chinese_full_paragraph_input_history = content_json["chinese_full_paragraph_input"]

app.py

@@ -1016,13 +1016,13 @@ def get_chinese_paragraph_practice_log_session_content(file_name):
         content = GCS_SERVICE.download_as_string("jutor_logs", file_name)
         print(f"content: {content}")
         content_json = json.loads(content)
-        chinese_full_paragraph_input_history = content_json["chinese_full_paragraph_input"]
+        chinese_full_paragraph_input_history = content_json["chinese_full_paragraph_input"] if "chinese_full_paragraph_input" in content_json else ""
         chinese_full_paragraph_evaluate_output_text_history = content_json["chinese_full_paragraph_evaluate_output_text"] if "chinese_full_paragraph_evaluate_output_text" in content_json else ""
-        chinese_full_paragraph_evaluate_output_table_history = pd.DataFrame(content_json["chinese_full_paragraph_evaluate_output_table"])
-        chinese_full_paragraph_refine_input_history = content_json["chinese_full_paragraph_refine_input"]
-        chinese_full_paragraph_refine_output_text_history = content_json["chinese_full_paragraph_refine_output_text"]
-        chinese_full_paragraph_refine_output_table_history = pd.DataFrame(content_json["chinese_full_paragraph_refine_output_table"])
-        chinese_full_paragraph_save_output_history = content_json["chinese_full_paragraph_save_output"]
+        chinese_full_paragraph_evaluate_output_table_history = pd.DataFrame(content_json["chinese_full_paragraph_evaluate_output_table"]) if "chinese_full_paragraph_evaluate_output_table" in content_json else pd.DataFrame()
+        chinese_full_paragraph_refine_input_history = content_json["chinese_full_paragraph_refine_input"] if "chinese_full_paragraph_refine_input" in content_json else ""
+        chinese_full_paragraph_refine_output_text_history = content_json["chinese_full_paragraph_refine_output_text"] if "chinese_full_paragraph_refine_output_text" in content_json else ""
+        chinese_full_paragraph_refine_output_table_history = pd.DataFrame(content_json["chinese_full_paragraph_refine_output_table"]) if "chinese_full_paragraph_refine_output_table" in content_json else pd.DataFrame()
+        chinese_full_paragraph_save_output_history = content_json["chinese_full_paragraph_save_output"] if "chinese_full_paragraph_save_output" in content_json else ""
 
         assignment_id = content_json["assignment_id"] if "assignment_id" in content_json else ""
         if assignment_id:
@@ -1123,8 +1123,17 @@ def assign_grade(subject_content, structure, diction, spelling_punctuation):
     # 預設為 🟡
     return "🟡"
 
-def get_chinese_paragraph_evaluate_content(model, user_content):
-    content = generate_content_by_open_ai_assistant(user_content, thread_id=None, model_name=model)
+def get_chinese_conversation_thread_id(thread_id):
+    if thread_id:
+        return thread_id
+    else:
+        client = CUTOR_OPEN_AI_CLIENT
+        thread = client.beta.threads.create()
+        thread_id = thread.id
+        return thread_id
+
+def get_chinese_paragraph_evaluate_content(thread_id, model, user_content):
+    content = generate_content_by_open_ai_assistant(user_content, thread_id, model_name=model)
     print(f"====generate_paragraph_evaluate====")
     print(content)
 
@@ -1158,7 +1167,7 @@ def get_chinese_paragraph_evaluate_content(model, user_content):
     return total_content_text, content_table
 
 
-def get_chinese_paragraph_1st_evaluate_content(model, sys_content, paragraph, user_generate_paragraph_evaluate_prompt):
+def get_chinese_paragraph_1st_evaluate_content(thread_id, model, sys_content, paragraph, user_generate_paragraph_evaluate_prompt):
     verify_string_length(paragraph)
     verify_moderation(paragraph)
 
@@ -1169,25 +1178,22 @@ def get_chinese_paragraph_1st_evaluate_content(model, sys_content, paragraph, us
         ---
         {user_generate_paragraph_evaluate_prompt}
     """
-    total_content_text, content_table = get_chinese_paragraph_evaluate_content(model, user_content)
+    total_content_text, content_table = get_chinese_paragraph_evaluate_content(thread_id, model, user_content)
     
     return total_content_text, content_table
 
-def get_chinese_paragraph_refine_evaluate_content(model, sys_content, paragraph_1, paragraph_2, user_refine_paragraph_prompt):
-    verify_string_length(paragraph_1)
+def get_chinese_paragraph_refine_evaluate_content(thread_id, model, sys_content, paragraph_2, user_refine_paragraph_prompt):
     verify_string_length(paragraph_2)
-    verify_moderation(paragraph_1 + paragraph_2)
+    verify_moderation(paragraph_2)
 
     user_content = f"""
         sys_content: {sys_content}
         ---
-        original paragraph is: {paragraph_1}
-        ---
         refined paragraph is: {paragraph_2}
         ---
         {user_refine_paragraph_prompt}        
     """
-    total_content_text, content_table = get_chinese_paragraph_evaluate_content(model, user_content)
+    total_content_text, content_table = get_chinese_paragraph_evaluate_content(thread_id, model, user_content)
     
     return total_content_text, content_table
 
@@ -1205,12 +1211,8 @@ def generate_content_by_open_ai_assistant(user_content, thread_id=None, model_na
     print(f"instructions: {assistant.instructions}")
 
     try:
-        if not thread_id:
-            thread = client.beta.threads.create()
-            thread_id = thread.id
-        else:
-            thread = client.beta.threads.retrieve(thread_id)
-
+        thread_id = get_chinese_conversation_thread_id(thread_id) if not thread_id else thread_id
+        thread = client.beta.threads.retrieve(thread_id)
         print(f"Thread ID: {thread.id}")
 
         # if metadata:
@@ -2921,12 +2923,15 @@ with gr.Blocks(theme=THEME, css=CSS) as demo:
                 js=english_logs_tab_button_js
             )
 
-
+    # 中文寫作練習
     with gr.Row(visible=False) as chinese_group:
         with gr.Column():
             with gr.Row() as page_title_chinese:
                 gr.Markdown("# 🍄 CUTOR 國文段落寫作練習")
             
+            with gr.Row(visible=True) as chinese_admin:
+                chinese_thread_id_state = gr.State()
+
             with gr.Row(visible=False) as chinese_assignment_row:
                 with gr.Column():
                     with gr.Row():
@@ -3025,55 +3030,33 @@ with gr.Blocks(theme=THEME, css=CSS) as demo:
                         with gr.Row():
                             chinese_full_paragraph_evaluate_output_table = gr.Dataframe(label="段落全文分析表格", wrap=True, column_widths=[20, 15, 65], interactive=False)
 
-                # JUTOR 段落批改與整體建議
-                # with gr.Row():
-                #     gr.Markdown("# JUTOR 段落批改與整體建議")
-                # with gr.Row():
-                #     gr.Markdown("## 修訂文法與拼字錯誤")
-                # with gr.Row():
-                #     with gr.Column():
-                #         chinese_full_paragraph_correct_grammatical_spelling_errors_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：")
-                #     with gr.Column():
-                #         generate_chinese_full_paragraph_correct_grammatical_spelling_errors_button = gr.Button("修訂文法與拼字錯誤", variant="primary")
-                #         chinese_full_paragraph_correct_grammatical_spelling_errors_output_table = gr.Dataframe(label="修訂文法與拼字錯誤", interactive=False, column_widths=[30, 30, 40])
-                #         revised_chinese_full_paragraph_output = gr.Textbox(label="Revised Paragraph", show_copy_button=True, visible=False)
-                #         gr.Markdown("## 修訂結果")
-                #         revised_chinese_full_paragraph_diff = gr.HTML()
-
-                # JUTOR 段落批改與整體建議
-                # with gr.Row():
-                #     gr.Markdown("## 段落改善建議")
-                # with gr.Row():
-                #     with gr.Column():
-                #         chinese_full_paragraph_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():
-                #         generate_chinese_full_paragraph_refine_button = gr.Button("段落改善建議", variant="primary")
-                #         chinese_full_paragraph_refine_output_table = gr.DataFrame(label="段落改善建議", wrap=True, interactive=False)
-                #         chinese_full_paragraph_refine_output = gr.HTML(label="修改建議", visible=False)
-                #         gr.Markdown("## 修改結果")
-                #         chinese_full_paragraph_refine_output_diff = gr.HTML()
-
                 # 修改文章
                 with gr.Row():
                     gr.Markdown("# 根據建議修改文章")
                 with gr.Row(visible=False) as chinese_full_paragraph_refine_params:
                     default_user_generate_chinese_full_paragraph_refine_evaluate_prompt =  """
                         # 請嚴格根據 instructions
-                        我給你兩篇文章，請進行比較跟批改，並給出建議。
-                        1. 先檢查是否是合理的作文或是段落，再進行評分
+                        # Rules: 
+                        1. 我給你兩篇文章，請進行比較跟批改，並給出建議，如果文章完全一樣，請給出回饋：「這兩篇文章內容完全一樣，無法進行評分。」，後續不用進行評分。給予 level X
+                        2. 先檢查是否是合理的作文或是段落，再進行評分
                         2. 請確保作文或段落的內容完整，並且符合中文語法
                         3. 如果是一篇亂打的文章請直接給予回饋：「這篇文章內容不完整，無法進行評分。」
                         4. 如果無法進行評分 評分標準與回饋的內容跟等級，則為 X
-                        
-                        # Output rules:
-                        1. 先給 整體評分、綜合回饋、評分標準與回饋、修改範例
+                        5. 針對修改後的���分標準與回饋根據「A+、A、A- 、B+、 B、 B-」等級來評分，最低為 B-
+
+                        # Restrictions:
+                        1. 不用給整體評分
+                        2. 不用改標點符號
+                        3. 評分標準的分數等級請使用「A+、A、A- 、B+、 B、 B-」等級，不可使用數字或是其他等級，像是「90分、80分、C、D」等等。
+                        4. 回傳的 output json 不需要有原文的評分，只需要有修改後的評分
+
+                        # Output format:
+                        1. 先給 綜合回饋、評分標準與回饋、修改範例
                         2. 再將評分標準與回饋的內容以JSON格式輸出，並且請使用繁體中文（ZH-TW）來評分段落並輸出，用 ```json ..... ``` 包裹:
                         3. please use Chinese language (ZH-TW) to evaluate the paragraph and output use JSON format:
+                        4. if the score is X, please still follow the format and give the key as level and value as 'X'. then give the explanation in Chinese
                         
                         EXAMPLE:
-                        # 整體評分： (🔴？🟡？🟢？) 
-                        if 評分與回饋 都是 X，則整體評分給予 🔴。 
-
                         # 綜合回饋（前後比較）
                         你的文章...............(寫出一段話，比較兩篇作文的差異)
 
@@ -3192,8 +3175,12 @@ with gr.Blocks(theme=THEME, css=CSS) as demo:
                     )
 
                 chinese_full_paragraph_evaluate_button.click(
+                    fn=get_chinese_conversation_thread_id,
+                    inputs=[chinese_thread_id_state],
+                    outputs=[chinese_thread_id_state]
+                ).then(
                     fn=get_chinese_paragraph_1st_evaluate_content,
-                    inputs=[model, chinese_full_paragraph_sys_content_input, chinese_full_paragraph_input, user_generate_chinese_full_paragraph_evaluate_prompt],
+                    inputs=[chinese_thread_id_state, model, chinese_full_paragraph_sys_content_input, chinese_full_paragraph_input, user_generate_chinese_full_paragraph_evaluate_prompt],
                     outputs=[chinese_full_paragraph_evaluate_output_text, chinese_full_paragraph_evaluate_output_table]
                 ).then(
                     fn=duplicate_element,
@@ -3202,8 +3189,12 @@ with gr.Blocks(theme=THEME, css=CSS) as demo:
                 )
 
                 generate_chinese_full_paragraph_refine_button.click(
+                    fn=get_chinese_conversation_thread_id,
+                    inputs=[chinese_thread_id_state],
+                    outputs=[chinese_thread_id_state]
+                ).then(
                     fn=get_chinese_paragraph_refine_evaluate_content,
-                    inputs=[model, chinese_full_paragraph_sys_content_input, chinese_full_paragraph_input, chinese_full_paragraph_refine_input, user_generate_chinese_full_paragraph_refine_evaluate_prompt],
+                    inputs=[chinese_thread_id_state, model, chinese_full_paragraph_sys_content_input, chinese_full_paragraph_refine_input, user_generate_chinese_full_paragraph_refine_evaluate_prompt],
                     outputs=[chinese_full_paragraph_refine_output_text, chinese_full_paragraph_refine_output_table]
                 )
 
@@ -3235,8 +3226,8 @@ with gr.Blocks(theme=THEME, css=CSS) as demo:
                                     gr.Markdown("<span style='color:#4e80ee'>作業說明</span>")
                                     chinese_assignment_description_history_log = gr.Markdown()
 
-                            gr.Markdown("---")
-                            gr.Markdown("# 回傳作業內容")
+                                    gr.Markdown("---")
+                                    gr.Markdown("# 回傳作業內容")
 
                             with gr.Row():
                                 with gr.Column():