Update app.py

app.py

@@ -1,8 +1,5 @@
 import gradio as gr
-from ontochat.functions import set_openai_api_key, user_story_generator, cq_generator, load_example_user_story, clustering_generator, ontology_testing, get_preidentified_prompts, load_example, update_examples
-
-# Global variables to hold pre-identified prompts
-preidentified_prompts = get_preidentified_prompts()
+from ontochat.functions import set_openai_api_key, user_story_generator, cq_generator, load_example_user_story, clustering_generator, ontology_testing, load_example
 
 user_story_template = """**Persona:**\n\n- Name: -\n- Age: -\n- Occupation: -\n- Skills: -\n- Interests: -\n\n**Goal:**\n\n- Description: -\n- Keywords: -\n\n**Scenario:**\n\n- Before: -\n- During: -\n- After: -\n\n**Example Data:**\n\n- Category: -\n- Data: -\n\n**Resources:**\n\n- Resource Name: -\n- Link: -"""
 
@@ -57,10 +54,14 @@ with gr.Blocks() as user_story_interface:
                 label="Suggestion",
                 type="index",
                 samples=[
-                    ["Cultural Heritage Preservation"],
-                    ["Accessibility and Inclusion"],
-                    ["Multisensory Interactions"],
-                    ["Digital Rights and Preservation"]
+                    ["Refine Persona"],
+                    ["Refine User Goal"],
+                    ["Refine Actions"],
+                    ["Refine Keywords"],
+                    ["Refine Current Methods"],
+                    ["Refine Challenges"],
+                    ["Refine New Methods"],
+                    ["Refine Outcomes"]
                 ],
                 samples_per_page = 10
             )
@@ -68,11 +69,7 @@ with gr.Blocks() as user_story_interface:
     user_story_input.submit(
         fn=user_story_generator,
         inputs=[user_story_input, user_story_chatbot],
-        outputs=[user_story_chatbot, user_story_input, elicitation_questions_dataset]
-    ).then(
-        fn=update_examples,
-        inputs=None,
-        outputs=[elicitation_questions_dataset]
+        outputs=[user_story_chatbot, user_story_input]
     )
     elicitation_questions_dataset.click(
         fn=load_example,