import os
from threading import Thread
from typing import Iterator

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

MAX_MAX_NEW_TOKENS = 1024
DEFAULT_MAX_NEW_TOKENS = 512
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))

DESCRIPTION = """\
# Msaidizi wa AI ya Kiswahili

Hii inaonyesha kielelezo cha Kiswahili (Jacaranda) kilichoundwa kutoka Llama-2 7b, kinachotumiwa kama msaidizi wa AI kwa maisha ya kila siku.

(This Space demonstrates the [Swahili (Jacaranda) model](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.1) fine-tuned from Llama-2 7b, used as a daily life AI assistant.)
"""

LICENSE = """
<p/>

---
As a derivate work of [Llama-2-7b-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat) by Meta,
this demo is governed by the original [license](https://huggingface.co/spaces/huggingface-projects/llama-2-7b-chat/blob/main/LICENSE.txt) and [acceptable use policy](https://huggingface.co/spaces/huggingface-projects/llama-2-7b-chat/blob/main/USE_POLICY.md).
"""

SYSTEM_PROMPT = "Below is an instruction that describes a task. Write a response that appropriately completes the request."

PROMPT_TEMPLATE = """{% if messages[0]['role'] == 'system' %}{{ messages[0]['content'] + '\n\n' }}{% endif %}### Instruction:\nWewe ni msaidizi wa AI unayepiga gumzo na mtumiaji.Hii ndiyo historia ya soga ya watu unaowasiliana nao kufikia sasa:\n\n{% for message in messages %}{% if message['role'] == 'user' %}{{ '\nUser: ' + message['content'] + '\n'}}{% elif message['role'] == 'assistant' %}{{ '\nAI: ' + message['content'] + '\n'}}{% endif %}{% endfor %}\n\nKama msaidizi wa AI, andika jibu lako linalofuata kwenye gumzo.\n\n### Response:\n"""

if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"

if torch.cuda.is_available():
    model_id = "Jacaranda/UlizaLlama"
    model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    tokenizer.add_special_tokens({'pad_token': '[PAD]'})
    tokenizer.chat_template = PROMPT_TEMPLATE
    tokenizer.use_default_system_prompt = False

@spaces.GPU
def generate(
    message: str,
    chat_history: list[tuple[str, str]],
    max_new_tokens: int = 1024,
    temperature: float = 0.6,
    top_p: float = 0.9,
    top_k: int = 50,
    repetition_penalty: float = 1.2,
) -> Iterator[str]:
    print("chat history: ", chat_history)
    conversation = [{"role": "system", "content": SYSTEM_PROMPT}]
    for user, assistant in chat_history:
        conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
    conversation.append({"role": "user", "content": message})
    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt")
    print(tokenizer.apply_chat_template(conversation, tokenize=False))
    print(conversation)
    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_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)

examples = [
    ["Ninawezaje kupata usingizi haraka?"],
    ["Bosi wangu anadhibiti sana, nifanye nini?"],
    ["Je, ni vipindi gani muhimu katika historia vya kujua kuvihusu?"],
    ["Ni kazi gani nzuri ikiwa ninataka kupata pesa lakini pia kufurahiya?"],
    ["Nivae nini kwenye harusi?"],
]

with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    chatbot = gr.Chatbot()
    msg = gr.Textbox(label="Ingiza ujumbe wako / Enter your message")
    submit_btn = gr.Button("Wasilisha / Submit")
    clear = gr.Button("Wazi / Clear")

    def user(user_message, history):
        return "", history + [[user_message, None]]

    def bot(history, max_new_tokens, temperature, top_p, top_k, repetition_penalty):
        user_message = history[-1][0]
        chat_history = [(msg[0], msg[1]) for msg in history[:-1]]
        bot_message = ""
        for response in generate(user_message, chat_history, max_new_tokens, temperature, top_p, top_k, repetition_penalty):
            bot_message = response
            history[-1][1] = bot_message
            yield history

    gr.Examples(examples=examples, inputs=[msg], label="Mifano / Examples")

    with gr.Accordion("Chaguzi za Juu / Advanced Options", open=False):
        max_new_tokens = gr.Slider(label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS)
        temperature = gr.Slider(label="Temperature", minimum=0.1, maximum=4.0, step=0.1, value=0.6)
        top_p = gr.Slider(label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9)
        top_k = gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50)
        repetition_penalty = gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2)

    submit_btn.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(
        bot,
        [chatbot, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
        chatbot,
    )
    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
        bot,
        [chatbot, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
        chatbot,
    )
    clear.click(lambda: None, None, chatbot, queue=False)
    
    gr.Markdown(LICENSE)

if __name__ == "__main__":
    demo.queue(max_size=20).launch()