import os
import urllib.request
import gradio as gr
from transformers import T5Tokenizer, T5ForConditionalGeneration
import huggingface_hub
import re
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import time
import transformers
import requests
import globals

def fetch_model(url, filename):
    if not os.path.isfile(filename):
        urllib.request.urlretrieve(url, filename)
        print("File downloaded successfully.")
    else:
        print("File already exists.")

def api_query(API_URL, headers, payload):
  response = requests.post(API_URL, headers=headers, json=payload)
  return response.json()

def post_process(model_output,input):
  start_pos = model_output.find(input)
  if start_pos != -1:
    answer = model_output[start_pos + len(input):].strip()
  else:
    answer = model_output
    print("'Literal meaning:' not found in the text.")
  answer.replace("\n", "")
  return answer