Update app.py

app.py

@@ -38,6 +38,15 @@ stage_analyzer_chain = LLMChain(
     verbose=False, 
     output_key="stage_number")
 
+user_response_prompt = load_prompt("./templates/user_response_prompt.json")
+llm = ChatOpenAI(model='gpt-3.5-turbo', temperature=0.0)
+user_response_chain = LLMChain(
+    llm=llm,
+    prompt=user_response_prompt, 
+    verbose=True, # 과정을 출력할지
+    output_key="user_responses"
+)
+
 df = pd.read_json('./data/unified_wine_data.json', encoding='utf-8', lines=True)
 
 loader =DataFrameLoader(data_frame=df, page_content_column='name')
@@ -167,7 +176,7 @@ conversation_stages_dict = {
     "1": "Start: Start the conversation by introducing yourself. Be polite and respectful while maintaining a professional tone of conversation.",
     "2": "Analyze: Identify the user's preferences in order to make wine recommendations. Ask questions to understand the preferences of your users in order to make wine recommendations. Ask only one question at a time. The wine database tool is not available here.",
     "3": "Recommendation: Recommend the right wine based on the user's preferences identified. Recommendations must be limited to wines in wine database, and you can use tools to do this.",
-    "4": "After recommendation: After making a wine recommendation, it asks if the user likes the wine you recommended, and if they do, it provides a link to it. Otherwise, it takes you back to the recommendation stage.",
+    "4": "After recommendation: After making a wine recommendation, it asks if the user likes the wine you recommended, and if they do, it provides a link and image to it. Otherwise, it takes you back to the recommendation stage.",
     "5": "Close: When you're done, say goodbye to the user.",
     "6": "Question and Answering: This is where you answer the user's questions. To answer user question, you can use the search tool or the wine database tool.",
     "7": "Not in the given steps: This step is for when none of the steps between 1 and 6 apply.",
@@ -242,7 +251,7 @@ agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, ve
 import gradio as gr
 
 # user_response, stage_history, conversation_history, pre_conversation_history = "", "", """""", """"""
- 
+
 stage_description = ""
 for key, value in conversation_stages_dict.items():
     stage_description += f"{key}.{value}\n"
@@ -261,12 +270,17 @@ with gr.Blocks(css='#chatbot .overflow-y-auto{height:750px}') as demo:
  
     chatbot = gr.Chatbot()
     msg = gr.Textbox(label='User input')
+    init_examples = [["이번 주에 친구들과 모임이 있는데, 훌륭한 와인 한 병을 추천해줄래?"], ["입문자에게 좋은 와인을 추천해줄래?"], ["보르도와 부르고뉴 와인의 차이점은 뭐야?"]]
+    user_response_examples = gr.Dataset(samples=init_examples, components=[msg])
     stage_history = gr.Textbox(value="stage history: ", interactive=False, label='stage history')
     submit_btn = gr.Button("전송")
-    clear_btn = gr.ClearButton([msg, chatbot, stage_history])
+    clear_btn = gr.ClearButton([msg, chatbot, stage_history, ])
     stage_info = gr.Textbox(value=stage_description, interactive=False, label='stage description')
 
-    def answer(user_response, chat_history, stage_history):
+    def load_example(example):
+        return example[0]
+
+    def answer(user_response, chat_history, stage_history, user_response_examples):
         chat_history = chat_history or []
         stage_history = stage_history or ""
         pre_conversation_history = ""
@@ -278,27 +292,21 @@ with gr.Blocks(css='#chatbot .overflow-y-auto{height:750px}') as demo:
         stage_number = stage_number[-1]
         stage_history += stage_number if stage_history == "stage history: " else ", " + stage_number
         response = agent_executor.run({'input':user_response, 'conversation_history': pre_conversation_history, 'stage_number': stage_number})
-        conversation_history += "이우선: " + response + "\n"
+        # conversation_history += "이우선: " + response + "\n"
         response = response.split('<END_OF_TURN>')[0]
-        print(stage_history)
-        print(conversation_history)
         chat_history.append((user_response, response))
+        user_response_examples = []
+        for user_response_example in user_response_chain.run({'conversation_history': conversation_history}).split('|'):
+            user_response_examples.append([user_response_example])
  
-        return "", chat_history, stage_history
-    
-    def user(user_message, history):
-        return gr.update(value="", interactive=False), history + [[user_message, None]]
-
-    # def clear(*args):
-    #     global conversation_history, pre_conversation_history, stage_history, answer_token
-    #     answer_token = ''
-    #     conversation_history, pre_conversation_history, stage_history = """""", """""", ""
-    def clear():
-        pass
+        return "", chat_history, stage_history, gr.Dataset.update(samples=user_response_examples)
 
-    clear_btn.click(fn=clear)
+    def clear(user_response_examples):
+        return gr.Dataset.update(samples=init_examples)
 
-    submit_btn.click(answer, [msg, chatbot, stage_history], [msg, chatbot, stage_history])
-    msg.submit(answer, [msg, chatbot, stage_history], [msg, chatbot, stage_history])
+    clear_btn.click(fn=clear, inputs=[user_response_examples], outputs=[user_response_examples])
+    user_response_examples.click(load_example, inputs=[user_response_examples], outputs=[msg])      
+    submit_btn.click(answer, [msg, chatbot, stage_history, user_response_examples], [msg, chatbot, stage_history, user_response_examples])
+    msg.submit(answer, [msg, chatbot, stage_history, user_response_examples], [msg, chatbot, stage_history, user_response_examples])
 
 demo.launch()