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import streamlit as st |
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import logging |
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from .semantic_process import process_semantic_analysis |
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from ..chatbot.chatbot import initialize_chatbot, process_semantic_chat_input |
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from ..database.database_oldFromV2 import store_file_semantic_contents, retrieve_file_contents, delete_file, get_user_files |
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from ..utils.widget_utils import generate_unique_key |
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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 display_semantic_interface(lang_code, nlp_models, t): |
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st.markdown(""" |
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<style> |
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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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.stButton > button { |
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width: 100%; |
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height: 3em; |
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} |
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.chat-container { |
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height: 400px; |
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overflow-y: auto; |
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border: 1px solid #ddd; |
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padding: 10px; |
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border-radius: 5px; |
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} |
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.file-management-container, .analysis-container { |
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border: 1px solid #ddd; |
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padding: 10px; |
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border-radius: 5px; |
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margin-bottom: 20px; |
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} |
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.horizontal-list { |
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display: flex; |
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flex-wrap: wrap; |
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gap: 10px; |
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} |
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.graph-container { |
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height: 500px; |
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overflow-y: auto; |
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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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{get_translation(t, 'semantic_initial_message', 'Welcome to the semantic analysis interface.')} |
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</div> |
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""", unsafe_allow_html=True) |
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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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with st.container(): |
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st.markdown('<div class="file-management-container">', unsafe_allow_html=True) |
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col1, col2, col3, col4 = st.columns(4) |
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with col1: |
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uploaded_file = st.file_uploader(get_translation(t, 'upload_file', 'Upload File'), 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 store_file_semantic_contents(st.session_state.username, uploaded_file.name, file_contents): |
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st.session_state.file_contents = file_contents |
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st.success(get_translation(t, 'file_uploaded_success', 'File uploaded and saved successfully')) |
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st.rerun() |
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else: |
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st.error(get_translation(t, 'file_upload_error', 'Error uploading file')) |
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with col2: |
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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("", options=file_options, key=generate_unique_key('semantic', 'file_selector')) |
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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 = retrieve_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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st.success(get_translation(t, 'file_loaded_success', 'File loaded successfully')) |
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else: |
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st.error(get_translation(t, 'file_load_error', 'Error loading file')) |
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with col3: |
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if st.button(get_translation(t, 'analyze_document', 'Analyze Document'), key=generate_unique_key('semantic', 'analyze_document')): |
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if 'file_contents' in st.session_state: |
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with st.spinner(get_translation(t, 'analyzing', 'Analyzing...')): |
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try: |
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nlp_model = nlp_models[lang_code] |
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concept_graph, entity_graph, key_concepts = process_semantic_analysis(st.session_state.file_contents, nlp_model, lang_code) |
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st.session_state.concept_graph = concept_graph |
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st.session_state.entity_graph = entity_graph |
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st.session_state.key_concepts = key_concepts |
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st.success(get_translation(t, 'analysis_completed', 'Analysis completed')) |
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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(get_translation(t, 'no_file_uploaded', 'No file uploaded')) |
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with col4: |
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if st.button(get_translation(t, 'delete_file', 'Delete File'), key=generate_unique_key('semantic', 'delete_file')): |
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if selected_file and selected_file != get_translation(t, 'select_saved_file', 'Select a saved file'): |
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if delete_file(st.session_state.username, selected_file, 'semantic'): |
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st.success(get_translation(t, 'file_deleted_success', 'File deleted successfully')) |
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if 'file_contents' in st.session_state: |
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del st.session_state.file_contents |
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st.rerun() |
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else: |
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st.error(get_translation(t, 'file_delete_error', 'Error deleting file')) |
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else: |
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st.error(get_translation(t, 'no_file_selected', 'No file selected')) |
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st.markdown('</div>', unsafe_allow_html=True) |
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st.markdown('<div class="analysis-container">', unsafe_allow_html=True) |
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col_chat, col_graph = st.columns([1, 1]) |
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with col_chat: |
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st.subheader(get_translation(t, 'chat_title', 'Semantic Analysis Chat')) |
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chat_container = st.container() |
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with chat_container: |
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chat_history = st.session_state.get('semantic_chat_history', []) |
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for message in chat_history: |
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with st.chat_message(message["role"]): |
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st.write(message["content"]) |
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user_input = st.chat_input(get_translation(t, 'semantic_chat_input', 'Type your message here...'), key=generate_unique_key('semantic', 'chat_input')) |
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if user_input: |
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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) |
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chat_history.append({"role": "assistant", "content": response}) |
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st.session_state.semantic_chat_history = chat_history |
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with col_graph: |
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st.subheader(get_translation(t, 'graph_title', 'Semantic Graphs')) |
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if 'key_concepts' in st.session_state: |
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st.write(get_translation(t, 'key_concepts_title', 'Key Concepts')) |
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st.markdown('<div class="horizontal-list">', unsafe_allow_html=True) |
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for concept, freq in st.session_state.key_concepts: |
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st.markdown(f'<span style="margin-right: 10px;">{concept}: {freq:.2f}</span>', unsafe_allow_html=True) |
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st.markdown('</div>', unsafe_allow_html=True) |
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if 'entities' in st.session_state: |
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st.write(get_translation(t, 'entities_title', 'Entities')) |
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st.markdown('<div class="horizontal-list">', unsafe_allow_html=True) |
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for entity, type in st.session_state.entities.items(): |
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st.markdown(f'<span style="margin-right: 10px;">{entity}: {type}</span>', unsafe_allow_html=True) |
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st.markdown('</div>', unsafe_allow_html=True) |
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tab1, tab2 = st.tabs(["Concept Graph", "Entity Graph"]) |
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with tab1: |
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if 'concept_graph' in st.session_state: |
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st.pyplot(st.session_state.concept_graph) |
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with tab2: |
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if 'entity_graph' in st.session_state: |
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st.pyplot(st.session_state.entity_graph) |
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st.markdown('</div>', unsafe_allow_html=True) |
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if st.button(get_translation(t, 'clear_chat', 'Clear chat'), key=generate_unique_key('semantic', 'clear_chat')): |
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st.session_state.semantic_chat_history = [] |
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st.rerun() |
