import streamlit as st
import logging
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):
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;
}
.stButton > button {
width: 100%;
height: 3em;
}
.chat-container {
height: 400px;
overflow-y: auto;
border: 1px solid #ddd;
padding: 10px;
border-radius: 5px;
}
.file-management-container, .analysis-container {
border: 1px solid #ddd;
padding: 10px;
border-radius: 5px;
margin-bottom: 20px;
}
.horizontal-list {
display: flex;
flex-wrap: wrap;
gap: 10px;
}
.graph-container {
height: 500px;
overflow-y: auto;
}
</style>
""", unsafe_allow_html=True)
st.markdown(f"""
<div class="semantic-initial-message">
{get_translation(t, 'semantic_initial_message', 'Welcome to the semantic analysis interface.')}
</div>
""", unsafe_allow_html=True)
if 'semantic_chatbot' not in st.session_state:
st.session_state.semantic_chatbot = initialize_chatbot('semantic')
# Contenedor para la gestión de archivos
with st.container():
st.markdown('<div class="file-management-container">', unsafe_allow_html=True)
col1, col2, col3, col4 = st.columns(4)
with col1:
uploaded_file = st.file_uploader(get_translation(t, 'upload_file', 'Upload File'), 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.session_state.file_contents = file_contents
st.success(get_translation(t, 'file_uploaded_success', 'File uploaded and saved successfully'))
st.rerun()
else:
st.error(get_translation(t, 'file_upload_error', 'Error uploading file'))
with col2:
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("", options=file_options, key=generate_unique_key('semantic', 'file_selector'))
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
st.success(get_translation(t, 'file_loaded_success', 'File loaded successfully'))
else:
st.error(get_translation(t, 'file_load_error', 'Error loading file'))
with col3:
if st.button(get_translation(t, 'analyze_document', 'Analyze Document'), key=generate_unique_key('semantic', 'analyze_document')):
if 'file_contents' in st.session_state:
with st.spinner(get_translation(t, 'analyzing', 'Analyzing...')):
try:
nlp_model = nlp_models[lang_code]
concept_graph, entity_graph, key_concepts = process_semantic_analysis(st.session_state.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(get_translation(t, 'analysis_completed', 'Analysis completed'))
except Exception as e:
logger.error(f"Error during analysis: {str(e)}")
st.error(f"Error during analysis: {str(e)}")
else:
st.error(get_translation(t, 'no_file_uploaded', 'No file uploaded'))
with col4:
if st.button(get_translation(t, 'delete_file', 'Delete File'), key=generate_unique_key('semantic', 'delete_file')):
if selected_file and selected_file != get_translation(t, 'select_saved_file', 'Select a saved file'):
if delete_file(st.session_state.username, selected_file, 'semantic'):
st.success(get_translation(t, 'file_deleted_success', 'File deleted successfully'))
if 'file_contents' in st.session_state:
del st.session_state.file_contents
st.rerun()
else:
st.error(get_translation(t, 'file_delete_error', 'Error deleting file'))
else:
st.error(get_translation(t, 'no_file_selected', 'No file selected'))
st.markdown('</div>', unsafe_allow_html=True)
# Contenedor para la sección de análisis
st.markdown('<div class="analysis-container">', unsafe_allow_html=True)
col_chat, col_graph = st.columns([1, 1])
with col_chat:
st.subheader(get_translation(t, 'chat_title', 'Semantic Analysis Chat'))
chat_container = st.container()
with chat_container:
chat_history = st.session_state.get('semantic_chat_history', [])
for message in chat_history:
with st.chat_message(message["role"]):
st.write(message["content"])
user_input = st.chat_input(get_translation(t, 'semantic_chat_input', 'Type your message here...'), key=generate_unique_key('semantic', 'chat_input'))
if user_input:
chat_history.append({"role": "user", "content": user_input})
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)
chat_history.append({"role": "assistant", "content": response})
st.session_state.semantic_chat_history = chat_history
with col_graph:
st.subheader(get_translation(t, 'graph_title', 'Semantic Graphs'))
# Mostrar conceptos clave y entidades horizontalmente
if 'key_concepts' in st.session_state:
st.write(get_translation(t, 'key_concepts_title', 'Key Concepts'))
st.markdown('<div class="horizontal-list">', unsafe_allow_html=True)
for concept, freq in st.session_state.key_concepts:
st.markdown(f'<span style="margin-right: 10px;">{concept}: {freq:.2f}</span>', unsafe_allow_html=True)
st.markdown('</div>', unsafe_allow_html=True)
if 'entities' in st.session_state:
st.write(get_translation(t, 'entities_title', 'Entities'))
st.markdown('<div class="horizontal-list">', unsafe_allow_html=True)
for entity, type in st.session_state.entities.items():
st.markdown(f'<span style="margin-right: 10px;">{entity}: {type}</span>', unsafe_allow_html=True)
st.markdown('</div>', unsafe_allow_html=True)
# Usar pestañas para mostrar los gráficos
tab1, tab2 = st.tabs(["Concept Graph", "Entity Graph"])
with tab1:
if 'concept_graph' in st.session_state:
st.pyplot(st.session_state.concept_graph)
with tab2:
if 'entity_graph' in st.session_state:
st.pyplot(st.session_state.entity_graph)
st.markdown('</div>', unsafe_allow_html=True)
if st.button(get_translation(t, 'clear_chat', 'Clear chat'), key=generate_unique_key('semantic', 'clear_chat')):
st.session_state.semantic_chat_history = []
st.rerun()