import streamlit as st
import logging
from streamlit_chat import message
from .semantic_process import process_semantic_analysis
from ..chatbot.chatbot import initialize_chatbot, process_semantic_chat_input
from ..database.database_oldFromV2 import store_file_semantic_contents, retrieve_file_contents, delete_file, get_user_files
from ..utils.widget_utils import generate_unique_key
logger = logging.getLogger(__name__)
def get_translation(t, key, default):
return t.get(key, default)
def display_semantic_interface(lang_code, nlp_models, t):
# Inicializar el chatbot y el historial del chat al principio de la función
if 'semantic_chatbot' not in st.session_state:
st.session_state.semantic_chatbot = initialize_chatbot('semantic')
if 'messages' not in st.session_state:
st.session_state.messages = []
st.markdown("""
<style>
.semantic-initial-message {
background-color: #f0f2f6;
border-left: 5px solid #4CAF50;
padding: 10px;
border-radius: 5px;
font-size: 16px;
margin-bottom: 20px;
}
</style>
""", unsafe_allow_html=True)
# Mostrar el mensaje inicial como un párrafo estilizado
st.markdown(f"""
<div class="morpho-initial-message">
{t['semantic_initial_message']}
</div>
""", unsafe_allow_html=True)
st.title("Semantic Analysis")
# Crear dos columnas principales: una para el chat y otra para la visualización
chat_col, viz_col = st.columns([1, 1])
with chat_col:
st.subheader("Chat with AI")
# Contenedor para los mensajes del chat
chat_container = st.container()
# Input para el chat
user_input = st.text_input("Type your message here...", key=generate_unique_key('semantic', 'chat_input'))
if user_input:
# Añadir mensaje del usuario
st.session_state.messages.append({"role": "user", "content": user_input})
# Generar respuesta del asistente
if user_input.startswith('/analyze_current'):
response = process_semantic_chat_input(user_input, lang_code, nlp_models[lang_code], st.session_state.get('file_contents', ''))
else:
response = st.session_state.semantic_chatbot.generate_response(user_input, lang_code, context=st.session_state.get('file_contents', ''))
# Añadir respuesta del asistente
st.session_state.messages.append({"role": "assistant", "content": response})
# Mostrar mensajes en el contenedor del chat
with chat_container:
for i, msg in enumerate(st.session_state.messages):
message(msg['content'], is_user=msg['role'] == 'user', key=f"{i}_{msg['role']}")
# Botón para limpiar el chat
if st.button("Clear Chat", key=generate_unique_key('semantic', 'clear_chat')):
st.session_state.messages = []
st.rerun()
with viz_col:
st.subheader("Visualization")
# Selector de archivo y botón de análisis
user_files = get_user_files(st.session_state.username, 'semantic')
file_options = [get_translation(t, 'select_saved_file', 'Select a saved file')] + [file['file_name'] for file in user_files]
selected_file = st.selectbox("Select a file to analyze", options=file_options, key=generate_unique_key('semantic', 'file_selector'))
if st.button("Analyze Document", key=generate_unique_key('semantic', 'analyze_document')):
if selected_file and selected_file != get_translation(t, 'select_saved_file', 'Select a saved file'):
file_contents = retrieve_file_contents(st.session_state.username, selected_file, 'semantic')
if file_contents:
st.session_state.file_contents = file_contents
with st.spinner("Analyzing..."):
try:
nlp_model = nlp_models[lang_code]
concept_graph, entity_graph, key_concepts = process_semantic_analysis(file_contents, nlp_model, lang_code)
st.session_state.concept_graph = concept_graph
st.session_state.entity_graph = entity_graph
st.session_state.key_concepts = key_concepts
st.success("Analysis completed successfully")
except Exception as e:
logger.error(f"Error during analysis: {str(e)}")
st.error(f"Error during analysis: {str(e)}")
else:
st.error("Error loading file contents")
else:
st.error("Please select a file to analyze")
# Visualización de conceptos clave
if 'key_concepts' in st.session_state:
st.write("Key Concepts:")
st.write(', '.join([f"{concept}: {freq:.2f}" for concept, freq in st.session_state.key_concepts]))
# Pestañas para los gráficos
tab_concept, tab_entity = st.tabs(["Concept Graph", "Entity Graph"])
with tab_concept:
if 'concept_graph' in st.session_state:
st.pyplot(st.session_state.concept_graph)
else:
st.info("No concept graph available. Please analyze a document first.")
with tab_entity:
if 'entity_graph' in st.session_state:
st.pyplot(st.session_state.entity_graph)
else:
st.info("No entity graph available. Please analyze a document first.")
# Sección de carga de archivos
st.subheader("File Management")
uploaded_file = st.file_uploader("Choose a file to upload", type=['txt', 'pdf', 'docx', 'doc', 'odt'], key=generate_unique_key('semantic', 'file_uploader'))
if uploaded_file is not None:
file_contents = uploaded_file.getvalue().decode('utf-8')
if store_file_semantic_contents(st.session_state.username, uploaded_file.name, file_contents):
st.success(f"File {uploaded_file.name} uploaded and saved successfully")
else:
st.error("Error uploading file")
st.markdown("---")
# Gestión de archivos cargados
st.subheader("Manage Uploaded Files")
user_files = get_user_files(st.session_state.username, 'semantic')
if user_files:
for file in user_files:
col1, col2 = st.columns([3, 1])
with col1:
st.write(file['file_name'])
with col2:
if st.button("Delete", key=f"delete_{file['file_name']}", help=f"Delete {file['file_name']}"):
if delete_file(st.session_state.username, file['file_name'], 'semantic'):
st.success(f"File {file['file_name']} deleted successfully")
st.rerun()
else:
st.error(f"Error deleting file {file['file_name']}")
else:
st.info("No files uploaded yet.")