app.py

import gradio as gr
from huggingface_hub import InferenceClient

MODELS = {
    "Zephyr 7B Beta": "HuggingFaceH4/zephyr-7b-beta",
    "DeepSeek Coder V2": "deepseek-ai/DeepSeek-Coder-V2-Instruct",
    "Meta Llama 3.1 8B": "meta-llama/Meta-Llama-3.1-8B-Instruct",
    "Mixtral 8x7B": "mistralai/Mixtral-8x7B-Instruct-v0.1",
    "Cohere Command R+": "CohereForAI/c4ai-command-r-plus",
}

def get_client(model_name):
    return InferenceClient(MODELS[model_name])

def respond(
    message,
    history: list[tuple[str, str]],
    model_name,
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    client = get_client(model_name)
    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})

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content
        response += token
        yield response

demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Dropdown(choices=list(MODELS.keys()), label="Language Model", value="Zephyr 7B Beta"),
        gr.Textbox(value="You are a friendly and helpful AI assistant.", label="System Message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max New Tokens"),
        gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
    ],
    title="Advanced AI Chatbot",
    description="Chat with different language models and customize your experience!",
)

if __name__ == "__main__":
    demo.launch()