import gradio as gr
import os
import cv2
import numpy as np
from paddleocr import PPStructure,save_structure_res
from paddleocr.ppstructure.recovery.recovery_to_doc import sorted_layout_boxes, convert_info_docx

# Chinese image
# table_engine = PPStructure(recovery=True)

# English image
# table_engine = PPStructure(recovery=True, lang='en')

examples = ['0.png']

def find_layout(image, mode):
    save_folder = './output'
    # img = cv2.imread(image)
    img = np.array(image)

    if mode=="LayoutAnalysis":
        table_engine = PPStructure(table=False, ocr=False, show_log=True)
    elif mode=="TableRecognition":
        table_engine = PPStructure(layout=False, show_log=True)
    elif mode=="LayoutReccovery":
        table_engine = PPStructure(recovery=True)
    
    result = table_engine(img)
    save_structure_res(result, save_folder, os.path.basename("result").split('.')[0])

    if mode=="LayoutReccovery":
        h, w, _ = img.shape
        res = sorted_layout_boxes(result, w)
    
    final_text = []
    for line in result:
        line.pop('img')
        print(line)
        final_text.append(line)

    # convert_info_docx(img, res, save_folder, os.path.basename("result").split('.')[0])

    image = Image.open(img).convert('RGB')
    im_show = draw_structure_result(image, result)
    im_show = Image.fromarray(im_show)
    im_show.save('result.jpg')

    return final_text, im_show

iface = gr.Interface(fn=find_layout, inputs=[
        gr.Image(type="pil"),
        gr.CheckboxGroup(["LayoutAnalysis", "TableRecognition", "LayoutRecovery"], label="mode"),
    ], outputs=["text", "image"], examples=examples)
iface.launch()