app.py

import os
os.system("pip install transformers")
os.system("pip install gradio")
os.system("pip install tensorflow")
os.system("pip install torch")
import gradio as gr

import tensorflow as tf
from transformers import GPT2LMHeadModel, GPT2Tokenizer, set_seed
tokenizer = GPT2Tokenizer.from_pretrained("gpt2-large")
model = GPT2LMHeadModel.from_pretrained("gpt2-large", pad_token_id=tokenizer.eos_token_id)


def generate(prompt,textCount=200):
    input_ids = tokenizer.encode(prompt, return_tensors='pt')
    if textCount == None or textCount < 200:
        textCount=200
    # generate text until the output length (which includes the context length) reaches 50
    output = model.generate(input_ids, max_length=textCount, num_beams=5, no_repeat_ngram_size=2, early_stopping=True)
    out = tokenizer.decode(output[0], skip_special_tokens=True)
    return out



demo = gr.Interface(
    fn=generate,
    inputs=[gr.Textbox(lines=8, placeholder="Paragraph Here..."),"number"],
    outputs="text",title="Text generation app with GPT2",
    description="This is a text generation app, it can prove useful when you want to generate texts. All you need to do is copy and paste a short prompt . ",
    examples=[
        ["During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in"
],["The bald eagle is"]
    ],
)
demo.launch()