Improved error message for trust_remote_code models

app.py

@@ -101,8 +101,8 @@ def search_bar_gr(model_names,slider=True,double_search=False,key=None):
     ret.insert(0,col2)
     return ret
 
-import spaces
-@spaces.GPU(duration=300)
+#import spaces
+#@spaces.GPU(duration=300)
 def _run(path,genes,N,progress_bar):
     #Load the model
     progress_bar(0.20, desc="Loading Model...",total=100)
@@ -110,7 +110,7 @@ def _run(path,genes,N,progress_bar):
         model,tokenizer = load_model(path)
     except ValueError as e:
             print(f"Error loading model '{path}': {e}")
-            gr.Warning("Model couldn't load. This space currently only works with AutoModelForCausalLM models. Please check the model architecture and try again.")
+            gr.Warning("Model couldn't load. This space currently only works with AutoModelForCausalLM models and trust_remote_code=False. Please check the model architecture and whether it requires the execution of custom code and try again.")
             return None
     except OSError as e:
             print(f"Error loading model '{path}': {e}")
@@ -175,7 +175,6 @@ def run(path,progress_bar):
 
     progress_bar(1, desc="Done!",total=100)
 
-
 def prepare_run(model_name,progress_bar=gr.Progress()):
     global MODEL_SEARCHED_X,MODEL_NAMES
     if model_name in MODEL_NAMES:
@@ -222,6 +221,8 @@ def reload_env():
     
 
 # Load environment variables
+from dotenv import load_dotenv
+load_dotenv()
 
 USERNAME = os.environ['GITHUB_USERNAME']
 TOKEN = os.environ['GITHUB_TOKEN']

llm_run.py

@@ -34,8 +34,8 @@ def download_llm_to_cache(model_name, revision="main", cache_dir=None):
         return None
 
 def load_model(path,cache_dir=None):
-    model = transformers.AutoModelForCausalLM.from_pretrained(path,cache_dir=cache_dir,device_map='auto')
-    tokenizer = transformers.AutoTokenizer.from_pretrained(path,cache_dir=cache_dir,device_map='auto')
+    model = transformers.AutoModelForCausalLM.from_pretrained(path,cache_dir=cache_dir,device_map='auto',trust_remote_code=False)
+    tokenizer = transformers.AutoTokenizer.from_pretrained(path,cache_dir=cache_dir,device_map='auto',trust_remote_code=False)
     return model,tokenizer
 
 def llm_run(model,tokenizer,genes,N):