import os
from threading import Thread
from typing import Iterator
import gradio as gr
from typing import List, Tuple
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import spaces

MAX_INPUT_TOKEN_LENGTH= 50

LICENSE = """
<p/>
---
As a derivate work of [ConsistentAgents]() by Seonghee Lee.
"""

if torch.cuda.is_available():
    model_id = "./backprop_llama2_69_1e-05"
    HF_ACCESS_TOKEN = os.getenv('HF_ACCESS_TOKEN')
    model = AutoModelForCausalLM.from_pretrained(model_id, use_auth_token=HF_ACCESS_TOKEN, torch_dtype=torch.float16, device_map="auto")
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    tokenizer.use_default_system_prompt = False

@spaces.GPU
def generate(
    message: str,
    chat_history: List[Tuple[str, str]],
    system_prompt: 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]:
    conversation = []
    if system_prompt:
        conversation.append({"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")
    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)


# Create the Gradio interface
# gr.ChatInterface(
#     yes_man,
#     chatbot=gr.Chatbot(height=300),
#     textbox=gr.Textbox(placeholder="Ask me a yes or no question", container=False, scale=7),
#     title="Yes Man",
#     description="Ask Yes Man any question",
#     theme="soft",
#     examples=["Hello", "Am I cool?", "Are tomatoes vegetables?"],
#     cache_examples=True,
#     retry_btn=None,
#     undo_btn="Delete Previous",
#     clear_btn="Clear",
# ).launch()


chat_interface = gr.ChatInterface(
    fn=generate,
    additional_inputs=[
        gr.Textbox(label="System prompt", lines=6),
        ], 
    )

with gr.Blocks(css="style.css") as demo:
    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
    chat_interface.render()
    gr.Markdown(LICENSE)


if __name__ == "__main__":
    demo.queue(max_size=20).launch(server_name='10.79.12.70',share=True)