from threading import Thread

import gradio as gr
from huggingface_hub import InferenceClient
import spaces
from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer

model_id = "CohereForAI/aya-expanse-8b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id).to("cuda")


@spaces.GPU
def generate(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p):
    
    conversation = [{"role": "system", "content": system_message}]
    for user, assistant in history:
        conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
    conversation.append({"role": "user", "content": message})

    input_ids = tokenizer.apply_chat_template(conversation, tokenize=True, add_generation_prompt=True, return_tensors="pt")

    # if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
    #     input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
    #     gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
    input_ids = input_ids.to(model.device)

    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
            
    generate_kwargs = dict(
        {"input_ids": input_ids},
        streamer=streamer,
        max_new_tokens=max_tokens,
        do_sample=True,
        top_p=top_p,
        temperature=temperature,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

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


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    generate,
    cache_examples=False,
    additional_inputs=[
        gr.Textbox(value="Je bent een vriendelijke, behulpzame 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)",
        ),
    ],
    examples=[
        ["""Vraagje: welk woord hoort er niet in dit rijtje thuis: "auto, vliegtuig, geit, bus"?"""],
        ["Schrijf een nieuwsbericht voor De Speld over de inzet van een kudde geiten door het Nederlands Forensisch Instituut"],
        ["Wat zijn 3 leuke dingen om te doen als ik een weekendje naar Friesland ga?"],
        ["Met wie trad clown Bassie op?"],
        ["Kan je naar de maan fietsen?"],
        ["Wat is het belang van open source taalmodellen?"],
    ],
    title="Aya Expanse 8B demo",
)


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