Update app.py

app.py

@@ -68,17 +68,20 @@ weights_gen = torch.load(gen_weight, map_location=torch.device(device))
 
 # Apply the weights to your model
 generator.load_state_dict(weights_gen)
-        
 
-# Load your model and other components here
 model = SentenceTransformer('sentence-transformers/all-MiniLM-L12-v2')
-with open(os.path.join("descriptions.json"), 'r') as file:
-    dataset = json.load(file)
 
-classes = [e["text"] for e in dataset]
-embeddings_list = {cls: model.encode(cls, convert_to_tensor=True) for cls in classes}
+def load_embedding(model):
+    # Load your model and other components here
+    with open(os.path.join("descriptions.json"), 'r') as file:
+        dataset = json.load(file)
+    
+    classes = [e["text"] for e in dataset]
+    embeddings_list = {cls: model.encode(cls, convert_to_tensor=True) for cls in classes}
+    return embeddings_list
 
 def generate_image(caption):
+    embeddings = load_embedding(model)
     noise_dim = 16
     results = [(util.pytorch_cos_sim(model.encode(caption, convert_to_tensor=True), embeddings[cls]).item(), cls) for cls in classes]
     sorted_results = sorted(results, key=lambda x: x[0], reverse=True)[:5]