app.py

"""import gradio as gr
from transformers import pipeline

pipeline = pipeline(task="text-generation", model="Preetham04/text-generation")

def predict(input_img):
    predictions = pipeline(input_img)
    return  {p["title"] for p in predictions}

gradio_app = gr.Interface(
    predict,
    inputs="textbox", 
    outputs="text",
    title="Text-generation",
)

if __name__ == "__main__":
    gradio_app.launch(share=True)
"""

"""
import gradio as gr

from transformers import pipeline

pipe = pipeline("text-generation", model="Preetham04/text-generation")


def generate(text):
    return pipe(text)[0]["title"]


with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            find = gr.Textbox(label="input text")
            search_btn = gr.Button(value="SEARCH")
        with gr.Column():
            found = gr.Textbox(label="Related searches")

    search_btn.click(generate, inputs=find, outputs=found)
    examples = gr.Examples(examples=["SDE", "UX"],
                           inputs=[find])

demo.launch()    
"""

import gradio as gr

description = "Story generation with GPT-2"
title = "Generate your own story"
examples = [["Adventurer is approached by a mysterious stranger in the tavern for a new quest."]]

interface = gr.Interface.load("huggingface/pranavpsv/gpt2-genre-story-generator",
            description=description,
            examples=examples
)

interface.launch()