| import streamlit as st
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| from streamlit_float import *
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| import logging
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| import sys
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| import io
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| from io import BytesIO
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| from datetime import datetime
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| import re
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| import base64
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| import matplotlib.pyplot as plt
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| import plotly.graph_objects as go
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| import pandas as pd
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| import numpy as np
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|
|
| from .flexible_analysis_handler import FlexibleAnalysisHandler
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|
|
| from .semantic_float_reset import semantic_float_init, float_graph, toggle_float_visibility, update_float_content
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|
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| from .semantic_process import process_semantic_analysis
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|
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| from ..chatbot.chatbot import initialize_chatbot, process_semantic_chat_input
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| from ..database.database_oldFromV2 import manage_file_contents, delete_file, get_user_files
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| from ..utils.widget_utils import generate_unique_key
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|
|
|
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| semantic_float_init()
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| logging.basicConfig(level=logging.DEBUG)
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| logger = logging.getLogger(__name__)
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|
|
| def get_translation(t, key, default):
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| return t.get(key, default)
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|
|
|
|
|
|
| def fig_to_base64(fig):
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| buf = io.BytesIO()
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| fig.savefig(buf, format='png')
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| buf.seek(0)
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| img_str = base64.b64encode(buf.getvalue()).decode()
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| return f'<img src="data:image/png;base64,{img_str}" />'
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|
|
|
|
|
|
| def display_semantic_interface(lang_code, nlp_models, t):
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|
|
|
|
| if 'semantic_chatbot' not in st.session_state:
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| st.session_state.semantic_chatbot = initialize_chatbot('semantic')
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|
|
| if 'semantic_chat_history' not in st.session_state:
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| st.session_state.semantic_chat_history = []
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|
|
| if 'show_graph' not in st.session_state:
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| st.session_state.show_graph = False
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|
|
| if 'graph_id' not in st.session_state:
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| st.session_state.graph_id = None
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|
|
| if 'semantic_chatbot' not in st.session_state:
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| st.session_state.semantic_chatbot = initialize_chatbot('semantic')
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|
|
| if 'semantic_chat_history' not in st.session_state:
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| st.session_state.semantic_chat_history = []
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|
|
| if 'show_graph' not in st.session_state:
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| st.session_state.show_graph = False
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|
|
| st.markdown("""
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| <style>
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| .chat-message-container {
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| height: calc(100vh - 200px);
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| overflow-y: auto;
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| display: flex;
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| flex-direction: column-reverse;
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| }
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| .chat-input-container {
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| position: fixed;
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| bottom: 0;
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| left: 0;
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| right: 0;
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| padding: 1rem;
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| background-color: white;
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| z-index: 1000;
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| }
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| .semantic-initial-message {
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| background-color: #f0f2f6;
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| border-left: 5px solid #4CAF50;
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| padding: 10px;
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| border-radius: 5px;
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| font-size: 16px;
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| margin-bottom: 20px;
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| }
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| </style>
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| """, unsafe_allow_html=True)
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|
|
| st.markdown(f"""
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| <div class="semantic-initial-message">
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| {t['semantic_initial_message']}
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| </div>
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| """, unsafe_allow_html=True)
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|
|
| col1, col2 = st.columns([2, 1])
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|
|
| with col1:
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| st.subheader("Chat with AI")
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|
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| chat_container = st.container()
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| with chat_container:
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| st.markdown('<div class="chat-message-container">', unsafe_allow_html=True)
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| for message in reversed(st.session_state.semantic_chat_history):
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| with st.chat_message(message["role"]):
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| st.markdown(message["content"])
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| st.markdown('</div>', unsafe_allow_html=True)
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|
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| st.markdown('<div class="chat-input-container">', unsafe_allow_html=True)
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| user_input = st.text_input("Type your message here...", key=generate_unique_key('semantic', 'chat_input'))
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| send_button = st.button("Send", key=generate_unique_key('semantic', 'send_message'))
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| clear_button = st.button("Clear Chat", key=generate_unique_key('semantic', 'clear_chat'))
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| st.markdown('</div>', unsafe_allow_html=True)
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|
|
| if send_button and user_input:
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| st.session_state.semantic_chat_history.append({"role": "user", "content": user_input})
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|
|
| if user_input.startswith('/analyze_current'):
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| response = process_semantic_chat_input(user_input, lang_code, nlp_models[lang_code], st.session_state.get('file_contents', ''))
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| else:
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| response = st.session_state.semantic_chatbot.generate_response(user_input, lang_code, context=st.session_state.get('file_contents', ''))
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|
|
| st.session_state.semantic_chat_history.append({"role": "assistant", "content": response})
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| st.rerun()
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|
|
| if clear_button:
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| st.session_state.semantic_chat_history = []
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| st.rerun()
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|
|
| with col2:
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| st.subheader("Document Analysis")
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| user_files = get_user_files(st.session_state.username, 'semantic')
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| file_options = [get_translation(t, 'select_saved_file', 'Select a saved file')] + [file['file_name'] for file in user_files]
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| selected_file = st.selectbox("Select a file to analyze", options=file_options, key=generate_unique_key('semantic', 'file_selector'))
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|
|
| if st.button("Analyze Document", key=generate_unique_key('semantic', 'analyze_document')):
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| if selected_file and selected_file != get_translation(t, 'select_saved_file', 'Select a saved file'):
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| file_contents = manage_file_contents(st.session_state.username, selected_file, 'semantic')
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| if file_contents:
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| st.session_state.file_contents = file_contents
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| with st.spinner("Analyzing..."):
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| try:
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| nlp_model = nlp_models[lang_code]
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| logger.debug("Calling process_semantic_analysis")
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| analysis_result = process_semantic_analysis(file_contents, nlp_model, lang_code)
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|
|
|
|
| handler = FlexibleAnalysisHandler(analysis_result)
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|
|
| logger.debug(f"Type of analysis_result: {type(analysis_result)}")
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| logger.debug(f"Keys in analysis_result: {analysis_result.keys() if isinstance(analysis_result, dict) else 'Not a dict'}")
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|
|
| st.session_state.concept_graph = handler.get_concept_graph()
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| st.session_state.entity_graph = handler.get_entity_graph()
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| st.session_state.key_concepts = handler.get_key_concepts()
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| st.session_state.show_graph = True
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| st.success("Analysis completed successfully")
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| except Exception as e:
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| logger.error(f"Error during analysis: {str(e)}")
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| st.error(f"Error during analysis: {str(e)}")
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| else:
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| st.error("Error loading file contents")
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| else:
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| st.error("Please select a file to analyze")
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|
|
| st.subheader("File Management")
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|
|
| uploaded_file = st.file_uploader("Choose a file to upload", type=['txt', 'pdf', 'docx', 'doc', 'odt'], key=generate_unique_key('semantic', 'file_uploader'))
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| if uploaded_file is not None:
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| file_contents = uploaded_file.getvalue().decode('utf-8')
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| if manage_file_contents(st.session_state.username, uploaded_file.name, file_contents):
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| st.success(f"File {uploaded_file.name} uploaded and saved successfully")
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| else:
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| st.error("Error uploading file")
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|
|
| st.markdown("---")
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|
|
| st.subheader("Manage Uploaded Files")
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|
|
| user_files = get_user_files(st.session_state.username, 'semantic')
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| if user_files:
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| for file in user_files:
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| col1, col2 = st.columns([3, 1])
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| with col1:
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| st.write(file['file_name'])
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| with col2:
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| 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.")
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|
|
|
|
|
|
| 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>"
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| update_float_content(graph_id, key_concepts_html)
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|
|
| tab_concept, tab_entity = st.tabs(["Concept Graph", "Entity Graph"])
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|
|
| with tab_concept:
|
| if 'concept_graph' in st.session_state:
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| update_float_content(graph_id, st.session_state.concept_graph)
|
| else:
|
| update_float_content(graph_id, "No concept graph available.")
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|
|
| with tab_entity:
|
| if 'entity_graph' in st.session_state:
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| update_float_content(graph_id, st.session_state.entity_graph)
|
| else:
|
| update_float_content(graph_id, "No entity graph available.")
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|
|
| 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() |
