optimize index loading

app.py

@@ -19,6 +19,13 @@ example_input = torch.rand(1, 3, 224, 224).to(device)  # Adjust size if needed
 traced_model = torch.jit.trace(model, example_input)
 traced_model = traced_model.to(device)
 
+# Load faiss index
+index = faiss.read_index("xbgp-faiss.index")
+
+# Load faiss map
+with open("xbgp-faiss-map.json", "r") as f:
+    images = json.load(f)
+
 
 def process_image(image):
     """
@@ -28,10 +35,6 @@ def process_image(image):
     # This will include preprocessing the image, passing it through the model,
     # and then formatting the output (extracted features).
 
-    # Load the index
-    with open("xbgp-faiss-map.json", "r") as f:
-        images = json.load(f)
-
     # Convert to RGB if it isn't already
     if image.mode != "RGB":
         image = image.convert("RGB")
@@ -62,7 +65,6 @@ def process_image(image):
     faiss.normalize_L2(vector)
 
     # Read the index file and perform search of top 50 images
-    index = faiss.read_index("xbgp-faiss.index")
     distances, indices = index.search(vector, 50)
 
     matches = []
@@ -72,8 +74,6 @@ def process_image(image):
         gamerpic["id"] = images[matching_gamerpic]
         gamerpic["score"] = str(round((1 / (distances[0][idx] + 1) * 100), 2)) + "%"
 
-        print(gamerpic)
-
         matches.append(gamerpic)
 
     return matches