no message

app.py

@@ -1,5 +1,4 @@
 import os
-
 import gradio as gr
 import pytesseract
 
@@ -11,7 +10,6 @@ langs = []
 
 choices = os.popen('tesseract --list-langs').read().split('\n')[1:-1]
 
-
 # If you don't have tesseract executable in your PATH, include the following:
 # pytesseract.pytesseract.tesseract_cmd = r'<full_path_to_your_tesseract_executable>'
 # Example tesseract_cmd = r'C:\Program Files (x86)\Tesseract-OCR\tesseract'
@@ -35,7 +33,6 @@ choices = os.popen('tesseract --list-langs').read().split('\n')[1:-1]
 # 取得所有語言清單
 languages = os.popen('tesseract --list-langs').read().split('\n')[1:-1]
 
-
 print(' ======================================================== ')
 # print(' ###### choices:' + choices)
 # print(' ###### GET ENV - TESSDATA_PREFIX:' + os.getenv('TESSDATA_PREFIX'))
@@ -61,6 +58,9 @@ def preprocess_and_llm(image, validation_type, language):
 with gr.Blocks() as demo:
     with gr.Row():
         image_input = gr.Image(type="pil", label="上傳圖片")
+        preprocess_output = gr.Image(type="pil", label="預處理後的圖片")
+
+    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")
 
@@ -68,13 +68,13 @@ with gr.Blocks() as demo:
         ocr_button = gr.Button("使用 OCR")
         llm_button = gr.Button("使用 AI LLM")
 
-    with gr.Row():
-        preprocess_output = gr.Image(label="OCR 預處理圖片")
     with gr.Row():
         ocr_output = gr.JSON(label="OCR 解析結果")
         llm_output = gr.JSON(label="AI LLM 解析結果")
 
-    ocr_button.click(preprocess_and_ocr, inputs=[image_input, validation_type, language_dropdown], outputs=ocr_output)
-    llm_button.click(preprocess_and_llm, inputs=[image_input, validation_type, language_dropdown], outputs=llm_output)
+    ocr_button.click(preprocess_and_ocr, inputs=[image_input, validation_type, language_dropdown],
+                     outputs=[preprocess_output, ocr_output])
+    llm_button.click(preprocess_and_llm, inputs=[image_input, validation_type, language_dropdown],
+                     outputs=[preprocess_output, llm_output])
 
 demo.launch(share=False)