#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
    """
    # Mantener la página en semántico
    st.session_state.page = 'semantic'

    # Inicializar estados si no existen
    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
    if 'semantic_analysis_counter' not in st.session_state:
        st.session_state.semantic_analysis_counter = 0

    # Estilos CSS para alinear los botones
    st.markdown("""
        <style>
        .stButton > button {
            width: 100%;
            height: 38px;
        }
        .stUploadButton > button {
            width: 100%;
            height: 38px;
        }
        div.row-widget.stButton {
            margin-top: 1px;
            margin-bottom: 1px;
        }
        </style>
    """, unsafe_allow_html=True)

    try:
        # Contenedor principal con layout fijo
        with st.container():
            # Una sola fila para todos los controles
            col_file, col_analyze, col_export, col_new = st.columns([4, 2, 2, 2])

            # Columna 1: Carga de archivo
            with col_file:
                uploaded_file = st.file_uploader(
                    semantic_t.get('file_uploader', 'Upload TXT file'),
                    type=['txt'],
                    key=f"semantic_uploader_{st.session_state.semantic_analysis_counter}"
                )
                
                if uploaded_file is not None:
                    # Actualizar el contenido del archivo
                    file_content = uploaded_file.getvalue().decode('utf-8')
                    if file_content != st.session_state.semantic_file_content:
                        st.session_state.semantic_file_content = file_content
                        st.session_state.semantic_analysis_done = False

            # Columna 2: Botón de análisis
            with col_analyze:
                analyze_enabled = uploaded_file is not None and not st.session_state.semantic_analysis_done
                analyze_button = st.button(
                    semantic_t.get('analyze_button', 'Analyze Text'),
                    disabled=not analyze_enabled,
                    key=f"analyze_button_{st.session_state.semantic_analysis_counter}",
                    use_container_width=True
                )

            # Columna 3: Botón de exportación
            with col_export:
                export_button = st.button(
                    semantic_t.get('export_button', 'Export'),
                    disabled=not st.session_state.semantic_analysis_done,
                    key=f"export_button_{st.session_state.semantic_analysis_counter}",
                    use_container_width=True
                )

            # Columna 4: Botón de nuevo análisis
            with col_new:
                new_button = st.button(
                    semantic_t.get('new_analysis', 'New Analysis'),
                    disabled=not st.session_state.semantic_analysis_done,
                    key=f"new_button_{st.session_state.semantic_analysis_counter}",
                    use_container_width=True
                )

        st.markdown("<hr style='margin: 1em 0; opacity: 0.3'>", unsafe_allow_html=True)

        # Procesar análisis cuando se presiona el botón
        if analyze_button and st.session_state.semantic_file_content:
            with st.spinner(semantic_t.get('processing', 'Processing...')):
                try:
                    analysis_result = process_semantic_input(
                        st.session_state.semantic_file_content,
                        lang_code,
                        nlp_models,
                        semantic_t
                    )
                    
                    if analysis_result['success']:
                        # Guardar resultados y actualizar estado
                        st.session_state.semantic_result = analysis_result
                        st.session_state.semantic_analysis_done = True
                        
                        # Guardar en 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'))
                            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: {str(e)}")
                    st.error(semantic_t.get('error_processing', f'Error: {str(e)}'))

        # Manejar exportación
        if export_button and st.session_state.semantic_analysis_done:
            try:
                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"download_{st.session_state.semantic_analysis_counter}"
                )
            except Exception as e:
                st.error(f"Error exporting: {str(e)}")

        # Manejar nuevo análisis
        if new_button:
            st.session_state.semantic_file_content = None
            st.session_state.semantic_analysis_done = False
            st.session_state.semantic_result = None
            st.session_state.semantic_analysis_counter += 1
            st.rerun()

        # Mostrar resultados existentes 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 uploaded_file:
            st.info(semantic_t.get('initial_message', 'Upload a TXT file to begin analysis'))

    except Exception as e:
        logger.error(f"Error general: {str(e)}")
        st.error("Error in semantic interface. Please try again.")
        

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'])