app.py

import matplotlib.pyplot as plt
import numpy as np
from PIL import Image, ImageFilter
import io
import time
import os
import copy
import pickle
import datetime
import urllib.request
import gradio as gr
import torch


from mmocr.apis import MMOCRInferencer
ocr = MMOCRInferencer(det='TextSnake', rec='ABINet_Vision')

url = (
    "https://upload.wikimedia.org/wikipedia/commons/thumb/5/5b/Draft_Marks_on_the_Bow_of_Kruzenshtern_Port_of_Tallinn_16_July_2011.jpg/1600px-Draft_Marks_on_the_Bow_of_Kruzenshtern_Port_of_Tallinn_16_July_2011.jpg"
)
path_input = "./example1.jpg"
urllib.request.urlretrieve(url, filename=path_input)

url = "https://upload.wikimedia.org/wikipedia/commons/3/3e/733_how-deep.jpg"
path_input = "./example2.jpg"
urllib.request.urlretrieve(url, filename=path_input)


path_img_output_folder = "./demo-out"
if not os.path.exists(path_img_output_folder):
    os.makedirs(path_img_output_folder)

path_img_input_folder = "./demo-input"
if not os.path.exists(path_img_input_folder):
    os.makedirs(path_img_input_folder)

def do_process(img):
    img_name = 'tmp.jpg'
    path_input = os.path.join(path_img_input_folder, img_name)
    path_output = os.path.join(path_img_output_folder, 'vis',img_name)
    img.save(path_input)
    img.save(path_output)
    # result = ocr(path_input, out_dir=path_img_output_folder, save_vis=True)
    img_res = Image(filename=path_output)
    return img_res

input_im = gr.inputs.Image(
    shape=None, image_mode="RGB", invert_colors=False, source="upload", type="pil"
)

output_img = gr.outputs.Image(label="Output of Integrated Gradients", type="pil")
# output_base = gr.outputs.Image(label="Baseline image", type="pil")
# output_label = gr.outputs.Label(label="Classification results", num_top_classes=3)

title = "Reading draught marks"
description = "Playground: Reading draught marks using pre-trained models. Tools: MMOCR, Gradio."
examples = [["./example1.jpg"], ["./example2.jpg"]]
article = "<p style='text-align: center'><a href='https://github.com/mawady' target='_blank'>By Dr. Mohamed Elawady</a></p>"
iface = gr.Interface(
    fn=do_process,
    inputs=[input_im],
    outputs=[output_img],
    live=False,
    interpretation=None,
    title=title,
    description=description,
    article=article,
    examples=examples,
)

iface.launch(debug=True)