import streamlit as st def display_initial_buttons(): if "upload_flow" not in st.session_state: st.session_state.upload_flow = False if "query_flow" not in st.session_state: st.session_state.query_flow = False if st.button("Upload new CSV"): st.session_state.upload_flow = True st.session_state.query_flow = False if st.button("Query existing data"): st.session_state.query_flow = True st.session_state.upload_flow = False def display_class_dropdown(client): if st.session_state.upload_flow: existing_classes = [cls["class"] for cls in client.schema.get()["classes"]] class_options = existing_classes + ["New Class"] return st.selectbox("Select a class or create a new one:", class_options) elif st.session_state.query_flow: existing_classes = [cls["class"] for cls in client.schema.get()["classes"]] class_options = existing_classes + ["Query all data"] return st.selectbox("Select a class or query all data:", class_options) def handle_new_class_selection(client, selected_class): if selected_class == "New Class": class_name = st.text_input("Enter the new class name:") class_description = st.text_input("Enter a description for the class:") if class_name and class_description: if st.button("Create Vector DB Class"): # Call function to create new class schema in Weaviate create_new_class_schema(class_name, class_description) def csv_upload_and_ingestion(client, selected_class): csv_file = st.file_uploader("Upload a CSV file", type=["csv"]) if csv_file: if st.button("Confirm CSV upload"): # Call function to ingest CSV data into Weaviate ingest_csv_to_weaviate(csv_file, selected_class) def display_query_input(): question = st.text_input("Enter your question:") if question: if st.button("Submit Query"): # Call function to query TAPAS with selected data and entered question query_tapas_with_weaviate_data(st.session_state.data_source, question)