def generate_refine_paragraph(model, max_tokens, sys_content, eng_level, paragraph):

app.py

@@ -920,10 +920,39 @@ def highlight_diff_texts(highlight_list, text):
 def update_paragraph_correct_grammatical_spelling_errors_input(paragraph):
     return paragraph
 
-def generate_refine_paragraph(model, sys_content, eng_level, paragraph, user_refine_paragraph_prompt):
+def generate_refine_paragraph(model, max_tokens, sys_content, eng_level, paragraph):
     """
     根据用户输入的段落，调用 LLM API 生成相关的段落改善建議。
     """
+    user_refine_paragraph_prompt = """
+        I need assistance with revising a paragraph. Please Refine the paragraph and immediately "Provide Explanations" for each suggestion you made. 
+        
+        Rules:
+        - Do not modify the sentence: topicSentence" 
+        - Make sure any revised vocabulary aligns with the eng_level. 
+        - When explaining, use Traditional Chinese (Taiwan, 繁體中文 zh-TW) for clarity.
+        - But others(Origin, Suggestion, revised_paragraph_v2) use English, that's very important.
+        
+        Guidelines for Length and Complexity: 
+        - Please keep explanations concise and straightforward
+        - if there are no problems, don't need to revise either no more suggestions to show in the revised paragraph.
+        
+        Restrictions:
+        - avoiding overly technical language.
+        - don't change the text's case in the original text.
+        
+        The response should strictly be in the below JSON format and nothing else:
+        
+        EXAMPLE:
+        {
+        "Suggestions and Explanations": [ 
+            { "origin": "#original_text_1", "suggestion": "#suggestion_1", "explanation": "#explanation_1(in_traditional_chinese zh-TW)" }, 
+            { "origin": "#original_text_2", "suggestion": "#suggestion_2", "explanation": "#explanation_2(in_traditional_chinese zh-TW)" }, 
+        ... ],
+        "Revised Paragraph": "#revised_paragraph_v2"
+        }  
+    """
+
     try:
         user_content = f"""
             eng_level is: {eng_level}
@@ -951,7 +980,7 @@ def generate_refine_paragraph(model, sys_content, eng_level, paragraph, user_ref
             prompt=f"{sys_content}\n{user_content}" if "gemini" in model.lower() else None,
             messages=messages,
             model=model,
-            max_tokens=4000,
+            max_tokens=max_tokens,
             response_format={"type": "json_object"}
         )
 
@@ -3006,37 +3035,6 @@ with gr.Blocks(theme=THEME, css=CSS) as demo:
                     # 段落改善建議
                     with gr.Row():
                         with gr.Column():
-                            with gr.Row() as paragraph_refine_params:
-                                default_user_refine_paragraph_prompt = """
-                                    I need assistance with revising a paragraph. Please Refine the paragraph and immediately "Provide Explanations" for each suggestion you made. 
-                                    
-                                    Rules:
-                                    - Do not modify the sentence: topicSentence" 
-                                    - Make sure any revised vocabulary aligns with the eng_level. 
-                                    - When explaining, use Traditional Chinese (Taiwan, 繁體中文 zh-TW) for clarity.
-                                    - But others(Origin, Suggestion, revised_paragraph_v2) use English, that's very important.
-                                    
-                                    Guidelines for Length and Complexity: 
-                                    - Please keep explanations concise and straightforward
-                                    - if there are no problems, don't need to revise either no more suggestions to show in the revised paragraph.
-                                    
-                                    Restrictions:
-                                    - avoiding overly technical language.
-                                    - don't change the text's case in the original text.
-                                    
-                                    The response should strictly be in the below JSON format and nothing else:
-                                    
-                                    EXAMPLE:
-                                    {
-                                    "Suggestions and Explanations": [ 
-                                        { "origin": "#original_text_1", "suggestion": "#suggestion_1", "explanation": "#explanation_1(in_traditional_chinese zh-TW)" }, 
-                                        { "origin": "#original_text_2", "suggestion": "#suggestion_2", "explanation": "#explanation_2(in_traditional_chinese zh-TW)" }, 
-                                    ... ],
-                                    "Revised Paragraph": "#revised_paragraph_v2"
-                                    }  
-                                """
-                                user_refine_paragraph_prompt = gr.Textbox(label="Refine Paragraph Prompt", value=default_user_refine_paragraph_prompt, visible=False)
-                            
                             with gr.Row() as paragraph_refine_html:
                                 gr.Markdown("# Step 8. 段落改善建議")
                                 with gr.Accordion("📝 參考指引：段落改善建議？", open=False ):
@@ -3111,10 +3109,10 @@ with gr.Blocks(theme=THEME, css=CSS) as demo:
                         fn=generate_refine_paragraph,
                         inputs=[
                             model,
+                            max_tokens,
                             sys_content_input,
                             eng_level_input,
                             paragraph_correct_grammatical_spelling_errors_input,
-                            user_refine_paragraph_prompt
                         ],
                         outputs=[refine_output_table, refine_output]
                     ).then(
@@ -3345,11 +3343,11 @@ with gr.Blocks(theme=THEME, css=CSS) as demo:
                     generate_full_paragraph_refine_button.click(
                         fn=generate_refine_paragraph,
                         inputs=[
-                            model, 
+                            model,
+                            max_tokens,
                             sys_content_input, 
                             eng_level_input, 
                             full_paragraph_refine_input, 
-                            user_refine_paragraph_prompt
                         ],
                         outputs=[full_paragraph_refine_output_table, full_paragraph_refine_output]
                     ).then(
@@ -3465,9 +3463,6 @@ with gr.Blocks(theme=THEME, css=CSS) as demo:
                                 with gr.Column():
                                     past_exam_refine_input = gr.TextArea(label="這是你的原始寫作內容，參考 JUTOR 的建議，你可以選擇是否修改：", show_copy_button=True)
                                 with gr.Column():
-                                    with gr.Row():
-                                        with gr.Accordion("prompt 提供微調測試", open=False, elem_classes=['accordion-prompts'], visible=False):
-                                            past_exam_refine_paragraph_prompt = gr.Textbox(label="Refine Paragraph Prompt", value=default_user_refine_paragraph_prompt, lines= 20)
                                     with gr.Row():
                                         past_exam_generate_refine_button = gr.Button("段落改善建議", variant="primary")
                                     with gr.Row():
@@ -3597,7 +3592,13 @@ with gr.Blocks(theme=THEME, css=CSS) as demo:
                                 
                                 past_exam_generate_refine_button.click(
                                     fn=generate_refine_paragraph,
-                                    inputs=[model, past_exam_evaluation_sys_content_prompt, eng_level_input, past_exam_refine_input, past_exam_refine_paragraph_prompt],
+                                    inputs=[
+                                        model,
+                                        max_tokens,
+                                        past_exam_evaluation_sys_content_prompt,
+                                        eng_level_input,
+                                        past_exam_refine_input,
+                                    ],
                                     outputs=[past_exam_refine_output_table, past_exam_refine_output]
                                 ).then(
                                     fn=highlight_diff_texts,