from transformers import AutoTokenizer, AutoModelForCausalLM
from itertools import chain
import gradio as gr
import torch 

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(device)

tokenizer = AutoTokenizer.from_pretrained("uer/gpt2-chinese-cluecorpussmall")
model = AutoModelForCausalLM.from_pretrained("uer/gpt2-chinese-cluecorpussmall").to(device)

def generate_text(prompt,length=500):
  inputs = tokenizer(prompt,add_special_tokens=False, return_tensors="pt").to(device)

  txt = tokenizer.decode(model.generate(inputs["input_ids"],
                       max_length=length, 
                       num_beams=2, 
                       no_repeat_ngram_size=2,
                       early_stopping=True,
                       pad_token_id = 0
                      )[0])
  
  #Replace text 
  replacements = {
    '[': "",
    ']': "",
    'S': "",
    'E': "",
    'P': "",
    'U': "",
    'N': "",
    'K': ""
                }


  new_text = ''.join(chain.from_iterable(replacements.get(word, [word]) for word in txt))


  return new_text

with gr.Blocks() as web:
  gr.Markdown("<h1><center>Andrew Lim Chinese stories </center></h1>")
  gr.Markdown("""<h2><center>让人工智能讲故事:<br><br>
 <img src=https://images.unsplash.com/photo-1550450339-e7a4787a2074?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1252&q=80></center></h2>""")
  gr.Markdown("""<center>******</center>""")
  

  input_text = gr.Textbox(label="故事的开始", value="在空中飞翔", lines=6)  
  buton = gr.Button("Submit ")  
  output_text = gr.Textbox(lines=6, label="人工智能讲一个故事 :")
  buton.click(generate_text, inputs=[input_text], outputs=output_text)  
   
web.launch()