import gradio as gr
from gradio import mix
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from huggingface_hub import from_pretrained_keras

title = "Miniature"
description = "Gradio Demo for a miniature with GPT. To use it, simply add your text, or click one of the examples to load them. Read more at the links below."



tokenizer = AutoTokenizer.from_pretrained("aditi2222/automatic_title_generation")

model = from_pretrained_keras("keras-io/text-generation-miniature-gpt")
  

def tokenize_data(text):
    # Tokenize the review body
    input_ =  str(text) + ' </s>'
    max_len = 120
    # tokenize inputs
    tokenized_inputs = tokenizer(input_, padding='max_length', truncation=True, max_length=max_len, return_attention_mask=True, return_tensors='pt')

    inputs={"input_ids": tokenized_inputs['input_ids'],
        "attention_mask": tokenized_inputs['attention_mask']}
    return inputs

def generate_answers(text):
    inputs = tokenize_data(text)
    results= model.generate(input_ids= inputs['input_ids'], attention_mask=inputs['attention_mask'], do_sample=True,
                            max_length=120,
                            top_k=120,
                            top_p=0.98,
                            early_stopping=True,
                            num_return_sequences=1)
    answer = tokenizer.decode(results[0], skip_special_tokens=True)
    return answer

iface = gr.Interface(fn=generate_answers, inputs=['text'], outputs=["text"])
iface.launch(inline=False, share=True)