def get_ai_content(password, video_id, df_string, topic, grade, level, specific_feature, content_type, source="gcs"):

app.py

@@ -906,37 +906,6 @@ def generate_summarise(df_string):
 
     return df_summarise
 
-def generate_questions(df_string):
-    # 使用 OpenAI 生成基于上传数据的问题
-
-    sys_content = "你是一個擅長資料分析跟影片教學的老師，user 為學生，請精讀資料文本，自行判斷資料的種類，並用既有資料為本質猜測用戶可能會問的問題，使用 zh-TW"
-    user_content = f"請根據 {df_string} 生成三個問題，並用 JSON 格式返回 questions:[q1的敘述text, q2的敘述text, q3的敘述text]"
-    messages = [
-        {"role": "system", "content": sys_content},
-        {"role": "user", "content": user_content}
-    ]
-    response_format = { "type": "json_object" }
-
-    print("=====messages=====")
-    print(messages)
-    print("=====messages=====")
-
-
-    request_payload = {
-        "model": "gpt-4-1106-preview",
-        "messages": messages,
-        "max_tokens": 4000,
-        "response_format": response_format
-    }
-
-    response = OPEN_AI_CLIENT.chat.completions.create(**request_payload)
-    questions = json.loads(response.choices[0].message.content)["questions"]
-    print("=====json_response=====")
-    print(questions)
-    print("=====json_response=====")
-
-    return questions
-
 def get_questions(video_id, df_string, source="gcs"):
     if source == "gcs":
         # 去 gcs 確認是有有 video_id_questions.json
@@ -989,6 +958,37 @@ def get_questions(video_id, df_string, source="gcs"):
     print("=====get_questions=====")
     return q1, q2, q3
 
+def generate_questions(df_string):
+    # 使用 OpenAI 生成基于上传数据的问题
+
+    sys_content = "你是一個擅長資料分析跟影片教學的老師，user 為學生，請精讀資料文本，自行判斷資料的種類，並用既有資料為本質猜測用戶可能會問的問題，使用 zh-TW"
+    user_content = f"請根據 {df_string} 生成三個問題，並用 JSON 格式返回 questions:[q1的敘述text, q2的敘述text, q3的敘述text]"
+    messages = [
+        {"role": "system", "content": sys_content},
+        {"role": "user", "content": user_content}
+    ]
+    response_format = { "type": "json_object" }
+
+    print("=====messages=====")
+    print(messages)
+    print("=====messages=====")
+
+
+    request_payload = {
+        "model": "gpt-4-1106-preview",
+        "messages": messages,
+        "max_tokens": 4000,
+        "response_format": response_format
+    }
+
+    response = OPEN_AI_CLIENT.chat.completions.create(**request_payload)
+    questions = json.loads(response.choices[0].message.content)["questions"]
+    print("=====json_response=====")
+    print(questions)
+    print("=====json_response=====")
+
+    return questions
+
 def change_questions(password, df_string):
     verify_password(password)
 
@@ -1005,7 +1005,63 @@ def change_questions(password, df_string):
 
 
 # AI 生成教學素材
-def on_generate_ai_content(password, df_string, topic, grade, level, specific_feature, content_type):
+def get_ai_content(password, video_id, df_string, topic, grade, level, specific_feature, content_type, source="gcs"):
+    verify_password(password)
+    if source == "gcs":
+        print("===get_ai_content on gcs===")
+        gcs_client = GCS_CLIENT
+        bucket_name = 'video_ai_assistant'
+        file_name = f'{video_id}_ai_content_list.json'
+        blob_name = f"{video_id}/{file_name}"
+        # 检查檔案是否存在
+        is_file_exists = GCS_SERVICE.check_file_exists(bucket_name, blob_name)
+        if not is_file_exists: 
+            # 先建立一個 ai_content_list.json
+            ai_content_list = []
+            ai_content_text = json.dumps(ai_content_list, ensure_ascii=False, indent=2)
+            upload_file_to_gcs_with_json_string(gcs_client, bucket_name, blob_name, ai_content_text)
+            print("ai_content_list [] 已上傳到GCS")
+
+        # 此時 ai_content_list 已存在
+        ai_content_list_string = download_blob_to_string(gcs_client, bucket_name, blob_name)
+        ai_content_list = json.loads(ai_content_list_string)
+        # by key 找到 ai_content （topic, grade, level, specific_feature, content_type）
+        target_kvs = {
+            "topic": topic,
+            "grade": grade,
+            "level": level,
+            "specific_feature": specific_feature,
+            "content_type": content_type
+        }
+        ai_content_json = [
+            item for item in ai_content_list
+            if all(item[k] == v for k, v in target_kvs.items())
+        ]         
+            
+        if len(ai_content_json) == 0:
+            ai_content, prompt = generate_ai_content(password, df_string, topic, grade, level, specific_feature, content_type)
+            ai_content_json = {
+                "content": str(ai_content),
+                "prompt": prompt,
+                "topic": topic,
+                "grade": grade,
+                "level": level,
+                "specific_feature": specific_feature,
+                "content_type": content_type
+            }
+
+            ai_content_list.append(ai_content_json)
+            ai_content_text = json.dumps(ai_content_list, ensure_ascii=False, indent=2)
+            upload_file_to_gcs_with_json_string(gcs_client, bucket_name, blob_name, ai_content_text)
+            print("ai_content已上傳到GCS")
+        else:
+            ai_content_json = ai_content_json[0]
+            ai_content = ai_content_json["content"]
+            prompt = ai_content_json["prompt"]
+
+    return ai_content, ai_content, prompt, prompt
+
+def generate_ai_content(password, df_string, topic, grade, level, specific_feature, content_type):
     verify_password(password)
     material = EducationalMaterial(df_string, topic, grade, level, specific_feature, content_type)
     prompt = material.generate_content_prompt()
@@ -1018,7 +1074,7 @@ def on_generate_ai_content(password, df_string, topic, grade, level, specific_fe
         "max_tokens": 4000  # 举例，实际上您可能需要更详细的配置
     }
     ai_content = material.send_ai_request(OPEN_AI_CLIENT, request_payload)
-    return ai_content, ai_content, prompt, prompt
+    return ai_content, prompt
 
 def generate_exam_fine_tune_result(password, exam_result_prompt , df_string_output, exam_result, exam_result_fine_tune_prompt):
     verify_password(password)
@@ -1850,18 +1906,18 @@ with gr.Blocks() as demo:
 
     # 教師版 學習單
     worksheet_content_btn.click(
-        on_generate_ai_content,
-        inputs=[password, df_string_output, content_topic, content_grade, content_level, worksheet_algorithm, worksheet_content_type_name],
+        get_ai_content,
+        inputs=[password, video_id, df_string_output, content_topic, content_grade, content_level, worksheet_algorithm, worksheet_content_type_name],
         outputs=[worksheet_exam_result_original, worksheet_exam_result, worksheet_prompt, worksheet_exam_result_prompt]
     )
     lesson_plan_btn.click(
-        on_generate_ai_content,
-        inputs=[password, df_string_output, content_topic, content_grade, content_level, lesson_plan_time, lesson_plan_content_type_name],
+        get_ai_content,
+        inputs=[password, video_id, df_string_output, content_topic, content_grade, content_level, lesson_plan_time, lesson_plan_content_type_name],
         outputs=[lesson_plan_exam_result_original, lesson_plan_exam_result, lesson_plan_prompt, lesson_plan_exam_result_prompt]
     )
     exit_ticket_btn.click(
-        on_generate_ai_content,
-        inputs=[password, df_string_output, content_topic, content_grade, content_level, exit_ticket_time, exit_ticket_content_type_name],
+        get_ai_content,
+        inputs=[password, video_id, df_string_output, content_topic, content_grade, content_level, exit_ticket_time, exit_ticket_content_type_name],
         outputs=[exit_ticket_exam_result_original, exit_ticket_exam_result, exit_ticket_prompt, exit_ticket_exam_result_prompt]
     )