app.py

import gradio as gr
from transformers import pipeline
# from transformers import AutoTokenizer, AutoModelForCausalLM

# tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path="/root/.cache/huggingface/hub/models")
# model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path="/root/.cache/huggingface/hub/models")
# generator = pipeline('text-generation', model=model, tokenizer=tokenizer)

generator = pipeline('text-generation', model='gpt2')

def generate(text):
    result = generator(text, max_length=30, num_return_sequences=1)
    return result[0]["generated_text"]

examples = [
    ["The Moon's orbit around Earth has"],
    ["The smooth Borealis basin in the Northern Hemisphere covers 40%"],
]

demo = gr.Interface(
    fn=generate,
    inputs=gr.inputs.Textbox(lines=5, label="Input Text"),
    outputs=gr.outputs.Textbox(label="Generated Text"),
    examples=examples
)

demo.launch(server_name="0.0.0.0", server_port=7860)