#import tensorflow as tf
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import gradio as gr

def generate_text(sentence,len):
    tokenizer = GPT2Tokenizer.from_pretrained("gpt2-large")
    model = GPT2LMHeadModel.from_pretrained("gpt2-large", pad_token_id=tokenizer.eos_token_id)
    input_ids = tokenizer.encode(sentence, return_tensors='pt')
    output = model.generate(input_ids, max_length=len, num_beams=5, no_repeat_ngram_size=2, early_stopping=True)
    ans=tokenizer.decode(output[0], skip_special_tokens=True)
    return ans


#sentence = 'YouTube Title: AI learns to'

#input_ids = tokenizer.encode(sentence, return_tensors='pt')

iface = gr.Interface(
    fn=generate_text,  # Replace with your actual function
    inputs=[
        gr.inputs.Textbox(lines=2, placeholder="Enter text here..."),
        gr.inputs.Slider(minimum=10, maximum=1000, step=10, default=100, label="Max length of output")
    ],
    outputs=gr.outputs.Textbox(),
    title="Blog Post Generation App",
    description="Enter some text and see the generated output.",
)

iface.launch()