app.py

# URL: https://huggingface.co/spaces/gradio/text_generation
# imports
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# loading the model
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")

# defining the core function
def generate(text):
    generation_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
    result = generation_pipeline(text)
    return result[0]["generated_text"]


# defining title, description and examples
title = "Text Generation with GPT-J-6B"
description = "This demo generates text using GPT-J 6B: a transformer model trained using Ben Wang's Mesh Transformer JAX."
examples = [
    ["The Moon's orbit around Earth has"],
    ["The smooth Borealis basin in the Northern Hemisphere covers 40%"],
]

# defining the interface
demo = gr.Interface(
    fn=generate,
    inputs=gr.inputs.Textbox(lines=5, label="Input Text"),
    outputs=gr.outputs.Textbox(label="Generated Text"),
    title=title,
    description=description,
    examples=examples,
)

# launching
demo.launch()