from transformers import pipeline
import gradio as gr
import cv2
from PIL import Image
from gradio.themes.utils import sizes
theme = gr.themes.Default(radius_size=sizes.radius_none).set(
    block_label_text_color = '#4D63FF',
    block_title_text_color = '#4D63FF',
    button_primary_text_color = '#4D63FF',
    button_primary_background_fill='#FFFFFF',
    button_primary_border_color='#4D63FF',
    button_primary_background_fill_hover='#EDEFFF',
)


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


def ocr(image):
    inp = Image.fromarray(image.astype('uint8'), 'RGB')
    text = image_to_text(inp)

    total_caption = "" 
    for caption in text:
        total_caption = total_caption + caption.get('generated_text')
        total_caption = total_caption + '\r\n'

    return total_caption 

demo = gr.Interface(fn=ocr,
                    inputs='image',
                    outputs='text',
                    theme = theme,
                    css = "footer {visibility: hidden}",
                    examples = ['soccer.jpg'])


if __name__ == "__main__":
    demo.queue(concurrency_count=10).launch(server_name = "0.0.0.0")