import os
import time
import torch
import gradio as gr

from strings import TITLE, ABSTRACT 
from gen import get_pretrained_models, get_output, setup_model_parallel

os.environ["RANK"] = "0"
os.environ["WORLD_SIZE"] = "1"
os.environ["MASTER_ADDR"] = "127.0.0.1"
os.environ["MASTER_PORT"] = "50505"

local_rank, world_size = setup_model_parallel()
generator = get_pretrained_models("7B", "tokenizer", local_rank, world_size)

history = []

def chat(user_input, top_p, temperature, max_gen_len, state_chatbot):
    bot_response = get_output(
        generator=generator, 
        prompt=user_input,
        max_gen_len=max_gen_len,
        temperature=temperature,
        top_p=top_p)

    # remove the first phrase identical to user prompt
    bot_response = bot_response[0][len(user_input):]
    bot_response = bot_response.replace("\n", "<br><br>")
    # trip the last phrase
    try:
        bot_response = bot_response[:bot_response.rfind(".")]
    except:
        pass

    history.append({
        "role": "user",
        "content": user_input
    })
    history.append({
        "role": "system",
        "content": bot_response
    })    

    state_chatbot = state_chatbot + [(user_input, None)]
    
    response = ""
    for word in bot_response.split(" "):
        time.sleep(0.1)
        response += word + " "
        current_pair = (user_input, response)
        state_chatbot[-1] = current_pair
        yield state_chatbot, state_chatbot

def reset_textbox():
    return gr.update(value='')

with gr.Blocks(css = """#col_container {width: 95%; margin-left: auto; margin-right: auto;}
                #chatbot {height: 400px; overflow: auto;}""") as demo:

    state_chatbot = gr.State([])
                    
    with gr.Column(elem_id='col_container'):
        gr.Markdown(f"## {TITLE}\n\n\n\n{ABSTRACT}")
        chatbot = gr.Chatbot(elem_id='chatbot')
        textbox = gr.Textbox(placeholder="Enter a prompt")

        with gr.Accordion("Parameters", open=False):
            max_gen_len = gr.Slider(minimum=20, maximum=512, value=256, step=1, interactive=True, label="Max Genenration Length",)
            top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
            temperature = gr.Slider(minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
        
    textbox.submit(chat, [textbox, top_p, temperature, max_gen_len, state_chatbot], [state_chatbot, chatbot])
    textbox.submit(reset_textbox, [], [textbox])

demo.queue(api_open=False).launch()