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import streamlit as st
from .semantic_process import process_semantic_analysis
from ..chatbot.chatbot import initialize_chatbot
from ..database.database_oldFromV2 import store_semantic_result
from ..text_analysis.semantic_analysis import perform_semantic_analysis
from ..utils.widget_utils import generate_unique_key
def display_semantic_interface(lang_code, nlp_models, t):
st.subheader(t['title'])
# Inicializar el chatbot si no existe
if 'semantic_chatbot' not in st.session_state:
st.session_state.semantic_chatbot = initialize_chatbot('semantic')
# Sección para cargar archivo
uploaded_file = st.file_uploader(t['file_uploader'], type=['txt', 'pdf', 'docx', 'doc', 'odt'])
if uploaded_file:
file_contents = uploaded_file.getvalue().decode('utf-8')
st.session_state.file_contents = file_contents
# Mostrar el historial del chat
chat_history = st.session_state.get('semantic_chat_history', [])
for message in chat_history:
with st.chat_message(message["role"]):
st.write(message["content"])
if "visualization" in message:
st.pyplot(message["visualization"])
# Input del usuario
user_input = st.chat_input(t['semantic_initial_message'], key=generate_unique_key('semantic', st.session_state.username))
if user_input:
# Procesar el input del usuario
response, visualization = process_semantic_analysis(user_input, lang_code, nlp_models[lang_code], st.session_state.get('file_contents'), t)
# Actualizar el historial del chat
chat_history.append({"role": "user", "content": user_input})
chat_history.append({"role": "assistant", "content": response, "visualization": visualization})
st.session_state.semantic_chat_history = chat_history
# Mostrar el resultado más reciente
with st.chat_message("assistant"):
st.write(response)
if visualization:
st.pyplot(visualization)
# Guardar el resultado en la base de datos si es un análisis
if user_input.startswith('/analisis_semantico'):
result = perform_semantic_analysis(st.session_state.file_contents, nlp_models[lang_code], lang_code)
store_semantic_result(st.session_state.username, st.session_state.file_contents, result)
# Botón para limpiar el historial del chat
if st.button(t['clear_chat'], key=generate_unique_key('semantic', 'clear_chat')):
st.session_state.semantic_chat_history = []
st.rerun() |