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import gradio as gr |
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from ontochat.functions import * |
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with gr.Blocks() as user_story_interface: |
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gr.Markdown( |
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""" |
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# OntoChat |
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Hello! I am OntoChat, your conversational ontology engineering assistant, to help you generate |
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user stories, elicit requirements, and extract and analyze competency questions. In ontology engineering, |
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a user story contains all the requirements from the perspective of an end user of the ontology. It is a way |
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of capturing what a user needs to achieve with the ontology while also providing context and value. This demo |
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will guide you step-by-step to create a user story and generate competency questions from it. Once you are |
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ready, start inputting your persona, objective (goal), and sample data and chat with the chatbot. Once you |
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find the generated user story satisfactory, please copy the generated user story and go to the next step ( |
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tab).""" |
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) |
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with gr.Group(): |
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api_key = gr.Textbox( |
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label="OpenAI API Key", |
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info="Please input your OpenAI API Key if you don't have it set up on your own machine. Please note that " |
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"the key will only be used for this demo and will not be uploaded or used anywhere else." |
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) |
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api_key_btn = gr.Button(value="Set API Key") |
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api_key_btn.click(fn=set_openai_api_key, inputs=api_key, outputs=api_key) |
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with gr.Row(): |
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with gr.Column(): |
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user_story_chatbot = gr.Chatbot([ |
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[None, "Hello! I am OntoChat, your conversational ontology engineering assistant. I will guide you step" |
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" by step in the creation of a user story. Let's start with the persona. What are the name, " |
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"occupations, skills, interests of the user?"], |
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]) |
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user_story_input = gr.Textbox( |
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label="Chatbot input", |
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placeholder="Please type your message here and press Enter to interact with the chatbot :)" |
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) |
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user_story = gr.TextArea( |
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label="User story", |
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interactive=True |
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) |
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user_story_input.submit( |
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fn=user_story_generator, |
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inputs=[ |
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user_story_input, user_story_chatbot |
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], |
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outputs=[ |
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user_story, user_story_chatbot, user_story_input |
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] |
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) |
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with gr.Blocks() as cq_interface: |
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gr.Markdown( |
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""" |
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# OntoChat |
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This is the second step of OntoChat. This functionality provides support for the extraction of competency |
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questions from a user story. Please, provide a user story to start extracting competency questions with the |
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chatbot, or simply load the example story below. |
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""" |
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) |
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with gr.Group(): |
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api_key = gr.Textbox( |
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label="OpenAI API Key", |
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placeholder="If you have set the key in other tabs, you don't have to set it again.", |
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info="Please input your OpenAI API Key if you don't have it set up on your own machine. Please note that " |
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"the key will only be used for this demo and will not be uploaded or used anywhere else." |
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) |
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api_key_btn = gr.Button(value="Set API Key") |
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api_key_btn.click(fn=set_openai_api_key, inputs=api_key, outputs=api_key) |
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with gr.Row(): |
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with gr.Column(): |
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cq_chatbot = gr.Chatbot([ |
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[None, "I am OntoChat, your conversational ontology engineering assistant. Here is the second step of " |
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"the system. Please give me your user story and tell me how many competency questions you want " |
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"me to generate from the user story."] |
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]) |
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cq_input = gr.Textbox( |
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label="Chatbot input", |
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placeholder="Please type your message here and press Enter to interact with the chatbot :)" |
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) |
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gr.Markdown( |
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""" |
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### User story examples |
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Click the button below to use an example user story from |
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[Linka](https://github.com/polifonia-project/stories/tree/main/Linka_Computer_Scientist) in Polifonia. |
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""" |
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) |
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example_btn = gr.Button(value="Use example user story") |
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example_btn.click( |
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fn=load_example_user_story, |
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inputs=[], |
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outputs=[cq_input] |
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) |
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cq_output = gr.TextArea( |
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label="Competency questions", |
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interactive=True |
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) |
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cq_input.submit( |
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fn=cq_generator, |
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inputs=[ |
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cq_input, cq_chatbot |
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], |
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outputs=[ |
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cq_output, cq_chatbot, cq_input |
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] |
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) |
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clustering_interface = gr.Interface( |
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fn=clustering_generator, |
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inputs=[ |
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gr.TextArea( |
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label="Competency questions", |
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info="Please copy the previously generated competency questions and paste it here. You can also modify " |
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"the questions before submitting them." |
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), |
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gr.Dropdown( |
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value="LLM clustering", |
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choices=["LLM clustering", "Agglomerative clustering"], |
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label="Clustering method", |
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info="Please select the clustering method." |
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), |
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gr.Textbox( |
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label="Number of clusters (optional for LLM clustering)", |
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info="Please input the number of clusters you want to generate. And please do not input a number that " |
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"exceeds the total number of competency questions." |
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) |
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], |
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outputs=[ |
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gr.Image(label="Visualization"), |
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gr.Code( |
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language='json', |
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label="Competency Question clusters" |
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) |
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], |
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title="OntoChat", |
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description="This is the third step of OntoChat. Please copy the generated competency questions from the previous " |
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"step and run the clustering algorithm to group the competency questions based on their topics. From " |
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"our experience, LLM clustering has the best performance.", |
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allow_flagging="never" |
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) |
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with gr.Blocks() as testing_interface: |
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gr.Markdown( |
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""" |
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# OntoChat |
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This is the final part of OntoChat which performs ontology testing based on the input ontology file and CQs. |
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""" |
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) |
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with gr.Group(): |
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api_key = gr.Textbox( |
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label="OpenAI API Key", |
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placeholder="If you have set the key in other tabs, you don't have to set it again.", |
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info="Please input your OpenAI API Key if you don't have it set up on your own machine. Please note that " |
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"the key will only be used for this demo and will not be uploaded or used anywhere else." |
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) |
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api_key_btn = gr.Button(value="Set API Key") |
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api_key_btn.click(fn=set_openai_api_key, inputs=api_key, outputs=api_key) |
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ontology_file = gr.File(label="Ontology file") |
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ontology_desc = gr.Textbox( |
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label="Ontology description", |
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placeholder="Please provide a description of the ontology uploaded to provide basic information and " |
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"additional context." |
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) |
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cq_testing_input = gr.Textbox( |
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label="Competency questions", |
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placeholder="Please provide the competency questions that you want to test with." |
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) |
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testing_btn = gr.Button(value="Test") |
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testing_output = gr.TextArea(label="Ontology testing output") |
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testing_btn.click( |
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fn=ontology_testing, |
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inputs=[ |
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ontology_file, ontology_desc, cq_testing_input |
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], |
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outputs=[ |
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testing_output |
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] |
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) |
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demo = gr.TabbedInterface( |
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[user_story_interface, cq_interface, clustering_interface, testing_interface], |
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["User Story Generation", "Competency Question Extraction", "Competency Question Analysis", "Ontology Testing"] |
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) |
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if __name__ == "__main__": |
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demo.launch() |
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