import gradio as gr
import torch
import tensorflow as tf
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer, GPT2LMHeadModel, GPT2Tokenizer
import time
import numpy as np
from torch.nn import functional as F
import os
from threading import Thread

print(f"Starting to load the model to memory")

tok = GPT2Tokenizer.from_pretrained("ethzanalytics/ai-msgbot-gpt2-XL-dialogue")
m = GPT2LMHeadModel.from_pretrained("ethzanalytics/ai-msgbot-gpt2-XL-dialogue", pad_token_id=tok.eos_token_id)
generator = pipeline('text-generation', model=m, tokenizer=tok)
print(f"Sucessfully loaded the model to the memory")

start_message = """You are an AI called assistant."""


class StopOnTokens(StoppingCriteria):
    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        stop_ids = [50278, 50279, 50277, 1, 0]
        for stop_id in stop_ids:
            if input_ids[0][-1] == stop_id:
                return True
        return False


def user(message, history):
    # Append the user's message to the conversation history
    return "", history + [[message, ""]]


def chat(curr_system_message, history):
    # Initialize a StopOnTokens object
    stop = StopOnTokens()

    # Construct the input message string for the model by concatenating the current system message and conversation history
    messages = curr_system_message + \
        "".join(["".join(["\nperson alpha:"+item[0], "\nperson beta:"+item[1]])
                for item in history])

    # Tokenize the messages string
    model_inputs = tok([messages], return_tensors="pt")
    streamer = TextIteratorStreamer(
        tok, skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        model_inputs,
        streamer=streamer,
        max_new_tokens=1024,
        do_sample=True,
        top_p=0.95,
        top_k=1000,
        temperature=1.0,
        num_beams=1,
        stopping_criteria=StoppingCriteriaList([stop])
    )
    t = Thread(target=m.generate, kwargs=generate_kwargs)
    t.start()

    # print(history)
    # Initialize an empty string to store the generated text
    partial_text = ""
    for new_text in streamer:
        # print(new_text)
        partial_text += new_text
        history[-1][1] = partial_text
        # Yield an empty string to cleanup the message textbox and the updated conversation history
        yield history
    return partial_text


with gr.Blocks() as demo:
    # history = gr.State([])
    gr.Markdown("## StableLM-Tuned-Alpha-7b Chat")
    gr.HTML('''<center><a href="https://huggingface.co./spaces/stabilityai/stablelm-tuned-alpha-chat?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space to skip the queue and run in a private space</center>''')
    chatbot = gr.Chatbot().style(height=500)
    with gr.Row():
        with gr.Column():
            msg = gr.Textbox(label="Chat Message Box", placeholder="Chat Message Box",
                             show_label=False).style(container=False)
        with gr.Column():
            with gr.Row():
                submit = gr.Button("Submit")
                stop = gr.Button("Stop")
                clear = gr.Button("Clear")
    system_msg = gr.Textbox(
        start_message, label="System Message", interactive=False, visible=False)

    submit_event = msg.submit(fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False).then(
        fn=chat, inputs=[system_msg, chatbot], outputs=[chatbot], queue=True)
    submit_click_event = submit.click(fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False).then(
        fn=chat, inputs=[system_msg, chatbot], outputs=[chatbot], queue=True)
    stop.click(fn=None, inputs=None, outputs=None, cancels=[
               submit_event, submit_click_event], queue=False)
    clear.click(lambda: None, None, [chatbot], queue=False)

demo.queue(max_size=32, concurrency_count=2)
demo.launch()