análisis, cargue otro archivo.'
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).format(st.session_state.semantic_state["current_file"])
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)
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display_semantic_results(
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st.session_state.semantic_result,
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lang_code,
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semantic_t
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)
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else:
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st.info(semantic_t.get('upload_prompt', 'Cargue un archivo para comenzar el análisis'))
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except Exception as e:
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logger.error(f"Error general en interfaz semántica: {str(e)}")
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st.error(semantic_t.get('general_error', "Se produjo un error. Por favor, intente de nuevo."))
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#######################################
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def display_semantic_results(semantic_result, lang_code, semantic_t):
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"""
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Muestra los resultados del análisis semántico de conceptos clave.
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"""
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if semantic_result is None or not semantic_result['success']:
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st.warning(semantic_t.get('no_results', 'No results available'))
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return
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analysis = semantic_result['analysis']
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# Mostrar conceptos clave en formato horizontal
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st.subheader(semantic_t.get('key_concepts', 'Key Concepts'))
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if 'key_concepts' in analysis and analysis['key_concepts']:
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# Crear tabla de conceptos
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df = pd.DataFrame(
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analysis['key_concepts'],
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columns=[
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semantic_t.get('concept', 'Concept'),
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semantic_t.get('frequency', 'Frequency')
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]
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)
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# Convertir DataFrame a formato horizontal
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st.write(
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"""
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<style>
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.concept-table {
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display: flex;
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flex-wrap: wrap;
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gap: 10px;
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margin-bottom: 20px;
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}
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.concept-item {
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background-color: #f0f2f6;
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border-radius: 5px;
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padding: 8px 12px;
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display: flex;
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align-items: center;
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gap: 8px;
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}
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.concept-name {
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font-weight: bold;
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}
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.concept-freq {
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color: #666;
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font-size: 0.9em;
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}
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</style>
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<div class="concept-table">
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""" +
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''.join([
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f'<div class="concept-item"><span class="concept-name">{concept}</span>'
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f'<span class="concept-freq">({freq:.2f})</span></div>'
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for concept, freq in df.values
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]) +
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"</div>",
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unsafe_allow_html=True
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)
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else:
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st.info(semantic_t.get('no_concepts', 'No key concepts found'))
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# Gráfico de conceptos
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# st.subheader(semantic_t.get('concept_graph', 'Concepts Graph'))
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#Colocar aquí el bloque de código
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if 'concept_graph' in analysis and analysis['concept_graph'] is not None:
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try:
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# Aplicar estilos directamente al contenedor de Streamlit
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st.markdown(
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"""
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<style>
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.stContainer {
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background-color: white;
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border-radius: 10px;
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padding: 20px;
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box-shadow: 0 2px 4px rgba(0,0,0,0.1);
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margin: 10px 0;
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}
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.button-container {
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display: flex;
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gap: 10px;
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margin: 10px 0;
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}
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</style>
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""",
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unsafe_allow_html=True
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)
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# Mostrar el gráfico directamente sin el div adicional
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graph_bytes = analysis['concept_graph']
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graph_base64 = base64.b64encode(graph_bytes).decode()
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st.markdown(
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f'<img src="data:image/png;base64,{graph_base64}" alt="Concept Graph" style="width:100%;"/>',
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unsafe_allow_html=True
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)
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# Expandible con la interpretación
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with st.expander("📊 " + semantic_t.get('semantic_graph_interpretation', "Interpretación del gráfico semántico")):
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st.markdown(f"""
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- 🔀 {semantic_t.get('semantic_arrow_meaning', 'Las flechas indican la dirección de la relación entre conceptos')}
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- 🎨 {semantic_t.get('semantic_color_meaning', 'Los colores más intensos indican conceptos más centrales en el texto')}
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- ⭕ {semantic_t.get('semantic_size_meaning', 'El tamaño de los nodos representa la frecuencia del concepto')}
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- ↔️ {semantic_t.get('semantic_thickness_meaning', 'El grosor de las líneas indica la fuerza de la conexión')}
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""")
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# Contenedor para botones
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col1, col2 = st.columns([1,4])
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with col1:
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st.download_button(
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label="📥 " + semantic_t.get('download_semantic_network_graph', "Descargar gráfico de red semántica"),
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data=graph_bytes,
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file_name="semantic_graph.png",
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mime="image/png",
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use_container_width=True
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)
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except Exception as e:
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logger.error(f"Error displaying graph: {str(e)}")
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st.error(semantic_t.get('graph_error', 'Error displaying the graph'))
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