| import gradio as gr | |
| from ontochat.functions import * | |
| with gr.Blocks() as user_story_interface: | |
| gr.Markdown( | |
| """ | |
| # OntoChat | |
| Hello! I am OntoChat, your conversational ontology engineering assistant, to help you generate | |
| user stories, elicit requirements, and extract and analyze competency questions. In ontology engineering, | |
| a user story contains all the requirements from the perspective of an end user of the ontology. It is a way | |
| of capturing what a user needs to achieve with the ontology while also providing context and value. This demo | |
| will guide you step-by-step to create a user story and generate competency questions from it. Once you are | |
| ready, start inputting your persona, objective (goal), and sample data and chat with the chatbot. Once you | |
| find the generated user story satisfactory, please copy the generated user story and go to the next step ( | |
| tab).""" | |
| ) | |
| with gr.Group(): | |
| api_key = gr.Textbox( | |
| label="OpenAI API Key", | |
| info="Please input your OpenAI API Key if you don't have it set up on your own machine. Please note that " | |
| "the key will only be used for this demo and will not be uploaded or used anywhere else." | |
| ) | |
| api_key_btn = gr.Button(value="Set API Key") | |
| api_key_btn.click(fn=set_openai_api_key, inputs=api_key, outputs=api_key) | |
| with gr.Row(): | |
| with gr.Column(): | |
| user_story_chatbot = gr.Chatbot([ | |
| [None, "Hello! I am OntoChat, your conversational ontology engineering assistant. I will guide you step" | |
| " by step in the creation of a user story. Let's start with the persona. What are the name, " | |
| "occupations, skills, interests of the user?"], | |
| ]) | |
| user_story_input = gr.Textbox( | |
| label="Chatbot input", | |
| placeholder="Please type your message here and press Enter to interact with the chatbot :)" | |
| ) | |
| user_story = gr.TextArea( | |
| label="User story", | |
| interactive=True | |
| ) | |
| user_story_input.submit( | |
| fn=user_story_generator, | |
| inputs=[ | |
| user_story_input, user_story_chatbot | |
| ], | |
| outputs=[ | |
| user_story, user_story_chatbot, user_story_input | |
| ] | |
| ) | |
| with gr.Blocks() as cq_interface: | |
| gr.Markdown( | |
| """ | |
| # OntoChat | |
| This is the second step of OntoChat. Please copy the generated user story from the previous | |
| step and use it here. You can also modify the user story before using it for generating competency questions. | |
| **Recommended prompt workflow:** | |
| 1. Obtain competency questions from the user story. | |
| - Zero-shot learning: | |
| - Prompt template: Given the user story: {user story}, generate {number} competency questions base on it. | |
| - Few-shot learning (i.e., provide examples to give more instructions on how to generate competency questions): | |
| - Prompt template: Here are some good examples of competency questions generated from example data. | |
| Formatted in {"Example data": "Competency questions"}. | |
| {"Yesterday was performed by Armando Rocca.": "Who performs the song?"}, | |
| {"The Church was built in 1619.": "When (what year) was the building built?"}, | |
| {"The Church is located in a periurban context.": "In which context is the building located?"}, | |
| {"The mounting system of the bells is the falling clapper.": "Which is the mounting system of the bell?"} | |
| 2. Clean and refine competency questions. | |
| - Obtain multiple competency questions. | |
| - Prompt template: Take the generated competency questions and check if any of them can be divided into | |
| multiple questions. If they do, split the competency question into multiple competency questions. If it | |
| does not, leave the competency question as it is. For example, the competency question "Who wrote The | |
| Hobbit and in what year was the book written?" must be split into two competency questions: "Who wrote | |
| the book?" and "In what year was the book written?". Another example is the competency question, "When | |
| was the person born?". This competency question cannot be divided into multiple questions. | |
| - Remove specific named entities. | |
| - Prompt template: Take the competency questions and check if they contain real-world entities, like | |
| "Freddy Mercury" or "1837". If they do, change those real-world entities from these competency questions | |
| to more general concepts. For example, the competency question "Which is the author of Harry Potter?" | |
| should be changed to "Which is the author of the book?". Similarly, the competency question "Who wrote | |
| the book in 2018?" should be changed to "Who wrote the book, and in what year was the book written?" | |
| """ | |
| ) | |
| with gr.Group(): | |
| api_key = gr.Textbox( | |
| label="OpenAI API Key", | |
| placeholder="If you have set the key in other tabs, you don't have to set it again.", | |
| info="Please input your OpenAI API Key if you don't have it set up on your own machine. Please note that " | |
| "the key will only be used for this demo and will not be uploaded or used anywhere else." | |
| ) | |
| api_key_btn = gr.Button(value="Set API Key") | |
| api_key_btn.click(fn=set_openai_api_key, inputs=api_key, outputs=api_key) | |
| 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."] | |
| ]) | |
| cq_input = gr.Textbox( | |
| label="Chatbot input", | |
| placeholder="Please type your message here and press Enter to interact with the chatbot :)" | |
| ) | |
| cq_output = gr.TextArea( | |
| label="Competency questions", | |
| interactive=True | |
| ) | |
| cq_input.submit( | |
| fn=cq_generator, | |
| inputs=[ | |
| cq_input, cq_chatbot | |
| ], | |
| outputs=[ | |
| cq_output, cq_chatbot, cq_input | |
| ] | |
| ) | |
| clustering_interface = gr.Interface( | |
| fn=clustering_generator, | |
| inputs=[ | |
| gr.TextArea( | |
| label="Competency questions", | |
| info="Please copy the previously generated competency questions and paste it here. You can also modify " | |
| "the questions before submitting them." | |
| ), | |
| gr.Dropdown( | |
| value="LLM clustering", | |
| choices=["LLM clustering", "Agglomerative clustering", "HDBSCAN"], | |
| label="Clustering method", | |
| info="Please select the clustering method." | |
| ), | |
| gr.Slider( | |
| minimum=2, | |
| maximum=50, | |
| step=1, | |
| label="Number of clusters", | |
| info="Please select the number of clusters you want to generate. Please note that for HDBSCAN, this value " | |
| "is used as the minimum size of a cluster. And please do not input a number that exceeds the total " | |
| "number of competency questions." | |
| ) | |
| ], | |
| outputs=[ | |
| gr.Image(label="Visualization"), | |
| gr.Code( | |
| language='json', | |
| label="Competency Question clusters" | |
| ) | |
| ], | |
| title="OntoChat", | |
| 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" | |
| ) | |
| with gr.Blocks() as testing_interface: | |
| gr.Markdown( | |
| """ | |
| # OntoChat | |
| This is the final part of OntoChat which performs ontology testing based on the input ontology file and CQs. | |
| """ | |
| ) | |
| with gr.Group(): | |
| api_key = gr.Textbox( | |
| label="OpenAI API Key", | |
| placeholder="If you have set the key in other tabs, you don't have to set it again.", | |
| info="Please input your OpenAI API Key if you don't have it set up on your own machine. Please note that " | |
| "the key will only be used for this demo and will not be uploaded or used anywhere else." | |
| ) | |
| api_key_btn = gr.Button(value="Set API Key") | |
| api_key_btn.click(fn=set_openai_api_key, inputs=api_key, outputs=api_key) | |
| ontology_file = gr.File(label="Ontology file") | |
| ontology_desc = gr.Textbox( | |
| label="Ontology description", | |
| placeholder="Please provide a description of the ontology uploaded to provide basic information and " | |
| "additional context." | |
| ) | |
| cq_testing_input = gr.Textbox( | |
| label="Competency questions", | |
| placeholder="Please provide the competency questions that you want to test with." | |
| ) | |
| testing_btn = gr.Button(value="Test") | |
| testing_output = gr.TextArea(label="Ontology testing output") | |
| testing_btn.click( | |
| fn=ontology_testing, | |
| inputs=[ | |
| ontology_file, ontology_desc, cq_testing_input | |
| ], | |
| outputs=[ | |
| testing_output | |
| ] | |
| ) | |
| demo = gr.TabbedInterface( | |
| [user_story_interface, cq_interface, clustering_interface, testing_interface], | |
| ["User Story Generation", "Competency Question Extraction", "Competency Question Analysis", "Ontology Testing"] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |