class EducationalMaterial:

app.py

@@ -1963,30 +1963,26 @@ def generate_ai_content(password, df_string, topic, grade, level, specific_featu
     verify_password(password)
     material = EducationalMaterial(df_string, topic, grade, level, specific_feature, content_type)
     prompt = material.generate_content_prompt()
-    user_content = material.build_user_content()
-    messages = material.build_messages(user_content)
-    ai_model_name = "gpt-4-turbo"
-    request_payload = {
-        "model": ai_model_name,
-        "messages": messages,
-        "max_tokens": 4000
-    }
-    ai_content = material.send_ai_request(OPEN_AI_CLIENT, request_payload)
+    try:
+        ai_content = material.get_ai_content(OPEN_AI_CLIENT, ai_type="openai")
+    except Exception as e:
+        error_msg = f" {video_id} OPEN AI 生成教學素材錯誤: {str(e)}"
+        print("===generate_ai_content error===")
+        print(error_msg)
+        print("===generate_ai_content error===")
+        ai_content = material.get_ai_content(BEDROCK_CLIENT, ai_type="bedrock")
+
     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)
     material = EducationalMaterial(df_string_output, "", "", "", "", "")
-    user_content = material.build_fine_tune_user_content(exam_result_prompt, exam_result, exam_result_fine_tune_prompt)
-    messages = material.build_messages(user_content)
-    ai_model_name = "gpt-4-turbo"
-    request_payload = {
-        "model": ai_model_name,
-        "messages": messages,
-        "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
+    try:
+        fine_tuned_ai_content = material.get_fine_tuned_ai_content(OPEN_AI_CLIENT, "openai", exam_result_prompt, exam_result, exam_result_fine_tune_prompt)
+    except:
+        fine_tuned_ai_content = material.get_fine_tuned_ai_content(BEDROCK_CLIENT, "bedrock", exam_result_prompt, exam_result, exam_result_fine_tune_prompt)
+
+    return fine_tuned_ai_content
 
 def return_original_exam_result(exam_result_original):
     return exam_result_original
@@ -2754,7 +2750,7 @@ with gr.Blocks(theme=gr.themes.Base(primary_hue=gr.themes.colors.orange, seconda
                         with gr.Row():
                             worksheet_content_type_name = gr.Textbox(value="worksheet", visible=False)
                             worksheet_algorithm = gr.Dropdown(label="選擇教學策略或理論", choices=["Bloom認知階層理論", "Polya數學解題法", "CRA教學法"], value="Bloom認知階層理論", visible=False)
-                            worksheet_content_btn = gr.Button("生成學習單 📄", variant="primary", visible=False)
+                            worksheet_content_btn = gr.Button("生成學習單 📄", variant="primary", visible=True)
                         with gr.Accordion("微調", open=False):
                             worksheet_exam_result_fine_tune_prompt = gr.Textbox(label="根據結果，輸入你想更改的想法")
                             worksheet_exam_result_fine_tune_btn = gr.Button("微調結果", variant="primary")
@@ -2775,7 +2771,7 @@ with gr.Blocks(theme=gr.themes.Base(primary_hue=gr.themes.colors.orange, seconda
                         with gr.Row():
                             lesson_plan_content_type_name = gr.Textbox(value="lesson_plan", visible=False)                    
                             lesson_plan_time = gr.Slider(label="選擇課程時間(分鐘)", minimum=10, maximum=120, step=5, value=40)
-                            lesson_plan_btn = gr.Button("生成教案 📕", variant="primary", visible=False)
+                            lesson_plan_btn = gr.Button("生成教案 📕", variant="primary", visible=True)
                         with gr.Accordion("微調", open=False):
                             lesson_plan_exam_result_fine_tune_prompt = gr.Textbox(label="根據結果，輸入你想更改的想法")
                             lesson_plan_exam_result_fine_tune_btn = gr.Button("微調結果", variant="primary")
@@ -2796,7 +2792,7 @@ with gr.Blocks(theme=gr.themes.Base(primary_hue=gr.themes.colors.orange, seconda
                         with gr.Row():
                             exit_ticket_content_type_name = gr.Textbox(value="exit_ticket", visible=False)
                             exit_ticket_time = gr.Slider(label="選擇出場券時間(分鐘)", minimum=5, maximum=10, step=1, value=8)
-                            exit_ticket_btn = gr.Button("生成出場券 🎟️", variant="primary", visible=False)
+                            exit_ticket_btn = gr.Button("生成出場券 🎟️", variant="primary", visible=True)
                         with gr.Accordion("微調", open=False):
                             exit_ticket_exam_result_fine_tune_prompt = gr.Textbox(label="根據結果，輸入你想更改的想法")
                             exit_ticket_exam_result_fine_tune_btn = gr.Button("微調結果", variant="primary")

educational_material.py

@@ -32,6 +32,72 @@ class EducationalMaterial:
         self.content_type = content_type  # 'worksheet' or 'lesson_plan'
         self.system_content = "你是一個擅長資料分析跟影片教學備課的老師，請精讀資料文本，自行判斷資料的種類，使用 zh-TW，遇到數學符號或是敘述請用 Latex 語法($...$)，例如：$x^2$。"
 
+    def get_ai_content(self, AI_Client ,ai_type="openai"):
+        system_content = self.system_content
+        user_content = self.build_user_content()
+        if ai_type.lower() == "openai":
+            return self.send_openai_request(AI_Client, system_content, user_content)
+        elif ai_type.lower() == "bedrock":
+            return self.send_bedrock_request(AI_Client, system_content, user_content)
+        else:
+            raise ValueError("Unsupported AI type. Please choose 'openai' or 'redrock'.")
+
+    def get_fine_tuned_ai_content(self, ai_client, ai_type, original_prompt, result, fine_tune_prompt):
+        system_content = self.system_content
+        user_content = self.build_fine_tune_user_content(original_prompt, result, fine_tune_prompt)
+        if ai_type.lower() == "openai":
+            return self.send_openai_request(ai_client, system_content, user_content)
+        elif ai_type.lower() == "bedrock":
+            return self.send_bedrock_request(ai_client, system_content, user_content)
+        else:
+            raise ValueError("Unsupported AI type. Please choose 'openai' or 'redrock'.")
+
+    def send_openai_request(self, AI_Client, system_content, user_content):
+        OPEN_AI_CLIENT = AI_Client
+        messages = [{"role": "system", "content": system_content}, {"role": "user", "content": user_content}]
+        request_payload = {
+            "model": "gpt-4-turbo",
+            "messages": messages,
+            "max_tokens": 512,
+            "temperature": 0.9,
+            "stream": False,
+        }
+        try:
+            response = OPEN_AI_CLIENT.chat.completions.create(**request_payload)
+            return response.choices[0].message.content.strip()
+        except Exception as e:
+            print(f"OpenAI failed: {e}")
+            raise  # Optionally re-raise the exception if fallback is not desired
+
+    def send_bedrock_request(self, AI_Client, system_content, user_content):
+        BEDROCK_CLIENT = AI_Client
+        #REDROCK
+        messages = [
+            {"role": "user", "content": user_content}
+        ]
+        model_id = "anthropic.claude-3-sonnet-20240229-v1:0"
+        # model_id = "anthropic.claude-3-haiku-20240307-v1:0"
+        kwargs = {
+            "modelId": model_id,
+            "contentType": "application/json",
+            "accept": "application/json",
+            "body": json.dumps({
+                "anthropic_version": "bedrock-2023-05-31",
+                "max_tokens": 4000,
+                "system": system_content,
+                "messages": messages
+            })
+        }
+        
+        try:
+            response = response = BEDROCK_CLIENT.invoke_model(**kwargs)
+            response_body = json.loads(response.get('body').read())
+            response_content = response_body.get('content')[0].get('text')
+            return response_content
+        except Exception as e:
+            print(f"Bedrock failed: {e}")
+            raise
+
     def _prepare_context(self, context):
         context_json = json.loads(context)
         processed_context = ""
@@ -420,14 +486,6 @@ class EducationalMaterial:
         """
         return exit_ticket_prompt
 
-    def create_ai_content(self, ai_client, request_payload):
-        user_content = self.build_user_content()
-        messages = self.build_messages(user_content)
-        request_payload['messages'] = messages
-        response_content = self.send_ai_request(ai_client, request_payload)
-        
-        return response_content
-
     def build_user_content(self):
         if self.content_type == 'worksheet':
             specific_feature_text = f"理論模型: {self.specific_feature}"