diff --git a/src/utils/peft_trainer.py b/src/utils/peft_trainer.py
index f951fd8c..160a1425 100644
--- a/src/utils/peft_trainer.py
+++ b/src/utils/peft_trainer.py
@@ -49,6 +49,8 @@ class LogCallback(TrainerCallback):
         r"""
         Event called after logging the last logs.
         """
+        if "loss" not in state.log_history[-1]:
+            return
         cur_time = time.time()
         cur_steps = state.log_history[-1].get("step")
         elapsed_time = cur_time - self.start_time
diff --git a/src/web_demo.py b/src/web_demo.py
index 77dd76d7..96bfbcd1 100644
--- a/src/web_demo.py
+++ b/src/web_demo.py
@@ -12,7 +12,7 @@ from transformers import TextIteratorStreamer
 from transformers.utils.versions import require_version
 
 
-require_version("gradio==3.27.0", "To fix: pip install gradio==3.27.0") # higher version may cause problems
+require_version("gradio>=3.30.0", "To fix: pip install gradio>=3.30.0")
 model_args, data_args, finetuning_args = prepare_infer_args()
 model, tokenizer = load_pretrained(model_args, finetuning_args)
 
@@ -93,6 +93,7 @@ def predict(input, chatbot, max_length, top_p, temperature, history):
     input_ids = tokenizer([format_example(input, history)], return_tensors="pt")["input_ids"]
     input_ids = input_ids.to(model.device)
     gen_kwargs = {
+        "input_ids": input_ids,
         "do_sample": True,
         "top_p": top_p,
         "temperature": temperature,
@@ -107,9 +108,9 @@ def predict(input, chatbot, max_length, top_p, temperature, history):
     response = ""
     for new_text in streamer:
         response += new_text
-        history = history + [(input, response)]
+        new_history = history + [(input, response)]
         chatbot[-1] = (parse_text(input), parse_text(response))
-        yield chatbot, history
+        yield chatbot, new_history
 
 
 def reset_user_input():
@@ -145,4 +146,4 @@ with gr.Blocks() as demo:
 
     emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True)
 
-demo.queue().launch(server_name="0.0.0.0", share=False, inbrowser=True)
+demo.queue().launch(server_name="0.0.0.0", share=True, inbrowser=True)