Upload brain.py

brain.py

@@ -0,0 +1,39 @@
+import streamlit as st
+import numpy as np
+
+def brain(supabase):
+    ## List all documents
+    response = supabase.table("documents").select("name:metadata->>file_name, size:metadata->>file_size", count="exact").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.")