import gradio as gr
from huggingface_hub import InferenceClient
#from unsloth import FastLanguageModel
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer


"""
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("HuggingFaceH4/zephyr-7b-beta")
#client = InferenceClient("halme/id2223_lora_model")


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, tokenizer = FastLanguageModel.from_pretrained(
        model_name = "halme/id2223_lora_model", # YOUR MODEL YOU USED FOR TRAINING
        max_seq_length = max_tokens,
        dtype = None,
        load_in_4bit = True,
    ) """

    model = AutoPeftModelForCausalLM.from_pretrained(
        "halme/id2223_lora_model", # YOUR MODEL YOU USED FOR TRAINING
    )
    tokenizer = AutoTokenizer.from_pretrained("halme/id2223_lora_model") 

    #FastLanguageModel.for_inference(model) # Enable native 2x faster inference

    """messages = [
        {"role": "user", "content": "Continue the fibonnaci sequence: 1, 1, 2, 3, 5, 8,"},
    ] """

    inputs = tokenizer.apply_chat_template(
        messages,
        tokenize = True,
        add_generation_prompt = True, # Must add for generation
        return_tensors = "pt",
    )

    from transformers import TextStreamer
    text_streamer = TextStreamer(tokenizer, skip_prompt = True)

    yield model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 128,
                    use_cache = True, temperature = 1.5, min_p = 0.1)


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


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