app.py

Your code is well-structured and seems to be implementing a simple chatbot using the Hugging Face OpenAI API and the Gradio library. However, there are a few issues that need to be addressed:

1.  The `respond` function does not yield the response after processing all the input messages. You need to add `yield` after `response += token_text`.

2.  The `client.chat.completions.create` function returns an iterator of completion objects, and the response will be in the form of a list of these objects. You need to iterate over these objects and get the `choices` attribute to get the response text.

Here is the corrected code:

```python
import gradio as gr
from openai import OpenAI
import os

ACCESS_TOKEN = os.getenv("HF_TOKEN")
print("Access token loaded.")

client = OpenAI(
    base_url="https://api-inference.huggingface.co/v1/",
    api_key=ACCESS_TOKEN,
)
print("OpenAI client initialized.")

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
    frequency_penalty,
    seed,
    custom_model
):
    print(f"Received message: {message}")
    print(f"History: {history}")
    print(f"System message: {system_message}")
    
    if seed == -1:
        seed = None
    
    messages = [{"role": "system", "content": system_message}]
    
    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})
    
    messages.append({"role": "user", "content": message})
    
    model_to_use = custom_model.strip() if custom_model.strip()!= "" else "meta-llama/Llama-3.2-3B-Instruct"
    
    response = ""
    
    for message_chunk in client.chat.completions.create(
        model=model_to_use,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
        frequency_penalty=frequency_penalty,
        seed=seed,
        messages=messages,
    ):
        for choice in message_chunk.choices:
            token_text = choice.delta.content
            response += token_text
            yield response

chatbot = gr.Chatbot(height=600, show_copy_button=True, placeholder="ChatGPT is initializing...", likeable=True, layout="panel")

system_message_box = gr.Label(value="You can select Max Tokens, Temperature, Top-P, Seed")

max_tokens_slider = gr.Slider(1024, 2048, value=1024, step=100, label="Max new tokens")
temperature_slider = gr.Slider(0.1, 1.0, value=0.7, step=0.1, label="Temperature")
top_p_slider = gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-P")
frequency_penalty_slider = gr.Slider(-2.0, 2.0, value=0.0, step=0.1, label="Frequency Penalty")
seed_slider = gr.Slider(-1, 65535, value=-1, step=1, label="Seed (-1 for random)")

custom_model_box = gr.Textbox(value="meta-llama/Llama-3.2-3B-Instruct", label="AI Mode is ")

demo = gr.ChatInterface(
    fn=respond,
    additional_inputs=[
        system_message_box,
        max_tokens_slider,
        temperature_slider,
        top_p_slider,
        frequency_penalty_slider,
        seed_slider,
        custom_model_box,
    ],
    fill_height=True,
    chatbot=chatbot,
    
)

if __name__ == "__main__":
    print("Launching the ChatGPT-Llama...")
    demo.launch()
```

Now, the chatbot should respond correctly to the user's input.