import os import gradio as gr from Plan.AiLLM import llm_recognition from Plan.pytesseractOCR import ocr_recognition from Preprocess.preprocessImg import preprocess_image001, preprocess_image002 # 取得所有語言清單 languages = os.popen('tesseract --list-langs').read().split('\n')[1:-1] def preprocess_and_ocr(image, valid_type, language): # 方案一 pre_img_001 = preprocess_image001(image) ocr_result_001 = ocr_recognition(pre_img_001, valid_type, language) # 方案二 pre_img_002 = preprocess_image002(image) ocr_result_002 = ocr_recognition(pre_img_002, valid_type, language) return pre_img_001, pre_img_002, ocr_result_001, ocr_result_002 def preprocess_and_llm(image, valid_type, language): # 方案一 pre_img_001 = preprocess_image001(image) llm_result_001 = llm_recognition(pre_img_001, valid_type, language) # 方案二 pre_img_002 = preprocess_image002(image) llm_result_002 = llm_recognition(pre_img_002, valid_type, language) return pre_img_001, pre_img_002, llm_result_001, llm_result_002 with gr.Blocks() as demo: with gr.Row(): image_input = gr.Image(type="pil", label="上傳圖片") preprocess_output_001 = gr.Image(type="pil", label="預處理後的圖片-方案一") preprocess_output_002 = gr.Image(type="pil", label="預處理後的圖片-方案二") with gr.Row(): validation_type = gr.Dropdown(choices=["身分證正面", "身分證反面"], label="驗證類別") language_dropdown = gr.Dropdown(choices=languages, value="chi_tra", label="語言") # preprocessed_type = gr.Radio(["001", "002"], label="解析方案") with gr.Row(): ocr_button = gr.Button("使用 OCR") llm_button = gr.Button("使用 AI LLM") with gr.Row(): ocr_output_001 = gr.JSON(label="OCR-001-解析結果") ocr_output_002 = gr.JSON(label="OCR-002-解析結果") llm_output_001 = gr.JSON(label="AiLLM-001 解析結果") llm_output_002 = gr.JSON(label="AiLLM-002 解析結果") ocr_button.click(preprocess_and_ocr, inputs=[image_input, validation_type, language_dropdown], outputs=[preprocess_output_001, preprocess_output_002, ocr_output_001, ocr_output_002]) llm_button.click(preprocess_and_llm, inputs=[image_input, validation_type, language_dropdown], outputs=[preprocess_output_001, preprocess_output_002, llm_output_001, llm_output_002]) demo.launch(share=False)