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import streamlit as st
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import matplotlib.pyplot as plt
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import io
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import base64
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import logging
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from ..text_analysis.semantic_analysis import (
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perform_semantic_analysis,
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identify_key_concepts,
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create_concept_graph,
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visualize_concept_graph
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)
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from ..database.semantic_mongo_db import store_student_semantic_result
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logger = logging.getLogger(__name__)
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def process_semantic_input(text, lang_code, nlp_models, t):
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"""
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Procesa el texto ingresado para realizar el análisis semántico.
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"""
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try:
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logger.info(f"Iniciando análisis semántico para texto de {len(text)} caracteres")
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nlp = nlp_models[lang_code]
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analysis_result = perform_semantic_analysis(text, nlp, lang_code)
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if not analysis_result['success']:
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return {
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'success': False,
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'message': analysis_result['error'],
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'analysis': None
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}
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logger.info("Análisis semántico completado. Guardando resultados...")
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try:
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store_result = store_student_semantic_result(
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st.session_state.username,
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text,
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analysis_result,
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lang_code
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)
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if not store_result:
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logger.warning("No se pudo guardar el análisis en la base de datos")
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except Exception as db_error:
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logger.error(f"Error al guardar en base de datos: {str(db_error)}")
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return {
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'success': True,
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'message': t.get('success_message', 'Analysis completed successfully'),
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'analysis': {
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'key_concepts': analysis_result['key_concepts'],
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'concept_graph': analysis_result['concept_graph']
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}
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}
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except Exception as e:
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logger.error(f"Error en process_semantic_input: {str(e)}")
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return {
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'success': False,
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'message': str(e),
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'analysis': None
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}
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def format_semantic_results(analysis_result, t):
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"""
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Formatea los resultados del análisis para su visualización.
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"""
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try:
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if not analysis_result['success']:
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return {
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'formatted_text': analysis_result['message'],
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'visualizations': None
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}
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formatted_sections = []
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analysis = analysis_result['analysis']
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if 'key_concepts' in analysis:
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concepts_section = [f"### {t.get('key_concepts', 'Key Concepts')}"]
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concepts_section.extend([
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f"- {concept}: {frequency:.2f}"
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for concept, frequency in analysis['key_concepts']
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])
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formatted_sections.append('\n'.join(concepts_section))
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return {
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'formatted_text': '\n\n'.join(formatted_sections),
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'visualizations': {
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'concept_graph': analysis.get('concept_graph')
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}
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}
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except Exception as e:
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logger.error(f"Error en format_semantic_results: {str(e)}")
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return {
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'formatted_text': str(e),
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'visualizations': None
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}
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__all__ = [
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'process_semantic_input',
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'format_semantic_results'
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] |
