hardcode CPU only

app.py

@@ -14,11 +14,11 @@ example_indexes = {
     "Uber 10k 2021": "./uber_index",
 }
 
-device = "cpu"
-if torch.cuda.is_available():
-    device = "cuda"
-elif torch.backends.mps.is_available():
-    device = "mps"
+# device = "cpu"
+# if torch.cuda.is_available():
+#     device = "cuda"
+# elif torch.backends.mps.is_available():
+#     device = "mps"
 
 image_embed_model = HuggingFaceEmbedding(
     model_name="llamaindex/vdr-2b-multi-v1",
@@ -80,9 +80,6 @@ def create_index(file, llama_parse_key, progress=gr.Progress()):
             image_docs.append(ImageDocument(text=image_dict["name"], image_path=image_dict["path"]))
 
         # Create index
-        # move models back to CPU
-        index._image_embed_model._model.to(device)
-        index._embed_model._model.to(device)
         progress(0.9, desc="Creating final index...")
         index = MultiModalVectorStoreIndex.from_documents(
             text_docs + image_docs,
@@ -95,17 +92,10 @@ def create_index(file, llama_parse_key, progress=gr.Progress()):
     
     except Exception as e:
         return None, f"Error creating index: {str(e)}"
-    finally:
-        # move models back to CPU
-        index._image_embed_model._model.to("cpu")
-        index._embed_model._model.to("cpu")
 
 def run_search(index, query, text_top_k, image_top_k):
     if not index:
         return "Please create or select an index first.", [], []
-    # move models back to CPU
-    index._image_embed_model._model.to(device)
-    index._embed_model._model.to(device)
     retriever = index.as_retriever(
         similarity_top_k=text_top_k,
         image_similarity_top_k=image_top_k,
@@ -114,10 +104,6 @@ def run_search(index, query, text_top_k, image_top_k):
     image_nodes = retriever.text_to_image_retrieve(query)
     text_nodes = retriever.text_retrieve(query)
 
-    # move models back to CPU
-    index._image_embed_model._model.to("cpu")
-    index._embed_model._model.to("cpu")
-
     # Extract text and scores from nodes
     text_results = [{"text": node.text, "score": f"{node.score:.3f}"} for node in text_nodes]