diff --git a/src/blackbox/vlms.py b/src/blackbox/vlms.py
index ba2ab43..489bbbe 100644
--- a/src/blackbox/vlms.py
+++ b/src/blackbox/vlms.py
@@ -112,15 +112,18 @@ class VLMS(Blackbox):
             settings = {}
 
         # Transform the images into base64 format where openai format need.
-        if is_base64(images):                               # image as base64 str
-            images_data = images    
-        elif isinstance(images,bytes):                      # image as bytes
-            images_data = str(base64.b64encode(images),'utf-8')
-        else:                                               # image as pathLike str
-            # with open(images, "rb") as img_file:
-            #     images_data = str(base64.b64encode(img_file.read()), 'utf-8')
-            res = requests.get(images)
-            images_data = str(base64.b64encode(res.content),'utf-8')
+        if images:
+            if is_base64(images):                               # image as base64 str
+                images_data = images    
+            elif isinstance(images,bytes):                      # image as bytes
+                images_data = str(base64.b64encode(images),'utf-8')
+            else:                                               # image as pathLike str
+                # with open(images, "rb") as img_file:
+                #     images_data = str(base64.b64encode(img_file.read()), 'utf-8')
+                res = requests.get(images)
+                images_data = str(base64.b64encode(res.content),'utf-8')
+        else:
+            images_data = None
         ## AutoLoad Model
         # url = 'http://10.6.80.87:8000/' + model_name + '/'
         # data_input = {'model': model_name, 'prompt': prompt, 'img_data': images_data}
@@ -132,38 +135,74 @@ class VLMS(Blackbox):
         ## Lmdeploy
         if not user_context:
             user_context = []
-            # user_context = [{'role':'user','content':'你好'}, {'role': 'assistant', 'content': '你好！很高兴为你提供帮助。'}]
+
+            ## Predefine user_context only for testing
+            # user_context = [{'role':'user','content':'你好，我叫康康，你是谁？'}, {'role': 'assistant', 'content': '你好！很高兴为你提供帮助。'}]
+            user_context = [{
+                    'role': 'user',
+                    'content': [{
+                        'type': 'text',
+                        'text': '图中有什么，请描述一下',
+                    }, {
+                        'type': 'image_url',
+                        'image_url': {
+                            'url': 'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg'
+                        },
+                    }]
+                },{
+                    'role': 'assistant',
+                    'content': '图片中主要展示了一只老虎，它正在绿色的草地上休息。草地上有很多可以让人坐下的地方，而且看起来相当茂盛。背景比较模糊，可能是因为老虎的影响，让整个图片的其他部分都变得不太清晰了。'
+                    }
+            ]
         api_client = APIClient(self.url)
         model_name = api_client.available_models[0]
 
-        messages = user_context + [{
-                'role': 'user',
-                'content': [{
-                    'type': 'text',
-                    'text': prompt,
-                }, {
-                    'type': 'image_url',
-                    'image_url': {
-                        'url': f"data:image/jpeg;base64,{images_data}",
-                        # './val_data/image_5.jpg',
-                    },
-                }]
-            }
-        ]
+        # Reformat input into openai format to request.
+        if images_data:
+            messages = user_context + [{
+                    'role': 'user',
+                    'content': [{
+                        'type': 'text',
+                        'text': prompt,
+                    },{
+                        'type': 'image_url',
+                        'image_url': {      # Image two
+                            'url': 
+                            # 'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg'
+                            # './val_data/image_5.jpg'
+                            f"data:image/jpeg;base64,{images_data}",
+                        },
+                    # },{                 # Image one
+                    #     'type': 'image_url',
+                    #     'image_url': {
+                    #         'url': 'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg'
+                    #     },
+                    }]
+                }
+            ]
+        else:
+            messages = user_context + [{
+                    'role': 'user',
+                    'content': [{
+                        'type': 'text',
+                        'text': prompt,
+                    }]
+                }
+            ]
         
         responses = ''
         total_token_usage = 0   # which can be used to count the cost of a query 
         for i,item in enumerate(api_client.chat_completions_v1(model=model_name,
-                                                messages=messages,stream = True,
+                                                messages=messages,#stream = True,
                                                 **settings,
                                                 # session_id=,
                                                 )):
             # Stream output
-            print(item["choices"][0]["delta"]['content'],end='')
-            responses += item["choices"][0]["delta"]['content']
+            # print(item["choices"][0]["delta"]['content'],end='')
+            # responses += item["choices"][0]["delta"]['content']
 
-            # print(item["choices"][0]["message"]['content'])
-            # responses += item["choices"][0]["message"]['content']
+            print(item["choices"][0]["message"]['content'])
+            responses += item["choices"][0]["message"]['content']
             # total_token_usage += item['usage']['total_tokens']          # 'usage': {'prompt_tokens': *, 'total_tokens': *, 'completion_tokens': *}
 
         user_context = messages + [{'role': 'assistant', 'content': responses}]
@@ -185,13 +224,13 @@ class VLMS(Blackbox):
         
         model_name = data.get("model_name")
         prompt = data.get("prompt")
-        
+        settings: dict = data.get('settings') 
+
         if json_request:
-            img_data = data.get("img_data")
-            settings: dict = data.get('settings')
+            img_data = data.get("img_data")    
         else:
             img_data = await data.get("img_data").read()
-            settings: dict = ast.literal_eval(data.get('settings'))
+            if settings: settings = ast.literal_eval(settings)
 
         if prompt is None:
             return JSONResponse(content={'error': "Question is required"}, status_code=status.HTTP_400_BAD_REQUEST)