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Delete modules/semantic/semantic_process.py

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  1. modules/semantic/semantic_process.py +0 -116
modules/semantic/semantic_process.py DELETED
@@ -1,116 +0,0 @@
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- # modules/semantic/semantic_process.py
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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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-
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- ##############################################################
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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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-
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- ##############################################################################
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- from ..database.semantic_mongo_db import store_student_semantic_result
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- #####################################################################
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- logger = logging.getLogger(__name__)
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- #########################################################
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-
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- def process_semantic_input(text, lang_code, nlp_models, t, semantic_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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-
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- # Realizar el análisis semántico
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- nlp = nlp_models[lang_code]
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- ####################################################################
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- analysis_result = perform_semantic_analysis(text, nlp, lang_code, semantic_t)
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- #########################################################################
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-
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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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-
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- logger.info("Análisis semántico completado. Guardando resultados...")
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-
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- # Intentar guardar en la base de datos
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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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- )
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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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-
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- # Devolver el resultado incluso si falla el guardado
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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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-
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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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-
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-
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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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-
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- formatted_sections = []
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- analysis = analysis_result['analysis']
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-
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- # Formatear conceptos clave
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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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-
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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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-
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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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-
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- __all__ = [
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- 'process_semantic_input',
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- 'format_semantic_results'
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- ]