import gradio as gr
from transformers import pipeline
import torch

import re

import spaces

torch.set_default_device("cuda")

model_id = "glides/llama-eap"
pipe = pipeline("text-generation", model=model_id, device_map="auto")

with open("sys", "r") as f:
    system_prompt = f.read()

def follows_rules(s):
    pattern = r'<thinking>.+?</thinking><output>.+?</output><reflecting>.+?</reflecting><refined>.+?</refined>'
    return bool(re.match(pattern, s.replace("\n", "")))

@spaces.GPU(duration=120)
def predict(input_text, history):
    chat = [{"role": "system", "content": system_prompt}]
    for item in history:
        chat.append({"role": "user", "content": item[0]})
        if item[1] is not None:
            chat.append({"role": "assistant", "content": item[1]})
    chat.append({"role": "user", "content": input_text})
    
    generated_text = pipe(chat, max_new_tokens=2 ** 16)[0]['generated_text'][-1]['content']
    
    removed_pres = "<thinking>" + generated_text.split("<thinking>")[-1]
    removed_posts = removed_pres.split("</refined>")[0] + "</refined>"
    
    while not follows_rules(removed_posts):
        print(f"model output {generated_text} was found invalid")
        generated_text = pipe(chat, max_new_tokens=2 ** 16)[0]['generated_text'][-1]['content']

    model_output = removed_posts.split("<refined>")[-1].replace("</refined>", "")
    
    return model_output

gr.ChatInterface(predict, theme="soft").launch()