| 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):
|
|
|
| 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>
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| .stTabs [data-baseweb="tab-list"] {
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| gap: 24px;
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| }
|
| .stTabs [data-baseweb="tab"] {
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| height: 50px;
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| white-space: pre-wrap;
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| background-color: #F0F2F6;
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| border-radius: 4px 4px 0px 0px;
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| gap: 1px;
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| padding-top: 10px;
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| padding-bottom: 10px;
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| }
|
| .stTabs [aria-selected="true"] {
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| background-color: #FFFFFF;
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| }
|
| .file-list {
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| border: 1px solid #ddd;
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| border-radius: 5px;
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| padding: 10px;
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| margin-top: 20px;
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| }
|
| .file-item {
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| display: flex;
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| justify-content: space-between;
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| align-items: center;
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| padding: 5px 0;
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| border-bottom: 1px solid #eee;
|
| }
|
| .file-item:last-child {
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| border-bottom: none;
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| }
|
| .chat-message-container {
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| margin-bottom: 10px;
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| max-height: 400px;
|
| overflow-y: auto;
|
| }
|
| .stButton {
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| margin-top: 0 !important;
|
| }
|
| .graph-container {
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| border: 1px solid #ddd;
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| border-radius: 5px;
|
| padding: 10px;
|
| }
|
| .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;
|
| font-size: 16px;
|
| margin-bottom: 20px;
|
| }
|
| </style>
|
| """, unsafe_allow_html=True)
|
|
|
|
|
| st.markdown(f"""
|
| <div class="morpho-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("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")
|
|
|
|
|
| with st.container():
|
| col_chat, col_graph = st.columns([1, 1])
|
|
|
| with col_chat:
|
| st.subheader("Chat with AI")
|
|
|
| chat_container = st.container()
|
| 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()
|
|
|
| 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.") |
