from transformers import pipeline
import gradio as gr
# import io
# import IPython.display
# from PIL import Image
# import base64

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 image_to_base64_str(pil_image):
#     byte_arr = io.BytesIO()
#     pil_image.save(byte_arr, format='PNG')
#     byte_arr = byte_arr.getvalue()
#     return str(base64.b64encode(byte_arr).decode('utf-8'))

def captioner(image):
    # base64_image = image_to_base64_str(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 with BLIP",
                    description="Caption any image using the BLIP model",
                    allow_flagging="never")

demo.launch()