modules/studentact/current_situation_interface.py

# 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")