Update app.py

app.py

@@ -4,10 +4,10 @@ from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
 ### Model Setup ###
 # Names for the two models
-LLAMA_MODEL_NAME = "meta-llama/Llama-3.1-8B"
+LLAMA_MODEL_NAME = "meta-llama/Llama-3.2-11B-Vision"
 QWEN_MODEL_NAME = "Qwen/Qwen2.5-VL-7B-Instruct"
 
-# Load Meta-Llama
+# Load Meta-Llama Vision model
 llama_tokenizer = AutoTokenizer.from_pretrained(LLAMA_MODEL_NAME)
 llama_model = AutoModelForCausalLM.from_pretrained(LLAMA_MODEL_NAME, device_map="auto")
 llama_pipe = pipeline(
@@ -29,7 +29,6 @@ def generate_response(prompt: str, model_choice: str) -> str:
     """
     Given a prompt and a model choice, generate a response.
     """
-    # Parameters can be tuned as needed.
     kwargs = {
         "max_length": 256,
         "do_sample": True,
@@ -45,9 +44,8 @@ def generate_response(prompt: str, model_choice: str) -> str:
 
 def chat_logic(user_input: str, chat_history: list):
     """
-    This function builds the prompt from the conversation history,
-    chooses which model will respond (based on whether the user interjected
-    or not) and appends the response to the chat history.
+    Build the conversation prompt from the history, decide which model responds,
+    and update the conversation.
     """
     if chat_history is None:
         chat_history = []
@@ -70,7 +68,7 @@ def chat_logic(user_input: str, chat_history: list):
             else:
                 selected_model = random.choice(["Llama", "Qwen"])
     
-    # Build the prompt from the conversation history.
+    # Construct the conversation prompt.
     prompt = ""
     for speaker, message in chat_history:
         prompt += f"{speaker}: {message}\n"
@@ -85,10 +83,10 @@ def chat_logic(user_input: str, chat_history: list):
 
 ### Gradio Interface ###
 with gr.Blocks() as demo:
-    gr.Markdown("# Group Chat: Meta-Llama and Qwen Models")
+    gr.Markdown("# Group Chat: Meta-Llama Vision and Qwen Models")
     gr.Markdown(
-        "This is a demo where two models converse with each other. "
-        "Leave the text box empty to let the models continue chatting automatically, "
+        "This demo allows two models to converse with each other. "
+        "Leave the textbox blank to let the models continue the conversation automatically, "
         "or type a message to interject (a random model will then respond)."
     )
     
@@ -106,7 +104,7 @@ with gr.Blocks() as demo:
     # Maintain the conversation history in Gradio's state.
     state = gr.State([])
 
-    # When the send button is clicked, update the chat.
+    # Update the chat when the send button is clicked.
     send_btn.click(
         fn=chat_logic,
         inputs=[user_message, state],