diff --git a/src/blackbox/vlms.py b/src/blackbox/vlms.py
index 3e07bfa..baff1da 100644
--- a/src/blackbox/vlms.py
+++ b/src/blackbox/vlms.py
@@ -1,11 +1,11 @@
 from fastapi import Request, Response, status
 from fastapi.responses import JSONResponse
 from injector import singleton,inject
-from typing import Optional
+from typing import Optional, List
 
 from .blackbox import Blackbox
 from ..log.logging_time import logging_time
-from .chroma_query import ChromaQuery
+# from .chroma_query import ChromaQuery
 from ..configuration import VLMConf
 
 import requests
@@ -39,46 +39,53 @@ class VLMS(Blackbox):
         data = args[0]
         return isinstance(data, list)
 
-    def processing(self, prompt, images, model_name: Optional[str] = None) -> str:
+    def processing(self, prompt:str, images:str | bytes, model_name: Optional[str] = None, user_context: List[dict] = None) -> str:
+        """
+        Args:
+            prompt: a string query to the model.
+            images: a base64 string of image data; 
+            user_context: a list of history conversation, should be a list of openai format.
 
+
+        Return:
+            response: a string 
+            history: a list
+        """
         if model_name == "Qwen-VL-Chat":
             model_name = "infer-qwen-vl"
         elif model_name == "llava-llama-3-8b-v1_1-transformers":
             model_name = "infer-lav-lam-v1-1"
         else:
             model_name = "infer-qwen-vl"
+        
 
-
+        # 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')
         ## AutoLoad Model
         # url = 'http://10.6.80.87:8000/' + model_name + '/'
-
-        if is_base64(images):
-            images_data = images
-        else:
-            # print("{}Type of image data in form {}".format('#'*20,type(images)))
-            # print("{}Type of image data in form {}".format('#'*20,type(images.file)))
-            # byte_stream = io.BytesIO(images.read())      
-            # print("{}Type of image data in form {}".format('#'*20,type(byte_stream)))
-            # # roiImg = Image.open(byte_stream) 
-            # # print("{}Successful {}".format('#'*20,type(roiImg)))
-            # return str(type(byte_stream))
-            # images_data = base64.b64encode(byte_stream)
-            with open(images, "rb") as img_file:
-                # images_data = str(base64.b64encode(img_file.read()), 'utf-8')
-                images_data = base64.b64encode(img_file.read())
-
         # data_input = {'model': model_name, 'prompt': prompt, 'img_data': images_data}
-
         # data = requests.post(url, json=data_input)
         # print(data.text)
+        # return data.text
+
         # 'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg'
         ## 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。'}]
         api_client = APIClient(self.url)
-        # api_client = APIClient(f'http://10.6.80.87:23333')
         model_name = api_client.available_models[0]
-        messages = [{
-                'role':
-                'user',
+
+        messages = user_context + [{
+                'role': 'user',
                 'content': [{
                     'type': 'text',
                     'text': prompt,
@@ -93,25 +100,37 @@ class VLMS(Blackbox):
         ]
         
         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
                                                 )):
-            print(item["choices"][0]["message"]['content'])
+            # print(item["choices"][0]["message"]['content'])
             responses += item["choices"][0]["message"]['content']
-        
-        return responses
+            total_token_usage += item['usage']['total_tokens']          # 'usage': {'prompt_tokens': *, 'total_tokens': *, 'completion_tokens': *}
+        user_context = messages + [{'role': 'assistant', 'content': responses}]
+        return responses, user_context
 
-        # return data.text
+        
 
     async def fast_api_handler(self, request: Request) -> Response:
+        json_request = True
         try:
-            data = await request.form()
-        except:
+            content_type = request.headers['content-type']
+            if content_type == 'application/json':
+                data = await request.json()
+            else:
+                data = await request.form()
+                json_request = False
+        except Exception as e:
             return JSONResponse(content={"error": "json parse error"}, status_code=status.HTTP_400_BAD_REQUEST)
-
+        
         model_name = data.get("model_name")
         prompt = data.get("prompt")
-        img_data = data.get("img_data")
+
+        if json_request:
+            img_data = data.get("img_data")
+        else:
+            img_data = await data.get("img_data").read()
 
         if prompt is None:
             return JSONResponse(content={'error': "Question is required"}, status_code=status.HTTP_400_BAD_REQUEST)
@@ -119,6 +138,7 @@ class VLMS(Blackbox):
         if model_name is None or model_name.isspace():
             model_name = "Qwen-VL-Chat"
 
+        response, history = self.processing(prompt, img_data, model_name)
         # jsonresp = str(JSONResponse(content={"response": self.processing(prompt, img_data, model_name)}).body, "utf-8")
 
-        return JSONResponse(content={"response": self.processing(prompt, img_data, model_name)}, status_code=status.HTTP_200_OK)
\ No newline at end of file
+        return JSONResponse(content={"response": response, "history": history}, status_code=status.HTTP_200_OK)
\ No newline at end of file