diff --git a/web_demo_streamlit-2_5.py b/web_demo_streamlit-2_5.py
index bf100ce..5182a39 100644
--- a/web_demo_streamlit-2_5.py
+++ b/web_demo_streamlit-2_5.py
@@ -4,11 +4,11 @@
 from transformers import AutoModel, AutoTokenizer
 
 # Model path
-model_path = "openbmb/MiniCPM-Llama3-V-2_5"
+model_path = "./MiniCPM-Llama3-V-2_5-int4"
 
 # User and assistant names
-U_NAME = "User"
-A_NAME = "Assistant"
+U_NAME = "YOU"
+A_NAME = "AI"
 
 # Set page configuration
 st.set_page_config(
@@ -22,7 +22,7 @@
 @st.cache_resource
 def load_model_and_tokenizer():
     print(f"load_model_and_tokenizer from {model_path}")
-    model = AutoModel.from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.float16).to(device="cuda")
+    model = AutoModel.from_pretrained(model_path, trust_remote_code=True,)
     tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
     return model, tokenizer
 
@@ -89,20 +89,34 @@ def load_model_and_tokenizer():
     model = st.session_state.model
     tokenizer = st.session_state.tokenizer
 
-    with st.chat_message(A_NAME, avatar="assistant"):
-        # If the previous message contains an image, pass the image to the model
-        if len(st.session_state.chat_history) > 1 and st.session_state.chat_history[-2]["image"] is not None:
-            uploaded_image = st.session_state.chat_history[-2]["image"]
-            imagefile = Image.open(uploaded_image).convert('RGB')
+    if selected_mode == "Image":
+        with st.chat_message(A_NAME, avatar="assistant"):
+            # If the previous message contains an image, pass the image to the model
+            if len(st.session_state.chat_history) > 1 and st.session_state.chat_history[-2]["image"] is not None:
+                uploaded_image = st.session_state.chat_history[-2]["image"]
+                imagefile = Image.open(uploaded_image).convert('RGB')
 
-        msgs = [{"role": "user", "content": user_text}]
-        res = model.chat(image=imagefile, msgs=msgs, context=None, tokenizer=tokenizer,
-                         sampling=True, top_p=top_p, top_k=top_k, repetition_penalty=repetition_penalty,
-                         temperature=temperature, stream=True)
+            msgs = [{"role": "user", "content": user_text}]
+            res = model.chat(image=imagefile, msgs=msgs, context=None, tokenizer=tokenizer,
+                            sampling=True, top_p=top_p, top_k=top_k, repetition_penalty=repetition_penalty,
+                            temperature=temperature, stream=True)
 
-        # Collect the generated_text str
-        generated_text = st.write_stream(res)
+            # Collect the generated_text str
+            generated_text = st.write_stream(res)
 
-        st.session_state.chat_history.append({"role": "model", "content": generated_text, "image": None})
+            st.session_state.chat_history.append({"role": "model", "content": generated_text, "image": None})
 
-    st.divider()
+        st.divider()
+    else:
+        with st.chat_message(A_NAME, avatar="assistant"):
+            msgs = [{"role": "user", "content": user_text}]
+            res = model.chat(image=None,msgs=msgs, context=None, tokenizer=tokenizer,
+                            sampling=True, top_p=top_p, top_k=top_k, repetition_penalty=repetition_penalty,
+                            temperature=temperature, stream=True)
+
+            # Collect the generated_text str
+            generated_text = st.write_stream(res)
+
+            st.session_state.chat_history.append({"role": "model", "content": generated_text, "image": None})
+
+        st.divider()