Update app.py

app.py

@@ -35,33 +35,33 @@ with gr.Blocks() as user_story_interface:
     with gr.Row():
         with gr.Column(scale=1):
             user_story_chatbot = gr.Chatbot(
-                [
-                    [None, "Hi there! I'm OntoChat, your go-to assistant for building ontologies. 😊\n"
-                            "I'll be helping you create a user story by asking a few questions and answering anything you're curious about along the way.\n"
-                            "Let's get started! Can you share a bit about what this ontology is for?"
-                    ],
+                value=[
+                    {"role": "assistant", "content": ( 
+                        "Hello! I'm OntoChat, your trusted assistant for creating an ontology user story. Here's how I can assist you: \n\n1. I'll guide you through a sequence of questions, and you can use the provided prompt templates to craft your answers. \n\n2. You're welcome to share comments on my responses, and I'll refine them based on your feedback to ensure continuous improvement. 😊\n\nLet's get started! Could you tell me a little about the purpose of this ontology?"
+                    )}
                 ],
-                height="572px"
+                height="472px",
+                type="messages"
             )
             user_story_input = gr.Textbox(
                 label="Message OntoChat",
                 placeholder="Please type your message here and press Shift + Enter to interact with the chatbot:",
-                max_lines=2,
-                lines = 2
+                max_lines=4,
+                lines = 4
             )
             elicitation_questions_dataset = gr.Dataset(
                 components=[user_story_input],
-                label="Suggestion",
+                label="Prompt Templates",
                 type="index",
                 samples=[
-                    ["Refine Persona"],
-                    ["Refine User Goal"],
-                    ["Refine Actions"],
-                    ["Refine Keywords"],
-                    ["Refine Current Methods"],
-                    ["Refine Challenges"],
-                    ["Refine New Methods"],
-                    ["Refine Outcomes"]
+                    ["Create Persona"],
+                    ["Create User Goal"],
+                    ["Create Actions"],
+                    ["Create Keywords"],
+                    ["Create Current Methods"],
+                    ["Create Challenges"],
+                    ["Create New Methods"],
+                    ["Create Outcomes"]
                 ],
                 samples_per_page = 10
             )
@@ -80,11 +80,19 @@ with gr.Blocks() as user_story_interface:
 with gr.Blocks() as cq_interface:
     with gr.Row():
         with gr.Column():
-            cq_chatbot = gr.Chatbot([
-                [None, "I am OntoChat, your conversational ontology engineering assistant. Here is the second step of "
-                       "the system. Please give me your user story and tell me how many competency questions you want "
-                       "me to generate from the user story."]
-            ])
+            cq_chatbot = gr.Chatbot(
+                value=[
+                    {
+                        "role": "assistant",
+                        "content": (
+                            "I am OntoChat, your conversational ontology engineering assistant. Here is the second step of "
+                            "the system. Please give me your user story and tell me how many competency questions you want "
+                            "me to generate from the user story."
+                        )
+                    }
+                ],
+                type="messages" 
+            )
             cq_input = gr.Textbox(
                 label="Chatbot input",
                 placeholder="Please type your message here and press Enter to interact with the chatbot:"
@@ -147,7 +155,7 @@ clustering_interface = gr.Interface(
     description="This is the third step of OntoChat. Please copy the generated competency questions from the previous "
                 "step and run the clustering algorithm to group the competency questions based on their topics. From "
                 "our experience, LLM clustering has the best performance.",
-    allow_flagging="never"
+    flagging_mode="never"
 )
 
 with gr.Blocks() as testing_interface:
@@ -196,4 +204,4 @@ demo = gr.TabbedInterface(
 )
 
 if __name__ == "__main__":
-    demo.launch(share=True)
+    demo.launch(share=True)