import gradio as gr
from huggingface_hub import InferenceClient

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient(
    base_url="https://huggingface.co/api/integrations/dgx/v1"
    # api_key="..."  # Uncomment and use your actual API key here if required
)

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
    model,
):
    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,
        model=model  #"meta-llama/Meta-Llama-3.1-8B-Instruct",
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        if token:
            response += token
        yield response

"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""

# List of model IDs
model_options = [
    "meta-llama/Meta-Llama-3.1-8B-Instruct",
    "meta-llama/Meta-Llama-3.1-70B-Instruct",
    "meta-llama/Meta-Llama-3.1-405B-Instruct-FP8",
    "meta-llama/Meta-Llama-3-8B-Instruct",
    "meta-llama/Meta-Llama-3-70B-Instruct",
    "mistralai/Mistral-7B-Instruct-v0.3",
    "mistralai/Mixtral-8x7B-Instruct-v0.1",
    "mistralai/Mixtral-8x22B-Instruct-v0.1",
]

demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, 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)"),
        gr.Dropdown(choices=model_options, value=model_options[0], label="Model"),  # Dropdown for model selection
    ],
)


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