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import streamlit as st
from streamlit_float import *
from streamlit_antd_components import *
from streamlit.components.v1 import html
import spacy
from spacy import displacy
import spacy_streamlit
import pandas as pd
import base64
import re

from .morphosyntax_process import (
    process_morphosyntactic_input,
    format_analysis_results,
    perform_advanced_morphosyntactic_analysis,
    get_repeated_words_colors,
    highlight_repeated_words,
    POS_COLORS,
    POS_TRANSLATIONS
)

from ..utils.widget_utils import generate_unique_key
from ..database.morphosintax_mongo_db import store_student_morphosyntax_result
from ..database.chat_mongo_db import store_chat_history, get_chat_history

import logging
logger = logging.getLogger(__name__)

def display_morphosyntax_interface(lang_code, nlp_models, morpho_t):
    try:
        # CSS para mejorar la estabilidad y prevenir saltos
        st.markdown("""
            <style>
            .stTextArea textarea {
                font-size: 1rem;
                line-height: 1.5;
                padding: 0.5rem;
                border-radius: 0.375rem;
                border: 1px solid #e2e8f0;
                background-color: white;
            }
            .stTextArea textarea:focus {
                border-color: #3182ce;
                box-shadow: 0 0 0 3px rgba(66, 153, 225, 0.5);
            }
            .block-container {
                padding-top: 1rem;
                padding-bottom: 1rem;
            }
            .arc-diagram-container {
                background-color: white;
                padding: 1rem;
                border-radius: 0.5rem;
                box-shadow: 0 1px 3px rgba(0,0,0,0.1);
                margin-top: 1rem;
            }
            </style>
        """, unsafe_allow_html=True)

        # Inicializar el estado
        if 'morphosyntax_state' not in st.session_state:
            st.session_state.morphosyntax_state = {
                'input_text': "",
                'analysis_count': 0,
                'last_analysis': None,
            }

        # Contenedor principal con manejo de estado mejorado
        with st.container():
            # Campo de entrada de texto con manejo de estado explícito
            input_key = f"morpho_input_{st.session_state.morphosyntax_state['analysis_count']}"
            
            # Función para manejar cambios en el texto
            def on_text_change():
                text = st.session_state[input_key]
                st.session_state.morphosyntax_state['input_text'] = text
                
            # Campo de texto con callback
            sentence_input = st.text_area(
                morpho_t.get('morpho_input_label', 'Enter text to analyze'),
                height=150,
                key=input_key,
                placeholder=morpho_t.get('morpho_input_placeholder', 'Enter your text here...'),
                on_change=on_text_change
            )

            # Botón de análisis
            col1, col2, col3 = st.columns([2,1,2])
            with col1:
                analyze_button = st.button(
                    morpho_t.get('morpho_analyze_button', 'Analyze Morphosyntax'),
                    key=f"morpho_button_{st.session_state.morphosyntax_state['analysis_count']}",
                    type="primary",
                    icon="🔍",
                    disabled=not bool(st.session_state.morphosyntax_state['input_text'].strip()),
                    use_container_width=True
                )

            # Procesar análisis
            if analyze_button and st.session_state.morphosyntax_state['input_text'].strip():
                try:
                    with st.spinner(morpho_t.get('processing', 'Processing...')):
                        # Procesar el texto
                        doc = nlp_models[lang_code](st.session_state.morphosyntax_state['input_text'])
                        
                        # Realizar análisis
                        advanced_analysis = perform_advanced_morphosyntactic_analysis(
                            st.session_state.morphosyntax_state['input_text'],
                            nlp_models[lang_code]
                        )

                        # Guardar resultado en el estado
                        st.session_state.morphosyntax_result = {
                            'doc': doc,
                            'advanced_analysis': advanced_analysis
                        }

                        # Incrementar contador
                        st.session_state.morphosyntax_state['analysis_count'] += 1

                        # Guardar en base de datos
                        if store_student_morphosyntax_result(
                            username=st.session_state.username,
                            text=st.session_state.morphosyntax_state['input_text'],
                            arc_diagrams=advanced_analysis['arc_diagrams']
                        ):
                            st.success(morpho_t.get('success_message', 'Analysis saved successfully'))
                            display_morphosyntax_results(
                                st.session_state.morphosyntax_result,
                                lang_code,
                                morpho_t
                            )
                        else:
                            st.error(morpho_t.get('error_message', 'Error saving analysis'))

                except Exception as e:
                    logger.error(f"Error en análisis morfosintáctico: {str(e)}")
                    st.error(morpho_t.get('error_processing', f'Error processing text: {str(e)}'))

            # Mostrar resultados previos
            elif 'morphosyntax_result' in st.session_state and st.session_state.morphosyntax_result:
                display_morphosyntax_results(
                    st.session_state.morphosyntax_result,
                    lang_code,
                    morpho_t
                )

    except Exception as e:
        logger.error(f"Error general en display_morphosyntax_interface: {str(e)}")
        st.error("Se produjo un error. Por favor, intente de nuevo.")

def display_morphosyntax_results(result, lang_code, morpho_t):
    """
    Muestra solo el análisis sintáctico con diagramas de arco.
    """
    if result is None:
        st.warning(morpho_t.get('no_results', 'No results available'))
        return

    doc = result['doc']

    # Análisis sintáctico (diagramas de arco)
    st.markdown(f"### {morpho_t.get('arc_diagram', 'Syntactic analysis: Arc diagram')}")
    
    with st.container():
        sentences = list(doc.sents)
        for i, sent in enumerate(sentences):
            with st.container():
                st.subheader(f"{morpho_t.get('sentence', 'Sentence')} {i+1}")
                try:
                    html = displacy.render(sent, style="dep", options={
                        "distance": 100,
                        "arrow_spacing": 20,
                        "word_spacing": 30
                    })
                    # Ajustar dimensiones del SVG
                    html = html.replace('height="375"', 'height="200"')
                    html = re.sub(r'<svg[^>]*>', lambda m: m.group(0).replace('height="450"', 'height="300"'), html)
                    html = re.sub(r'<g [^>]*transform="translate\((\d+),(\d+)\)"', 
                                lambda m: f'<g transform="translate({m.group(1)},50)"', html)
                    
                    # Envolver en un div con clase para estilos
                    html = f'<div class="arc-diagram-container">{html}</div>'
                    st.write(html, unsafe_allow_html=True)
                except Exception as e:
                    logger.error(f"Error rendering sentence {i}: {str(e)}")
                    st.error(f"Error displaying diagram for sentence {i+1}")