#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 io
from io import BytesIO
import base64
import matplotlib.pyplot as plt
import pandas as pd
import re

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.semantics_export import export_user_interactions

import logging
logger = logging.getLogger(__name__)

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
    """
    # Inicializar el estado de la entrada
    input_key = f"semantic_input_{lang_code}"
    if input_key not in st.session_state:
        st.session_state[input_key] = ""
        
    # Inicializar contador de análisis si no existe
    if 'semantic_analysis_counter' not in st.session_state:
        st.session_state.semantic_analysis_counter = 0

    # Campo de entrada de texto
    text_input = st.text_area(
        semantic_t.get('text_input_label', 'Enter text to analyze'),
        height=150,
        placeholder=semantic_t.get('text_input_placeholder', 'Enter your text here...'),
        value=st.session_state[input_key],
        key=f"text_area_{lang_code}_{st.session_state.semantic_analysis_counter}"
    )

    # Opción para cargar archivo
    uploaded_file = st.file_uploader(
        semantic_t.get('file_uploader', 'Or upload a text file'),
        type=['txt'],
        key=f"file_uploader_{lang_code}_{st.session_state.semantic_analysis_counter}"
    )

    if st.button(
        semantic_t.get('analyze_button', 'Analyze text'),
        key=f"analyze_button_{lang_code}_{st.session_state.semantic_analysis_counter}"
    ):
        if text_input or uploaded_file is not None:
            try:
                with st.spinner(semantic_t.get('processing', 'Processing...')):
                    # Obtener el texto a analizar
                    text_content = uploaded_file.getvalue().decode('utf-8') if uploaded_file else text_input
                    
                    # Realizar el análisis
                    analysis_result = process_semantic_input(
                        text_content, 
                        lang_code,
                        nlp_models,
                        semantic_t
                    )
                    
                    # Guardar resultado en el estado de la sesión
                    st.session_state.semantic_result = analysis_result
                    st.session_state.semantic_analysis_counter += 1
                    
                    # Mostrar resultados
                    display_semantic_results(
                        st.session_state.semantic_result,
                        lang_code,
                        semantic_t
                    )

            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)}'))
        else:
            st.warning(semantic_t.get('warning_message', 'Please enter text or upload a file'))
            
    # Si no se presionó el botón, verificar si hay 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', 'Enter text to begin analysis'))

def display_semantic_results(result, lang_code, semantic_t):
    """
    Muestra los resultados del análisis semántico
    Args:
        result: Resultados del análisis
        lang_code: Código del idioma
        semantic_t: Diccionario de traducciones
    """
    if result is None or not result['success']:
        st.warning(semantic_t.get('no_results', 'No results available'))
        return

    analysis = result['analysis']
    
    # Mostrar conceptos clave
    with st.expander(semantic_t.get('key_concepts', 'Key Concepts'), expanded=True):
        concept_text = " | ".join([
            f"{concept} ({frequency:.2f})" 
            for concept, frequency in analysis['key_concepts']
        ])
        st.write(concept_text)

    # Mostrar gráfico de relaciones conceptuales
    with st.expander(semantic_t.get('conceptual_relations', 'Conceptual Relations'), expanded=True):
        st.image(analysis['concept_graph'])

    # Mostrar gráfico de entidades
    with st.expander(semantic_t.get('entity_relations', 'Entity Relations'), expanded=True):
        st.image(analysis['entity_graph'])

    # Mostrar entidades identificadas
    if 'entities' in analysis:
        with st.expander(semantic_t.get('identified_entities', 'Identified Entities'), expanded=True):
            for entity_type, entities in analysis['entities'].items():
                st.subheader(entity_type)
                st.write(", ".join(entities))

    # Botón de exportación
    if st.button(semantic_t.get('export_button', 'Export Analysis')):
        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"
        )