app.py

from transformers import pipeline
import gradio as gr

get_completion = pipeline("image-to-text",model="nlpconnect/vit-gpt2-image-captioning")

# def summarize(input):
#     output = get_completion(input)
#     return output[0]['generated_text']

def captioner(image):
    result = get_completion(image)
    return result[0]['generated_text']

gr.close_all()
demo = gr.Interface(fn=captioner,
                    inputs=[gr.Image(label="Upload image", type="pil")],
                    outputs=[gr.Textbox(label="Caption")],
                    title="Image Captioning!",
                    description="I'll be back... with captions!",
                    allow_flagging="never")

demo.launch()