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#modules/semantic/semantic_interface.py
import streamlit as st
from streamlit_float import *
from streamlit_antd_components import *
from streamlit.components.v1 import html
import spacy_streamlit
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
def display_semantic_interface(lang_code, nlp_models, semantic_t):
"""
Interfaz para el análisis semántico con controles alineados horizontalmente
"""
# Forzar la página a semántico
st.session_state.page = 'semantic'
# Inicializar estados básicos
if 'semantic_content' not in st.session_state:
st.session_state.semantic_content = None
if 'semantic_analyzed' not in st.session_state:
st.session_state.semantic_analyzed = False
# Contenedor principal
with st.container():
# Una sola fila para todos los controles
cols = st.columns([4, 2, 2, 2])
# Columna 1: Carga de archivo
with cols[0]:
uploaded_file = st.file_uploader(
"Upload text file", # Simplificamos el mensaje
type=['txt'],
key="semantic_file_upload"
)
# Columna 2: Botón de análisis
with cols[1]:
can_analyze = uploaded_file is not None and not st.session_state.semantic_analyzed
if st.button('Analyze',
disabled=not can_analyze,
key="semantic_analyze"):
if uploaded_file:
text_content = uploaded_file.getvalue().decode('utf-8')
# Realizar el análisis
with st.spinner("Analyzing..."):
analysis_result = process_semantic_input(
text_content,
lang_code,
nlp_models,
semantic_t
)
if analysis_result['success']:
st.session_state.semantic_result = analysis_result
st.session_state.semantic_analyzed = True
st.success("Analysis completed!")
# Mostrar resultados
display_semantic_results(
analysis_result,
lang_code,
semantic_t
)
# Columna 3: Botón de exportación
with cols[2]:
if st.button('Export',
disabled=not st.session_state.semantic_analyzed,
key="semantic_export"):
if st.session_state.semantic_analyzed:
try:
pdf_buffer = export_user_interactions(
st.session_state.username,
'semantic'
)
st.download_button(
"Download PDF",
data=pdf_buffer,
file_name="semantic_analysis.pdf",
mime="application/pdf"
)
except Exception as e:
st.error(f"Error exporting: {str(e)}")
# Columna 4: Botón de nuevo análisis
with cols[3]:
if st.button('New Analysis',
disabled=not st.session_state.semantic_analyzed,
key="semantic_new"):
st.session_state.semantic_content = None
st.session_state.semantic_analyzed = False
st.session_state.semantic_result = None
st.rerun()
# Mostrar resultados si existen
if st.session_state.semantic_analyzed and 'semantic_result' in st.session_state:
display_semantic_results(
st.session_state.semantic_result,
lang_code,
semantic_t
)
elif not uploaded_file:
st.info("Please upload a text file to begin analysis")
def display_semantic_results(result, lang_code, semantic_t):
"""
Muestra los resultados del análisis semántico
"""
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']) |