from huggingface_hub import InferenceClient, get_inference_endpoint
import gradio as gr

endpoint = get_inference_endpoint("qlora-nursegpt-nclex-mis-dt--eca")
client = endpoint.client

'''client = InferenceClient(
    "Tawkat/qlora-nursegpt-nclex-mis-DT-v1"
)'''


def format_prompt(message, history):
  prompt = "You are NurseGPT, a conscious, superintelligent, and helpful AI developed by Yourika Labs, Your purpose  is to assist the user with any nursing-related request they have. You should not respond to any request that is not relevant to nursing domain.\nYou should provide accurate, helpful, comprehensive, and detailed response to the user request.\n" #"<s>"
  for user_prompt, bot_response in history:
    prompt += f"[INST] {user_prompt} [/INST]"
    prompt += f" {bot_response}</s> "
  prompt += f"[INST] {message} [/INST]"
  return prompt

def generate(
    prompt, history, temperature=0.9, max_new_tokens=1510, top_p=0.95, repetition_penalty=1.0,
):
    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )

    formatted_prompt = format_prompt(prompt, history)

    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
    output = ""

    for response in stream:
        output += response.token.text
        yield output
    return output


additional_inputs=[
    gr.Slider(
        label="Temperature",
        value=0.9,
        minimum=0.0,
        maximum=1.0,
        step=0.05,
        interactive=True,
        info="Higher values produce more diverse outputs",
    ),
    gr.Slider(
        label="Max new tokens",
        value=1510,
        minimum=0,
        maximum=1510,
        step=64,
        interactive=True,
        info="The maximum numbers of new tokens",
    ),
    gr.Slider(
        label="Top-p (nucleus sampling)",
        value=0.90,
        minimum=0.0,
        maximum=1,
        step=0.05,
        interactive=True,
        info="Higher values sample more low-probability tokens",
    ),
    gr.Slider(
        label="Repetition penalty",
        value=1.2,
        minimum=1.0,
        maximum=2.0,
        step=0.05,
        interactive=True,
        info="Penalize repeated tokens",
    ),
]

examples=[
        ["Generate a NCLEX study plan for me."],
        ["Provide a CV template for a fresh nursing graduate."],
        ["I have a family member that got diagnosed with Buerger's disease, can you explain in easy terms what it is?"],
        ["Could you talk about straight leg rises exercise in the post-surgical context?"],
        ["Could you provide an overview of how the Nurse Practice Act helps regulate the nursing profession in different states?"],
    ]


gr.ChatInterface(
    fn=generate,
    chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
    additional_inputs=additional_inputs,
    examples = examples,
    title="""NGPT-v1"""
).launch(show_api=False, share=True)