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import streamlit as st
import streamlit_float
import streamlit_option_menu
import streamlit_antd_components
import streamlit.components.v1 as components
import streamlit.components.v1 as stc
import logging
from .semantic_process import *
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
from .semantic_float68ok import *
logger = logging.getLogger(__name__)
semantic_float_init()
def get_translation(t, key, default):
return t.get(key, default)
def display_semantic_interface(lang_code, nlp_models, t):
# Inicialización del chatbot y el historial del chat
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 = []
# Inicializar el estado del grafo si no existe
if 'graph_visible' not in st.session_state:
st.session_state.graph_visible = False
if 'graph_content' not in st.session_state:
st.session_state.graph_content = ""
st.markdown("""
<style>
.chat-message {
margin-bottom: 10px;
padding: 5px;
border-radius: 5px;
}
.user-message {
background-color: #e6f3ff;
text-align: right;
}
.assistant-message {
background-color: #f0f0f0;
text-align: left;
}
.semantic-float {
position: fixed;
right: 20px;
top: 50%;
transform: translateY(-50%);
width: 800px;
height: 600px;
z-index: 1000;
background-color: white;
border: 1px solid #ddd;
border-radius: 5px;
padding: 10px;
overflow: hidden;
box-shadow: 0 0 10px rgba(0,0,0,0.1);
}
.semantic-float img {
width: 100%;
height: auto;
max-height: 440px;
object-fit: contain;
}
.chat-input {
position: fixed;
bottom: 20px;
left: 20px;
right: 20px;
z-index: 1000;
}
</style>
""", unsafe_allow_html=True)
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("---")
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("Semantic Analysis")
st.subheader("File Selection and 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"):
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:
with st.spinner("Analyzing..."):
try:
nlp_model = nlp_models[lang_code]
concept_graph_base64, entity_graph_base64, key_concepts = process_semantic_analysis(file_contents, nlp_model, lang_code)
st.session_state.current_file_contents = file_contents
st.success("Analysis completed successfully")
if concept_graph_base64:
graph_content = f"""
<h3>Key Concepts:</h3>
<p>{', '.join([f"{concept}: {freq:.2f}" for concept, freq in key_concepts])}</p>
<img src="data:image/png;base64,{concept_graph_base64}" alt="Concept Graph" style="width:100%; height:auto;"/>
"""
st.session_state.graph_id = float_graph(graph_content, width="800px", height="600px", position="center-right")
st.session_state.graph_visible = True
st.session_state.graph_content = graph_content
if entity_graph_base64:
entity_graph_content = f"""
<h3>Entity Graph:</h3>
<img src="data:image/png;base64,{entity_graph_base64}" alt="Entity Graph" style="width:100%; height:auto;"/>
"""
st.session_state.entity_graph_id = float_graph(entity_graph_content, width="800px", height="600px", position="bottom-left")
# Log para depuración
st.write(f"Debug: Graph ID: {st.session_state.get('graph_id')}")
st.write(f"Debug: Graph visible: {st.session_state.get('graph_visible')}")
st.write(f"Debug: Concept graph base64 (first 100 chars): {concept_graph_base64[:100]}")
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")
st.subheader("Chat with AI")
# Mostrar el historial del chat
for message in st.session_state.semantic_chat_history:
message_class = "user-message" if message["role"] == "user" else "assistant-message"
st.markdown(f'<div class="chat-message {message_class}">{message["content"]}</div>', unsafe_allow_html=True)
# Colocar la entrada de usuario y los botones en la parte inferior
st.markdown('<div class="chat-input">', unsafe_allow_html=True)
user_input = st.text_input("Type your message here...", key=generate_unique_key('semantic', 'chat_input'))
col1, col2, col3 = st.columns([3, 1, 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'))
with col3:
if 'graph_id' in st.session_state:
toggle_button = st.button("Toggle Graph", key="toggle_graph")
if toggle_button:
st.session_state.graph_visible = not st.session_state.get('graph_visible', True)
toggle_float_visibility(st.session_state.graph_id, st.session_state.graph_visible)
st.markdown('</div>', unsafe_allow_html=True)
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('current_file_contents', ''))
else:
response = st.session_state.semantic_chatbot.generate_response(user_input, lang_code, context=st.session_state.get('current_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()
# Asegurarse de que el grafo flotante permanezca visible después de las interacciones
if 'graph_id' in st.session_state and st.session_state.get('graph_visible', False):
toggle_float_visibility(st.session_state.graph_id, True)
# Mostrar el grafo flotante si está visible
if st.session_state.get('graph_visible', False) and 'graph_content' in st.session_state:
components.html(
f"""
<div id="{st.session_state.graph_id}" class="semantic-float" style="display:block;">
{st.session_state.graph_content}
</div>
""",
height=600,
scrolling=True
) |