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#modules/semantic/semantic_interface.py
# Importaciones necesarias
import streamlit as st
from streamlit_float import *
from streamlit_antd_components import *
from streamlit.components.v1 import html
import io
from io import BytesIO
import base64
import matplotlib.pyplot as plt
import pandas as pd
import re
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.semantic_export import export_user_interactions
#modules/semantic/semantic_interface.py
# [Mantener las importaciones igual...]
def display_semantic_interface(lang_code, nlp_models, semantic_t):
"""
Interfaz para el análisis semántico con controles fijos y manejo de estado mejorado
"""
try:
# Inicializar estados
if 'semantic_analysis_counter' not in st.session_state:
st.session_state.semantic_analysis_counter = 0
if 'semantic_file_content' not in st.session_state:
st.session_state.semantic_file_content = None
if 'semantic_analysis_done' not in st.session_state:
st.session_state.semantic_analysis_done = False
# Contenedor principal con bordes
with st.container():
st.markdown("### Semantic Analysis Controls")
st.markdown("---")
# Sección de carga de archivo
col_file, _ = st.columns([6, 4])
with col_file:
uploaded_file = st.file_uploader(
semantic_t.get('file_uploader', 'Upload a text file for analysis'),
type=['txt'],
key=f"semantic_file_uploader_{st.session_state.semantic_analysis_counter}",
on_change=lambda: handle_file_upload(uploaded_file)
)
# Contenedor fijo para botones
st.markdown("---")
col1, col2, col3, col4 = st.columns([2, 2, 2, 6])
with col1:
analyze_button = st.button(
semantic_t.get('analyze_button', 'Analyze Text'),
disabled=not st.session_state.semantic_file_content,
use_container_width=True,
key="analyze_semantic"
)
with col2:
export_button = st.button(
semantic_t.get('export_button', 'Export Analysis'),
disabled=not st.session_state.semantic_analysis_done,
use_container_width=True,
key="export_semantic"
)
with col3:
clear_button = st.button(
semantic_t.get('clear_button', 'Clear Analysis'),
disabled=not st.session_state.semantic_analysis_done,
use_container_width=True,
key="clear_semantic"
)
st.markdown("---")
# Procesar análisis
if analyze_button and st.session_state.semantic_file_content:
try:
with st.spinner(semantic_t.get('processing', 'Processing...')):
analysis_result = process_semantic_input(
st.session_state.semantic_file_content,
lang_code,
nlp_models,
semantic_t
)
if analysis_result['success']:
st.session_state.semantic_result = analysis_result
st.session_state.semantic_analysis_done = True
st.session_state.semantic_analysis_counter += 1
# Guardar en la base de datos
if store_student_semantic_result(
st.session_state.username,
st.session_state.semantic_file_content,
analysis_result['analysis']
):
st.success(semantic_t.get('success_message', 'Analysis saved successfully'))
# Mostrar resultados
display_semantic_results(
analysis_result,
lang_code,
semantic_t
)
else:
st.error(semantic_t.get('error_message', 'Error saving analysis'))
else:
st.error(analysis_result['message'])
except Exception as e:
logger.error(f"Error en análisis semántico: {str(e)}")
st.error(semantic_t.get('error_processing', f'Error processing text: {str(e)}'))
# Manejo de exportación
if export_button and st.session_state.semantic_analysis_done:
pdf_buffer = export_user_interactions(st.session_state.username, 'semantic')
st.download_button(
label=semantic_t.get('download_pdf', 'Download PDF'),
data=pdf_buffer,
file_name="semantic_analysis.pdf",
mime="application/pdf",
key=f"semantic_download_{st.session_state.semantic_analysis_counter}"
)
# Manejo de limpieza
if clear_button:
st.session_state.semantic_file_content = None
st.session_state.semantic_analysis_done = False
st.session_state.semantic_result = None
st.rerun()
# Mostrar resultados previos o mensaje inicial
if st.session_state.semantic_analysis_done and 'semantic_result' in st.session_state:
display_semantic_results(
st.session_state.semantic_result,
lang_code,
semantic_t
)
elif not st.session_state.semantic_file_content:
st.info(semantic_t.get('initial_message', 'Upload a file to begin analysis'))
except Exception as e:
logger.error(f"Error general en interfaz semántica: {str(e)}")
st.error("Se produjo un error. Por favor, intente de nuevo.")
def handle_file_upload(uploaded_file):
"""Maneja la carga de archivos y mantiene el estado"""
if uploaded_file is not None:
try:
content = uploaded_file.getvalue().decode('utf-8')
st.session_state.semantic_file_content = content
st.session_state.page = 'semantic' # Mantener en la página semántica
except Exception as e:
logger.error(f"Error al cargar archivo: {str(e)}")
st.error("Error al cargar el archivo. Asegúrese de que es un archivo de texto válido.")
st.session_state.semantic_file_content = None
else:
st.session_state.semantic_file_content = None
# [Resto del código igual...] ###############################################################################################################
def display_semantic_results(result, lang_code, semantic_t):
"""
Muestra los resultados del análisis semántico en tabs
"""
if result is None or not result['success']:
st.warning(semantic_t.get('no_results', 'No results available'))
return
analysis = result['analysis']
# Crear tabs para los resultados
tab1, tab2 = st.tabs([
semantic_t.get('concepts_tab', 'Key Concepts Analysis'),
semantic_t.get('entities_tab', 'Entities Analysis')
])
# Tab 1: Conceptos Clave
with tab1:
col1, col2 = st.columns(2)
# Columna 1: Lista de conceptos
with col1:
st.subheader(semantic_t.get('key_concepts', 'Key Concepts'))
concept_text = "\n".join([
f"• {concept} ({frequency:.2f})"
for concept, frequency in analysis['key_concepts']
])
st.markdown(concept_text)
# Columna 2: Gráfico de conceptos
with col2:
st.subheader(semantic_t.get('concept_graph', 'Concepts Graph'))
st.image(analysis['concept_graph'])
# Tab 2: Entidades
with tab2:
col1, col2 = st.columns(2)
# Columna 1: Lista de entidades
with col1:
st.subheader(semantic_t.get('identified_entities', 'Identified Entities'))
if 'entities' in analysis:
for entity_type, entities in analysis['entities'].items():
st.markdown(f"**{entity_type}**")
st.markdown("• " + "\n• ".join(entities))
# Columna 2: Gráfico de entidades
with col2:
st.subheader(semantic_t.get('entity_graph', 'Entities Graph'))
st.image(analysis['entity_graph'])
# Botón de exportación al final
col1, col2, col3 = st.columns([2,1,2])
with col2:
if st.button(
semantic_t.get('export_button', 'Export Analysis'),
key=f"semantic_export_{st.session_state.semantic_analysis_counter}",
use_container_width=True
):
pdf_buffer = export_user_interactions(st.session_state.username, 'semantic')
st.download_button(
label=semantic_t.get('download_pdf', 'Download PDF'),
data=pdf_buffer,
file_name="semantic_analysis.pdf",
mime="application/pdf",
key=f"semantic_download_{st.session_state.semantic_analysis_counter}"
) |