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("meta-llama/Meta-Llama-3-8B")


def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    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
"""
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device="cuda",
)

def chat_function(message, history, system_prompt, max_new_tokens, temperature):
    messages = [{"role":"system","content":system_prompt},
                {"role":"user", "content":message}]
    prompt = pipeline.tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True,)
    terminators = [
        pipeline.tokenizer.eos_token_id,
        pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")]
    outputs = pipeline(
        prompt,
        max_new_tokens = max_new_tokens,
        eos_token_id = terminators,
        do_sample = True,
        temperature = temperature + 0.1,
        top_p = 0.9,)
    return outputs[0]["generated_text"][len(prompt):]


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
            
demo = gr.ChatInterface(
    chat_function,
    textbox=gr.Textbox(placeholder="Enter message here", container=False, scale = 7),
    chatbot=gr.Chatbot(height=400),
    additional_inputs=[
        gr.Textbox("You are helpful AI", label="System Prompt"),
        gr.Slider(500,4000, label="Max New Tokens"),
        gr.Slider(0,1, label="Temperature", value= 0.7)
        ],
    )
if __name__ == "__main__":
    demo.launch()