import numpy as np import streamlit as st def brain(supabase): ## List all documents response = supabase.table("documents").select("name:metadata->>file_name, size:metadata->>file_size", count="exact").filter('metadata->>user', 'eq', st.session_state["username"]).execute() documents = response.data # Access the data from the response # Convert each dictionary to a tuple of items, then to a set to remove duplicates, and then back to a dictionary unique_data = [dict(t) for t in set(tuple(d.items()) for d in documents)] # Sort the list of documents by size in decreasing order unique_data.sort(key=lambda x: int(x['size']), reverse=True) # Display some metrics at the top of the page col1, col2 = st.columns(2) col1.metric(label="Total Documents", value=len(unique_data)) col2.metric(label="Total Size (bytes)", value=sum(int(doc['size']) for doc in unique_data)) for document in unique_data: # Create a unique key for each button by using the document name button_key = f"delete_{document['name']}" # Display the document name, size and the delete button on the same line col1, col2, col3 = st.columns([3, 1, 1]) col1.markdown(f"**{document['name']}** ({document['size']} bytes)") if col2.button('❌', key=button_key): delete_document(supabase, document['name']) def delete_document(supabase, document_name): # Delete the document from the database response = supabase.table("documents").delete().match({"metadata->>file_name": document_name}).execute() # Check if the deletion was successful if len(response.data) > 0: st.write(f"✂️ {document_name} was deleted.") else: st.write(f"❌ {document_name} was not deleted.")