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# modules/studentact/current_situation_interface.py
import streamlit as st
import logging
from ..utils.widget_utils import generate_unique_key
import matplotlib.pyplot as plt
import numpy as np
from ..database.current_situation_mongo_db import store_current_situation_result
from .current_situation_analysis import (
analyze_text_dimensions,
analyze_clarity,
analyze_reference_clarity,
analyze_vocabulary_diversity,
analyze_cohesion,
analyze_structure,
get_dependency_depths,
normalize_score,
generate_sentence_graphs,
generate_word_connections,
generate_connection_paths,
create_vocabulary_network,
create_syntax_complexity_graph,
create_cohesion_heatmap,
)
# Configuración del estilo de matplotlib para el gráfico de radar
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['axes.grid'] = True
plt.rcParams['axes.spines.top'] = False
plt.rcParams['axes.spines.right'] = False
logger = logging.getLogger(__name__)
####################################
def display_current_situation_interface(lang_code, nlp_models, t):
"""
Interfaz simplificada con gráfico de radar para visualizar métricas.
"""
try:
# Inicializar estados si no existen
if 'text_input' not in st.session_state:
st.session_state.text_input = ""
if 'show_results' not in st.session_state:
st.session_state.show_results = False
if 'current_doc' not in st.session_state:
st.session_state.current_doc = None
if 'current_metrics' not in st.session_state:
st.session_state.current_metrics = None
st.markdown("## Análisis Inicial de Escritura")
# Container principal con dos columnas
with st.container():
input_col, results_col = st.columns([1,2])
with input_col:
# Definir función para manejar cambios de texto
def on_text_change():
st.session_state.text_input = st.session_state.text_area
st.session_state.show_results = False
# Text area con manejo de estado
text_input = st.text_area(
t.get('input_prompt', "Escribe o pega tu texto aquí:"),
height=400,
key="text_area",
value=st.session_state.text_input,
on_change=on_text_change,
help="Este texto será analizado para darte recomendaciones personalizadas"
)
if st.button(
t.get('analyze_button', "Analizar mi escritura"),
type="primary",
disabled=not text_input.strip(),
use_container_width=True,
):
try:
with st.spinner(t.get('processing', "Analizando...")):
doc = nlp_models[lang_code](text_input)
metrics = analyze_text_dimensions(doc)
storage_success = store_current_situation_result(
username=st.session_state.username,
text=text_input,
metrics=metrics,
feedback=None
)
if not storage_success:
logger.warning("No se pudo guardar el análisis en la base de datos")
st.session_state.current_doc = doc
st.session_state.current_metrics = metrics
st.session_state.show_results = True
st.session_state.text_input = text_input
except Exception as e:
logger.error(f"Error en análisis: {str(e)}")
st.error(t.get('analysis_error', "Error al analizar el texto"))
# Mostrar resultados en la columna derecha
with results_col:
if st.session_state.show_results and st.session_state.current_metrics is not None:
display_results(st.session_state.current_metrics)
def display_results(metrics):
"""
Muestra los resultados del análisis: métricas y gráfico radar.
"""
try:
# Métricas en una fila con columnas uniformes
metric_cols = st.columns(4, gap="small", vertical_alignment="center", border=True)
metrics_config = [
("Vocabulario", metrics['vocabulary']['normalized_score'], "Riqueza y variedad del vocabulario"),
("Estructura", metrics['structure']['normalized_score'], "Organización y complejidad de oraciones"),
("Cohesión", metrics['cohesion']['normalized_score'], "Conexión y fluidez entre ideas"),
("Claridad", metrics['clarity']['normalized_score'], "Facilidad de comprensión del texto")
]
# Mostrar métricas
for i, (label, value, help_text) in enumerate(metrics_config):
metric_cols[i].metric(
label,
f"{value:.2f}",
"Meta: 1.00",
delta_color="off",
help=help_text
)
# Espacio entre métricas y gráfico
st.markdown("<div style='margin-top: 1rem;'></div>", unsafe_allow_html=True)
# Gráfico radar centrado
left_space, graph_col, right_space = st.columns([1, 2, 1])
with graph_col:
# Preparar datos para el gráfico
categories = [m[0] for m in metrics_config]
values_user = [m[1] for m in metrics_config]
values_pattern = [1.0] * len(categories)
# Crear y configurar gráfico
fig = plt.figure(figsize=(6, 6))
ax = fig.add_subplot(111, projection='polar')
# Configurar gráfico radar
angles = [n / float(len(categories)) * 2 * np.pi for n in range(len(categories))]
angles += angles[:1]
values_user += values_user[:1]
values_pattern += values_pattern[:1]
# Configurar ejes
ax.set_xticks(angles[:-1])
ax.set_xticklabels(categories, fontsize=8)
circle_ticks = np.arange(0, 1.1, 0.2)
ax.set_yticks(circle_ticks)
ax.set_yticklabels([f'{tick:.1f}' for tick in circle_ticks], fontsize=8)
ax.set_ylim(0, 1)
# Dibujar gráfico
ax.plot(angles, values_pattern, 'g--', linewidth=1, label='Patrón', alpha=0.5)
ax.fill(angles, values_pattern, 'g', alpha=0.1)
ax.plot(angles, values_user, 'b-', linewidth=2, label='Tu escritura')
ax.fill(angles, values_user, 'b', alpha=0.2)
ax.legend(loc='upper right', bbox_to_anchor=(0.1, 0.1), fontsize=8)
plt.tight_layout()
st.pyplot(fig)
plt.close()
except Exception as e:
logger.error(f"Error mostrando resultados: {str(e)}")
st.error("Error al mostrar los resultados") |