Update app.py

app.py

@@ -179,38 +179,38 @@ def downvote(knowledge_base, query):
     return
 
 
+
 device = 'cuda' 
+
+print("emb model load begin...")
 model_path = 'RhapsodyAI/minicpm-visual-embedding-v0' # replace with your local model path
 tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
 model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
 model.eval()
 model.to(device)
+print("emb model load success!")
+
+print("gen model load begin...")
+gen_model_path = 'openbmb/MiniCPM-V-2_6'
+gen_tokenizer = AutoTokenizer.from_pretrained(gen_model_path, trust_remote_code=True)
+gen_model =  AutoModel.from_pretrained(gen_model_path, trust_remote_code=True, attn_implementation='sdpa', torch_dtype=torch.bfloat16)
+gen_model.eval()
+gen_model.to(device)
+print("gen model load success!")
 
 
 @spaces.GPU(duration=50)
 def answer_question(images, question):
-    print("model load begin...")
-    gen_model_path = 'openbmb/MiniCPM-V-2_6'
-    gen_tokenizer = AutoTokenizer.from_pretrained(gen_model_path, trust_remote_code=True)
-    gen_model =  AutoModel.from_pretrained(gen_model_path, trust_remote_code=True, attn_implementation='sdpa', torch_dtype=torch.bfloat16)
-    gen_model.eval()
-    gen_model.to(device)
-    print("model load success!")
-    
+    global gen_model, gen_tokenizer
     # here each element of images is a tuple of (image_path, None).
-    
     images_ = [Image.open(image[0]).convert('RGB') for image in images]
-    
     msgs = [{'role': 'user', 'content': [*images_, question]}]
-    
     answer = gen_model.chat(
         image=None,
         msgs=msgs,
         tokenizer=gen_tokenizer
     )
-    
     print(answer)
-
     return answer