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import streamlit as st
from streamlit_float import *
import logging
import sys
import io
from io import BytesIO
from datetime import datetime
import re
import base64
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import pandas as pd
import numpy as np
from .flexible_analysis_handler import FlexibleAnalysisHandler
from .semantic_float_reset import semantic_float_init, float_graph, toggle_float_visibility, update_float_content
from .semantic_process import process_semantic_analysis
from ..chatbot.chatbot import initialize_chatbot, process_semantic_chat_input
from ..database.database_oldFromV2 import manage_file_contents, delete_file, get_user_files
from ..utils.widget_utils import generate_unique_key
semantic_float_init()
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)
def get_translation(t, key, default):
return t.get(key, default)
##
def fig_to_base64(fig):
buf = io.BytesIO()
fig.savefig(buf, format='png')
buf.seek(0)
img_str = base64.b64encode(buf.getvalue()).decode()
return f'<img src="data:image/png;base64,{img_str}" />'
##
def display_semantic_interface(lang_code, nlp_models, t):
#st.set_page_config(layout="wide")
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 = []
if 'show_graph' not in st.session_state:
st.session_state.show_graph = False
if 'graph_id' not in st.session_state:
st.session_state.graph_id = None
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 = []
if 'show_graph' not in st.session_state:
st.session_state.show_graph = False
st.markdown("""
<style>
.chat-message-container {
height: calc(100vh - 200px);
overflow-y: auto;
display: flex;
flex-direction: column-reverse;
}
.chat-input-container {
position: fixed;
bottom: 0;
left: 0;
right: 0;
padding: 1rem;
background-color: white;
z-index: 1000;
}
.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)
st.markdown(f"""
<div class="semantic-initial-message">
{t['semantic_initial_message']}
</div>
""", unsafe_allow_html=True)
col1, col2 = st.columns([2, 1])
with col1:
st.subheader("Chat with AI")
chat_container = st.container()
with chat_container:
st.markdown('<div class="chat-message-container">', unsafe_allow_html=True)
for message in reversed(st.session_state.semantic_chat_history):
with st.chat_message(message["role"]):
st.markdown(message["content"])
st.markdown('</div>', unsafe_allow_html=True)
st.markdown('<div class="chat-input-container">', unsafe_allow_html=True)
user_input = st.text_input("Type your message here...", key=generate_unique_key('semantic', 'chat_input'))
send_button = st.button("Send", key=generate_unique_key('semantic', 'send_message'))
clear_button = st.button("Clear Chat", key=generate_unique_key('semantic', 'clear_chat'))
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('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()
with col2:
st.subheader("Document 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("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 = manage_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]
logger.debug("Calling process_semantic_analysis")
analysis_result = process_semantic_analysis(file_contents, nlp_model, lang_code)
# Crear una instancia de FlexibleAnalysisHandler con los resultados del análisis
handler = FlexibleAnalysisHandler(analysis_result)
logger.debug(f"Type of analysis_result: {type(analysis_result)}")
logger.debug(f"Keys in analysis_result: {analysis_result.keys() if isinstance(analysis_result, dict) else 'Not a dict'}")
st.session_state.concept_graph = handler.get_concept_graph()
st.session_state.entity_graph = handler.get_entity_graph()
st.session_state.key_concepts = handler.get_key_concepts()
st.session_state.show_graph = True
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")
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 manage_file_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.")
#########################################################################################################################
# Floating graph visualization
if st.session_state.show_graph:
if st.session_state.graph_id is None:
st.session_state.graph_id = float_graph(
content="<div id='semantic-graph'>Loading graph...</div>",
width="40%",
height="60%",
position="bottom-right",
shadow=2,
transition=1
)
graph_id = st.session_state.graph_id
if 'key_concepts' in st.session_state:
key_concepts_html = "<h3>Key Concepts:</h3><p>" + ', '.join([f"{concept}: {freq:.2f}" for concept, freq in st.session_state.key_concepts]) + "</p>"
update_float_content(graph_id, key_concepts_html)
tab_concept, tab_entity = st.tabs(["Concept Graph", "Entity Graph"])
with tab_concept:
if 'concept_graph' in st.session_state:
update_float_content(graph_id, st.session_state.concept_graph)
else:
update_float_content(graph_id, "No concept graph available.")
with tab_entity:
if 'entity_graph' in st.session_state:
update_float_content(graph_id, st.session_state.entity_graph)
else:
update_float_content(graph_id, "No entity graph available.")
if st.button("Close Graph", key="close_graph"):
toggle_float_visibility(graph_id, False)
st.session_state.show_graph = False
st.session_state.graph_id = None
st.rerun() |