Hide progress bar for BM25S retrieve call

app.py

@@ -58,10 +58,10 @@ def cleanup_graph(request: gr.Request):
     timestamp = datetime.now().replace(microsecond=0).isoformat()
     if request.session_hash in graph_instances["local"]:
         del graph_instances["local"][request.session_hash]
-        print(f"{timestamp} - Deleted local graph for session {request.session_hash}")
+        print(f"{timestamp} - Del local graph for session {request.session_hash}")
     if request.session_hash in graph_instances["remote"]:
         del graph_instances["remote"][request.session_hash]
-        print(f"{timestamp} - Deleted remote graph for session {request.session_hash}")
+        print(f"{timestamp} - Del remote graph for session {request.session_hash}")
 
 
 def append_content(chunk_messages, history, thinking_about):
@@ -98,8 +98,6 @@ def run_workflow(input, history, compute_mode, thread_id, session_hash):
 
     # Get graph instance
     graph = graph_instances[compute_mode].get(session_hash)
-    if graph is not None:
-        print(f"Get {compute_mode} graph for session {session_hash}")
 
     if graph is None:
         # Notify when we're loading the local model because it takes some time
@@ -126,6 +124,9 @@ def run_workflow(input, history, compute_mode, thread_id, session_hash):
         print(f"{timestamp} - Set {compute_mode} graph for session {session_hash}")
         # Notify when model finishes loading
         gr.Success(f"{compute_mode}", duration=4, title=f"Model loaded!")
+    else:
+        timestamp = datetime.now().replace(microsecond=0).isoformat()
+        print(f"{timestamp} - Get {compute_mode} graph for session {session_hash}")
 
     # print(f"Using thread_id: {thread_id}")
 

mods/bm25s_retriever.py

@@ -166,6 +166,6 @@ class BM25SRetriever(BaseRetriever):
             return [self.docs[i] for i in return_docs.documents[0]]
         else:
             return_docs, scores = self.vectorizer.retrieve(
-                processed_query, self.docs, k=self.k
+                processed_query, self.docs, k=self.k, show_progress=False
             )
             return [return_docs[0, i] for i in range(return_docs.shape[1])]