#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
    Args:
        lang_code: Código del idioma actual
        nlp_models: Modelos de spaCy cargados
        semantic_t: Diccionario de traducciones semánticas
    """
    try:
        # Inicializar el estado si no existe
        if 'semantic_analysis_counter' not in st.session_state:
            st.session_state.semantic_analysis_counter = 0

        # Opción para cargar archivo con key única
        uploaded_file = st.file_uploader(
            semantic_t.get('file_uploader', 'Upload a text file for analysis'),
            type=['txt'],
            key=f"semantic_file_uploader_{st.session_state.semantic_analysis_counter}"
        )

        # Botón de análisis con key única
        col1, col2, col3 = st.columns([2,1,2])
        with col2:
            analyze_button = st.button(
                semantic_t.get('analyze_button', 'Analyze text'),
                key=f"semantic_analyze_button_{st.session_state.semantic_analysis_counter}",
                use_container_width=True
            )

        if analyze_button and uploaded_file is not None:
            try:
                with st.spinner(semantic_t.get('processing', 'Processing...')):
                    text_content = uploaded_file.getvalue().decode('utf-8')
                    
                    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_analysis_counter += 1
                        
                        # Guardar en la base de datos
                        if store_student_semantic_result(
                            st.session_state.username,
                            text_content,
                            analysis_result['analysis']
                        ):
                            st.success(semantic_t.get('success_message', 'Analysis saved successfully'))
                            # Mostrar resultados
                            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 semántico: {str(e)}")
                st.error(semantic_t.get('error_processing', f'Error processing text: {str(e)}'))
        elif analyze_button:
            st.warning(semantic_t.get('warning_message', 'Please upload a file first'))
        
        # Mostrar resultados previos
        elif 'semantic_result' in st.session_state and st.session_state.semantic_result is not None:
            display_semantic_results(
                st.session_state.semantic_result,
                lang_code,
                semantic_t
            )
        else:
            st.info(semantic_t.get('initial_message', 'Upload a file to begin analysis'))

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

def display_semantic_results(result, lang_code, semantic_t):
    """
    Muestra los resultados del análisis semántico en tabs
    """
    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'])

    # Botón de exportación al final
    col1, col2, col3 = st.columns([2,1,2])
    with col2:
        if st.button(
            semantic_t.get('export_button', 'Export Analysis'), 
            key=f"semantic_export_{st.session_state.semantic_analysis_counter}",
            use_container_width=True
        ):
            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"semantic_download_{st.session_state.semantic_analysis_counter}"
            )