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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.")
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