Update app.py

app.py

@@ -43,7 +43,7 @@ def build_faiss_index(chunks, model):
     for chunk in chunks:
         input_ids = torch.tensor([chunk])
         with torch.no_grad():
-            embedding = model(input_ids).last_hidden_state.mean(dim=1).numpy()
+            embedding = model(input_ids).last_hidden_state.mean(dim=1).detach().numpy()
         embeddings.append(embedding)
     embeddings = np.vstack(embeddings)
 
@@ -79,7 +79,9 @@ if text:
         st.write("Searching for the most relevant chunk...")
         query_tokens = tokenizer.encode(query, add_special_tokens=False)
         query_embedding = (
-            model(torch.tensor([query_tokens])).last_hidden_state.mean(dim=1).numpy()
+            model(torch.tensor([query_tokens]))
+            .last_hidden_state.mean(dim=1)
+            .detach().numpy()
         )
         _, indices = index.search(query_embedding, k=1)
         
@@ -104,4 +106,3 @@ else:
     st.error("Failed to extract content from the document.")
 
 
-