app.py

import os
import subprocess
from datetime import datetime

import gradio as gr

from Plan.AiLLM import llm_recognition
from Plan.pytesseractJsOCR import pytesseractJs_recognition
from Plan.pytesseractOCR import ocr_recognition
from Preprocess.preprocessImg import PreprocessImg

# 切換到 node_app 目錄
os.chdir("node_app")
# 如果 node_modules 目錄不存在，則執行 npm install
if not os.path.exists("node_modules"):
    print(" ######################## NPM INSTALL ########################")
    subprocess.run(["npm", "install"])
# 切換回上級目錄
os.chdir("..")


# 取得所有語言清單
languages = os.popen('tesseract --list-langs').read().split('\n')[1:-1]


# 預處理圖片
def preprocess_image(image):
    if image is None:
        gr.Warning("尚未上傳圖片！")
        raise ValueError("尚未上傳圖片！")
    preprocessed_images = PreprocessImg(image)
    return (
        preprocessed_images,
        True,
        preprocessed_images[0],
        preprocessed_images[1],
        preprocessed_images[2],
        preprocessed_images[3],
        preprocessed_images[4]
    )


# pytesseract OCR
def Basic_ocr(valid_type, language, preprocessed_images, finish_pre_img):
    if not finish_pre_img:
        gr.Warning("請先執行圖像預處理，再進行分析！")
        raise ValueError("請先執行圖像預處理，再進行分析！")

    # 方案一
    ocr_result_001 = ocr_recognition(preprocessed_images[0], valid_type, language)
    # 方案二
    ocr_result_002 = ocr_recognition(preprocessed_images[1], valid_type, language)
    # 方案三
    ocr_result_003 = ocr_recognition(preprocessed_images[2], valid_type, language)
    # 方案四
    ocr_result_004 = ocr_recognition(preprocessed_images[3], valid_type, language)
    # 方案五
    ocr_result_005 = ocr_recognition(preprocessed_images[4], valid_type, language)

    return ocr_result_001, ocr_result_002, ocr_result_003, ocr_result_004, ocr_result_005


# AI LLM OCR
def AiLLM_ocr(valid_type, language, preprocessed_images, finish_pre_img):
    if not finish_pre_img:
        gr.Warning("請先執行圖像預處理，再進行分析！")
        raise ValueError("請先執行圖像預處理，再進行分析！")

    # 方案一
    llm_result_001 = llm_recognition(preprocessed_images[0], valid_type, language)
    # 方案二
    llm_result_002 = llm_recognition(preprocessed_images[1], valid_type, language)
    # 方案三
    llm_result_003 = llm_recognition(preprocessed_images[2], valid_type, language)
    # 方案四
    llm_result_004 = llm_recognition(preprocessed_images[3], valid_type, language)
    # 方案五
    llm_result_005 = llm_recognition(preprocessed_images[4], valid_type, language)

    return llm_result_001, llm_result_002, llm_result_003, llm_result_004, llm_result_005


def pytesseractJs_ocr(valid_type, language, preprocessed_images, finish_pre_img):
    if not finish_pre_img:
        gr.Warning("請先執行圖像預處理，再進行分析！")
        raise ValueError("請先執行圖像預處理，再進行分析！")

    temp_path = 'node_app/TempFile/' + datetime.now().strftime('%Y%m%d_%H%M%S') + '/'
    # 檢查目錄是否存在，如果不存在則建立
    if not os.path.exists(temp_path):
        os.makedirs(temp_path)

    image_files = []
    for i, image in enumerate(preprocessed_images):
        filename = temp_path + f'preprocessed_image_{i}.png'
        image.save(filename)
        image_files.append(filename)

    # 方案一
    file_name = 'out_pytesseractJs_result_1.txt'
    out_ocr_text_001 = pytesseractJs_recognition(valid_type, image_files[0], temp_path, file_name, language)

    # 方案二
    file_name = 'out_pytesseractJs_result_2.txt'
    out_ocr_text_002 = pytesseractJs_recognition(valid_type, image_files[1], temp_path, file_name, language)

    # file_name = 'out_pytesseractJs_result_2.txt'
    # 使用 subprocess 執行 JavaScript 代碼，傳遞語言參數
    # subprocess.run(['node', 'pytesseractJsOCR.js', image_files[1], language, temp_path + file_name], capture_output=True,
    #                text=True)
    # with open(temp_path + file_name, 'r') as file:
    #     out_ocr_text_002 = file.read()

    # 方案三
    file_name = 'out_pytesseractJs_result_3.txt'
    out_ocr_text_003 = pytesseractJs_recognition(valid_type, image_files[2], temp_path, file_name, language)

    # 方案四
    file_name = 'out_pytesseractJs_result_4.txt'
    out_ocr_text_004 = pytesseractJs_recognition(valid_type, image_files[3], temp_path, file_name, language)

    # 方案五
    file_name = 'out_pytesseractJs_result_5.txt'
    out_ocr_text_005 = pytesseractJs_recognition(valid_type, image_files[4], temp_path, file_name, language)

    return out_ocr_text_001, out_ocr_text_002, out_ocr_text_003, out_ocr_text_004, out_ocr_text_005


# VIEW
with gr.Blocks() as demo:
    with gr.Row():
        image_input = gr.Image(type="pil", label="上傳圖片")
        with gr.Column():
            validation_type = gr.Dropdown(choices=["全文分析", "身分證正面", "身分證反面"], value='全文分析',
                                          label="驗證類別")
            language_dropdown = gr.Dropdown(choices=languages, value="chi_tra", label="語言")

    with gr.Row():
        with gr.Column():
            preImg_button = gr.Button("圖片預先處理")
            gr.Markdown(
                "<div style='display: flex;justify-content: center;align-items: center;background-color: #ffdf00;font-weight: bold;text-decoration: underline;font-size: 20px;'>多模態預處理圖像</div>")

    with gr.Row():
        with gr.Column():
            ocr_button = gr.Button("使用 Pytesseract OCR 辨識")
            gr.Markdown(
                "<div style='display: flex;justify-content: center;align-items: center;background-color: #ffdf00;font-weight: bold;text-decoration: underline;font-size: 20px;'>Package: Google Pytesseract</div>")
        with gr.Column():
            llm_button = gr.Button("使用 AI LLM 模型辨識")
            gr.Markdown(
                "<div style='display: flex;justify-content: center;align-items: center;background-color: #ffdf00;font-weight: bold;text-decoration: underline;font-size: 20px;'>Package：Bert-base-chinese</div>")
        with gr.Column():
            pytesseractJS_button = gr.Button("使用 PytesseractJS 模型辨識")
            gr.Markdown(
                "<div style='display: flex;justify-content: center;align-items: center;background-color: #ffdf00;font-weight: bold;text-decoration: underline;font-size: 20px;'>Package：PytesseractJS</div>")

    with gr.Row():
        preprocess_output_001 = gr.Image(type="pil", label="預處理後的圖片-方案一")
        ocr_output_001 = gr.JSON(label="OCR-001-解析結果")
        llm_output_001 = gr.JSON(label="AiLLM-001-解析結果")
        pytesseractJS_output_001 = gr.JSON(label="PytesseractJS-001-解析結果")

    with gr.Row():
        preprocess_output_002 = gr.Image(type="pil", label="預處理後的圖片-方案二")
        ocr_output_002 = gr.JSON(label="OCR-002-解析結果")
        llm_output_002 = gr.JSON(label="AiLLM-002-解析結果")
        pytesseractJS_output_002 = gr.JSON(label="PytesseractJS-002-解析結果")

    with gr.Row():
        preprocess_output_003 = gr.Image(type="pil", label="預處理後的圖片-方案三")
        ocr_output_003 = gr.JSON(label="OCR-003-解析結果")
        llm_output_003 = gr.JSON(label="AiLLM-003-解析結果")
        pytesseractJS_output_003 = gr.JSON(label="PytesseractJS-003-解析結果")

    with gr.Row():
        preprocess_output_004 = gr.Image(type="pil", label="預處理後的圖片-方案四")
        ocr_output_004 = gr.JSON(label="OCR-004-解析結果")
        llm_output_004 = gr.JSON(label="AiLLM-004-解析結果")
        pytesseractJS_output_004 = gr.JSON(label="PytesseractJS-004-解析結果")

    with gr.Row():
        preprocess_output_005 = gr.Image(type="pil", label="預處理後的圖片-方案五")
        ocr_output_005 = gr.JSON(label="OCR-005-解析結果")
        llm_output_005 = gr.JSON(label="AiLLM-005-解析結果")
        pytesseractJS_output_005 = gr.JSON(label="PytesseractJS-005-解析結果")

    # 定義狀態
    finish_pre_img_state = gr.State(False)
    preprocessed_images_state = gr.State([])

    # 預先處理圖片 按鈕
    preImg_button.click(preprocess_image, inputs=[image_input],
                        outputs=[preprocessed_images_state, finish_pre_img_state,
                                 preprocess_output_001, preprocess_output_002,
                                 preprocess_output_003, preprocess_output_004,
                                 preprocess_output_005])
    # pytesseract 按鈕
    ocr_button.click(Basic_ocr, inputs=[validation_type, language_dropdown,
                                        preprocessed_images_state, finish_pre_img_state],
                     outputs=[ocr_output_001, ocr_output_002, ocr_output_003, ocr_output_004, ocr_output_005])
    # AI LLM 按鈕
    llm_button.click(AiLLM_ocr, inputs=[validation_type, language_dropdown,
                                        preprocessed_images_state, finish_pre_img_state],
                     outputs=[llm_output_001, llm_output_002, llm_output_003, llm_output_004, llm_output_005])

    # pytesseract 按鈕
    pytesseractJS_button.click(pytesseractJs_ocr, inputs=[validation_type, language_dropdown,
                                                          preprocessed_images_state, finish_pre_img_state],
                               outputs=[pytesseractJS_output_001, pytesseractJS_output_002, pytesseractJS_output_003,
                                        pytesseractJS_output_004, pytesseractJS_output_005])

demo.launch(share=False)