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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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if 'semantic_chatbot' not in st.session_state:
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st.session_state.semantic_chatbot = initialize_chatbot('semantic')
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st.markdown("""
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<style>
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.stTabs [data-baseweb="tab-list"] {
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gap: 24px;
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}
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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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}
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.stTabs [aria-selected="true"] {
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background-color: #FFFFFF;
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}
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</style>
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""", unsafe_allow_html=True)
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tab1, tab2, tab3, tab4, tab5 = st.tabs(["Upload", "Analyze", "Results", "Chat", "Export"])
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with tab1:
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tab21, tab22 = st.tabs(["File Management", "File Analysis"])
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with tab21:
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st.subheader("Upload and Manage Files")
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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 store_file_semantic_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.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']}"):
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if delete_file(st.session_state.username, file['file_name'], 'semantic'):
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st.success(f"File {file['file_name']} deleted successfully")
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st.rerun()
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else:
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st.error(f"Error deleting file {file['file_name']}")
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else:
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st.write("No files uploaded yet.")
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with tab22:
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st.subheader("Select File for 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("", 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 = 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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with st.spinner("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(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("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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with tab2:
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st.subheader("Analysis Results")
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if 'key_concepts' in st.session_state:
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st.write("Key Concepts:")
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st.write(', '.join([f"{concept}: {freq:.2f}" for concept, freq in st.session_state.key_concepts]))
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col1, col2 = st.columns(2)
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with col1:
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if 'concept_graph' in st.session_state:
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st.subheader("Concept Graph")
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st.pyplot(st.session_state.concept_graph)
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with col2:
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if 'entity_graph' in st.session_state:
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st.subheader("Entity Graph")
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st.pyplot(st.session_state.entity_graph)
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with tab3:
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st.subheader("Chat with AI")
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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("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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if st.button("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()
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with tab4:
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st.subheader("Export Results")
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with tab5:
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st.subheader("Help")
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