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import streamlit as st
import logging
import time
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 'semantic_chat_history' not in st.session_state:
st.session_state.semantic_chat_history = []
st.markdown("""
<style>
.stTabs [data-baseweb="tab-list"] {
gap: 24px;
}
.stTabs [data-baseweb="tab"] {
height: 50px;
white-space: pre-wrap;
background-color: #F0F2F6;
border-radius: 4px 4px 0px 0px;
gap: 1px;
padding-top: 10px;
padding-bottom: 10px;
}
.stTabs [aria-selected="true"] {
background-color: #FFFFFF;
}
.file-list {
border: 1px solid #ddd;
border-radius: 5px;
padding: 10px;
margin-top: 20px;
}
.file-item {
display: flex;
justify-content: space-between;
align-items: center;
padding: 5px 0;
border-bottom: 1px solid #eee;
}
.file-item:last-child {
border-bottom: none;
}
.chat-message-container {
height: 400px;
overflow-y: auto;
border: 1px solid #ddd;
border-radius: 5px;
padding: 10px;
margin-bottom: 10px;
}
.stButton {
margin-top: 0 !important;
}
.graph-container {
border: 1px solid #ddd;
border-radius: 5px;
padding: 10px;
}
.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="semantic-initial-message">
{t['semantic_initial_message']}
</div>
""", unsafe_allow_html=True)
tab1, tab2 = st.tabs(["Upload", "Analyze"])
with tab1:
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("---") # Línea separadora
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.")
with tab2:
st.subheader("Select File for Analysis")
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 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")
# Chat and Visualization --1
with st.container():
col_chat, col_graph = st.columns([1, 1])
with col_chat:
st.subheader("Chat with AI")
# Create a container for the chat messages
chat_container = st.container()
# Display chat messages from history on app rerun
with chat_container:
for message in st.session_state.semantic_chat_history:
with st.chat_message(message["role"]):
st.markdown(message["content"])
user_input = st.text_input("Type your message here...", key=generate_unique_key('semantic', 'chat_input'))
col1, col2 = st.columns([3, 1])
with col1:
send_button = st.button("Send", key=generate_unique_key('semantic', 'send_message'))
with col2:
clear_button = st.button("Clear Chat", key=generate_unique_key('semantic', 'clear_chat'))
if send_button and user_input:
st.session_state.semantic_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, context=st.session_state.get('file_contents', ''))
st.session_state.semantic_chat_history.append({"role": "assistant", "content": response})
st.rerun()
if clear_button:
st.session_state.semantic_chat_history = []
st.rerun()
'''
# Accept user input
if prompt := st.chat_input("Type your message here...", key=generate_unique_key('semantic', 'chat_input')):
# Add user message to chat history
st.session_state.semantic_chat_history.append({"role": "user", "content": prompt})
# Display user message in chat message container
with st.chat_message("user"):
st.markdown(prompt)
# Generate and display assistant response
with st.chat_message("assistant"):
message_placeholder = st.empty()
full_response = ""
if prompt.startswith('/analyze_current'):
assistant_response = process_semantic_chat_input(prompt, lang_code, nlp_models[lang_code], st.session_state.get('file_contents', ''))
else:
assistant_response = st.session_state.semantic_chatbot.generate_response(prompt, lang_code, context=st.session_state.get('file_contents', ''))
# Simulate stream of response with milliseconds delay
for chunk in assistant_response.split():
full_response += chunk + " "
time.sleep(0.05)
# Add a blinking cursor to simulate typing
message_placeholder.markdown(full_response + "▌")
message_placeholder.markdown(full_response)
# Add assistant response to chat history
st.session_state.semantic_chat_history.append({"role": "assistant", "content": full_response})
# Add a clear chat button
if st.button("Clear Chat", key=generate_unique_key('semantic', 'clear_chat')):
st.session_state.semantic_chat_history = [{"role": "assistant", "content": "Chat cleared. How can I assist you?"}]
st.rerun()
'''
'''
with col_graph:
st.subheader("Visualization")
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]))
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.")
''' |