diff --git a/source/panel/image_generation.py b/source/panel/image_generation.py
index 13381099..4242984f 100644
--- a/source/panel/image_generation.py
+++ b/source/panel/image_generation.py
@@ -41,6 +41,7 @@
 support_model_list = []
 default_models = ["v1-5-pruned-emaonly.safetensors"]
 
+
 # todo will update api
 def deploy_sagemaker_endpoint(instance_type: str = "ml.g4dn.4xlarge", initial_instance_count: int = 1,
                               endpoint_name: str = "default-endpoint-for-llm-bot"):
@@ -55,12 +56,14 @@ def deploy_sagemaker_endpoint(instance_type: str = "ml.g4dn.4xlarge", initial_in
         "endpoint_name": endpoint_name
     }
     # https://<Your API Gateway ID>.execute-api.<Your AWS Account Region>.amazonaws.com/{basePath}/inference/deploy-sagemaker-endpoint
-    res = requests.post(COMMAND_API_URL + 'inference/deploy-sagemaker-endpoint', headers = headers, json = inputBody)
+    res = requests.post(COMMAND_API_URL + 'inference/deploy-sagemaker-endpoint', headers=headers, json=inputBody)
     logger.info("deploy_sagemaker_endpoint: {}".format(res.json()))
 
+
 def upload_model():
     pass
 
+
 def get_bedrock_llm():
     # specify the profile_name to call the bedrock api if needed
     bedrock_client = boto3.client('bedrock-runtime', region_name=BEDROCK_REGION)
@@ -73,6 +76,7 @@ def get_bedrock_llm():
     )
     return cl_llm
 
+
 # todo template use dynamic checkpoints
 sd_prompt = PromptTemplate.from_template(
     """
@@ -107,6 +111,7 @@ def get_bedrock_llm():
     Assistant:
     """)
 
+
 def get_llm_processed_prompts(initial_prompt):
     cl_llm = get_bedrock_llm()
     memory = ConversationBufferMemory()
@@ -122,13 +127,15 @@ def get_llm_processed_prompts(initial_prompt):
 
     positive_prompt = response.split('Positive Prompt: ')[1].split('Negative Prompt: ')[0].strip()
     negative_prompt = response.split('Negative Prompt: ')[1].split('Recommended Model List: ')[0].strip()
-    model_list = response.split('Recommended Model List: ')[1].strip().replace('[', '').replace(']', '').replace('"', '').split(',')
-    logger.info("positive_prompt: {}\n negative_prompt: {}\n model_list: {}".format(positive_prompt, negative_prompt, model_list))
+    model_list = response.split('Recommended Model List: ')[1].strip().replace('[', '').replace(']', '').replace('"',
+                                                                                                                 '').split(
+        ',')
+    logger.info("positive_prompt: {}\n negative_prompt: {}\n model_list: {}".format(positive_prompt, negative_prompt,
+                                                                                    model_list))
     return positive_prompt, negative_prompt, model_list
 
 
 def generate_image(positive_prompts: str, negative_prompts: str, model: List[str], current_col, progress_bar):
-
     job = create_inference_job(model)
     st.session_state.progress += 5
     progress_bar.progress(st.session_state.progress)
@@ -246,7 +253,6 @@ def get_checkpoints():
 
 
 def create_inference_job(models: List[str]):
-
     models = select_checkpoint(models)
 
     headers = {