Update app.py

app.py

@@ -66,16 +66,16 @@ def run_llava(prompt, pil_image, temperature, top_p, max_new_tokens):
     return outputs[0].strip()
 
 
-def load_selected_model(model_path):
-    model_name = model_path.split("/")[-1]
-    global tokenizer, model, image_processor, context_len
-    with warnings.catch_warnings(record=True) as w:
-        warnings.simplefilter("always")
-        tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name)
-    for warning in w:
-        if "vision" not in str(warning.message).lower():
-            print(warning.message)
-    model.config.tokenizer_model_max_length = 2048 * 2
+# def load_selected_model(model_path):
+#     model_name = model_path.split("/")[-1]
+#     global tokenizer, model, image_processor, context_len
+#     with warnings.catch_warnings(record=True) as w:
+#         warnings.simplefilter("always")
+#         tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name)
+#     for warning in w:
+#         if "vision" not in str(warning.message).lower():
+#             print(warning.message)
+#     model.config.tokenizer_model_max_length = 2048 * 2
 
 
 def get_conv_log_filename():
@@ -516,8 +516,8 @@ Set the environment variable `model` to change the model:
         model_path = '/common-repos/LlavaGuard/models/LlavaGuard-v1.1-7b-full/smid_and_crawled_v2_with_augmented_policies/json-v16/llava'
 
     print(f"Loading model {model_path}")
-    # tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name, token=api_key)
-    load_selected_model(model_path)
+    tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name, token=api_key)
+    # load_selected_model(model_path)
     model.config.tokenizer_model_max_length = 2048 * 2
 
     exit_status = 0