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