"""Template Demo for IBM Granite Hugging Face spaces."""

from collections.abc import Iterator
from datetime import datetime
from pathlib import Path
from threading import Thread

import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer

from themes.research_monochrome import theme

today_date = datetime.today().strftime("%B %-d, %Y")  # noqa: DTZ002

SYS_PROMPT = f"""Knowledge Cutoff Date: April 2024.
Today's Date: {today_date}.
You are Granite, developed by IBM. You are a helpful AI assistant"""
TITLE = "IBM Granite 3.1 8b Instruct"
DESCRIPTION = """
<p>Granite 3.1 8b instruct is an open-source LLM supporting a 128k context window. Start with one of the sample prompts
or enter your own. Keep in mind that AI can occasionally make mistakes.
<span class="gr_docs_link">
<a href="https://www.ibm.com/granite/docs/">View Documentation <i class="fa fa-external-link"></i></a>
</span>
</p>
"""
MAX_INPUT_TOKEN_LENGTH = 128_000
MAX_NEW_TOKENS = 1024
TEMPERATURE = 0.7
TOP_P = 0.85
TOP_K = 50
REPETITION_PENALTY = 1.05

#model = AutoModelForCausalLM.from_pretrained(
#    "ibm-granite/granite-3.1-8b-instruct", torch_dtype=torch.float16, device_map="auto"
#)
#tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-3.1-8b-instruct")
#tokenizer.use_default_system_prompt = False


def generate(
    message: str,
    chat_history: list[dict],
    temperature: float = TEMPERATURE,
    repetition_penalty: float = REPETITION_PENALTY,
    top_p: float = TOP_P,
    top_k: float = TOP_K,
    max_new_tokens: int = MAX_NEW_TOKENS,
) -> Iterator[str]:
    """Generate function for chat demo."""
    # Build messages
    conversation = []
    conversation.append({"role": "system", "content": SYS_PROMPT})
    conversation += chat_history
    conversation.append({"role": "user", "content": message})

    # Convert messages to prompt format
    input_ids = tokenizer.apply_chat_template(
        conversation,
        return_tensors="pt",
        add_generation_prompt=True,
        truncation=True,
        max_length=MAX_INPUT_TOKEN_LENGTH - max_new_tokens,
    )

    input_ids = input_ids.to(model.device)
    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        {"input_ids": input_ids},
        streamer=streamer,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        num_beams=1,
        repetition_penalty=repetition_penalty,
    )

    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    outputs = []
    for text in streamer:
        outputs.append(text)
        yield "".join(outputs)


css_file_path = Path(Path(__file__).parent / "app.css")
head_file_path = Path(Path(__file__).parent / "app_head.html")

# advanced settings (displayed in Accordion)
temperature_slider = gr.Slider(
    minimum=0, maximum=1.0, value=TEMPERATURE, step=0.1, label="Temperature", elem_classes=["gr_accordion_element"]
)
top_p_slider = gr.Slider(
    minimum=0, maximum=1.0, value=TOP_P, step=0.05, label="Top P", elem_classes=["gr_accordion_element"]
)
top_k_slider = gr.Slider(
    minimum=0, maximum=100, value=TOP_K, step=1, label="Top K", elem_classes=["gr_accordion_element"]
)
repetition_penalty_slider = gr.Slider(
    minimum=0,
    maximum=2.0,
    value=REPETITION_PENALTY,
    step=0.05,
    label="Repetition Penalty",
    elem_classes=["gr_accordion_element"],
)
max_new_tokens_slider = gr.Slider(
    minimum=1,
    maximum=2000,
    value=MAX_NEW_TOKENS,
    step=1,
    label="Max New Tokens",
    elem_classes=["gr_accordion_element"],
)
chat_interface_accordion = gr.Accordion(label="Advanced Settings", open=False)

with gr.Blocks(fill_height=True, css_paths=css_file_path, head_paths=head_file_path, theme=theme, title=TITLE) as demo:
    gr.HTML(f"<h1>{TITLE}</h1>", elem_classes=["gr_title"])
    gr.HTML(DESCRIPTION)
    chat_interface = gr.ChatInterface(
        fn=generate,
        examples=[
            ["Explain the concept of quantum computing to someone with no background in physics or computer science."],
            ["What is OpenShift?"],
            ["What's the importance of low latency inference?"],
            ["Help me boost productivity habits."],
        ],
        example_labels=[
            "Explain quantum computing",
            "What is OpenShift?",
            "Importance of low latency inference",
            "Boosting productivity habits",
        ],
        cache_examples=False,
        type="messages",
        additional_inputs=[
            temperature_slider,
            repetition_penalty_slider,
            top_p_slider,
            top_k_slider,
            max_new_tokens_slider,
        ],
        additional_inputs_accordion=chat_interface_accordion,
    )

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