app.py

import gradio as gr 
from transformers import AutoTokenizer, TFGPT2LMHeadModel

review_model = TFGPT2LMHeadModel.from_pretrained("kmkarakaya/turkishReviews-ds")
review_tokenizer = AutoTokenizer.from_pretrained("kmkarakaya/turkishReviews-ds")

def generate_review(prompt):
  input_ids = review_tokenizer.encode(prompt, return_tensors='tf')
  context_length = 40
  output = review_model.generate(
      input_ids, 
      do_sample=True,
      max_length=context_length, 
      top_k=10,
      no_repeat_ngram_size=2, 
      early_stopping=True
  )
  return(review_tokenizer.decode(output[0], skip_special_tokens=True))
  
  

title="Turkish Review Generator: A GPT2 based Text Generator Trained with a Custom Dataset"
description= """Generate a review in Turkish by providing a prompt. 
Generation takes 15-20 seconds on average."""
article = """<p style='text-align: center'>On YouTube:</p>
            <p style='text-align: center'><a href='https://youtube.com/playlist?list=PLQflnv_s49v9d9w-L0S8XUXXdNks7vPBL' target='_blank'>How to Train a Hugging Face Causal Language Model from Scratch with a Custom Dataset and a Custom Tokenizer?</a></p> 
            <p style='text-align: center'><a href='https://youtube.com/playlist?list=PLQflnv_s49v8aajw6m9MRNbAAbL63flKD' target='_blank'>Hugging Face kütüphanesini kullanarak bir GPT2 Transformer Dil Modelini Kendi Veri Setimizle nasıl eğitip kullanabiliriz? (in Turkish)</a></p>
            <p style='text-align: center'>On Medium:</p>
            <p style='text-align: center'><a href='https://medium.com/deep-learning-with-keras/how-to-train-a-hugging-face-causal-language-model-from-scratch-8d08d038168f' target='_blank'>How to Train a Hugging Face Causal Language Model from Scratch with a Custom Dataset and a Custom Tokenizer?</a></p>"""                    
examples=["Bir hafta önce aldığım cep telefonu",
          "Tatil için rezervasyon yaptırdım.",
          "Henüz alalı bir",
          "Spor salonuna 1 yıllık abone oldum ama"] 

demo = gr.Interface(fn=generate_review, 
                    inputs="text", 
                    outputs="text",
                    examples=examples,
                    title=title,
                    description= description,
                    article = article
                    )
demo.launch(`share=True`)