#import pipeline function from huggingface transformers  and gradio 
from transformers import pipeline
import gradio as gr

#choosing the model distilbart for text-summarization (the default will be the same)
get_completion = pipeline(
                 "image-to-text",model="Salesforce/blip-image-captioning-base"
                 )

def captioner(input_image):
    result = get_completion(input_image)
    return result[0]["generated_text"]


#create an interface for the model
#with gr.Interface(summarize, "textbox", "text") as interface:
    #interface.launch()

demo = gr.Interface(captioner, 
                  inputs = [gr.Image(label="Upload any image here", type="pil")], #customize the UI with gradio components textbox, and size of text fields
                  outputs = [gr.Textbox(label="Caption")],#customize the UI with gradio components textbox,and size of text fields
                  title = "Image Caption Generation App with BLIP",#add title for app
                  allow_flagging="never")



demo.launch()