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| import openai | |
| import gradio as gr | |
| STARTING_PROMPT = [{"role": "user", "content": """You are a math question generator. For each question, I will provide you with 4 things: | |
| 1. the main topic to be tested, 2. the types of question type, 3. the difficulty level, and 4. the required skillsets to solve the question. | |
| You will then reply with appropriate math question as well as the step by step solution for the question. Reply in Four parts. | |
| 1. Question Information: | |
| Topic(s) Tested: ... | |
| Question Type: ... | |
| Difficulty Level: ... | |
| Skills required: ... | |
| Case Study: True/False | |
| 2. Question: .... | |
| 3. Step by Step Solution: ... | |
| 4. Final answer(s): ..."""}, | |
| {"role": "assistant", "content": f"OK"}] | |
| API_KEY = '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' | |
| openai.api_key = API_KEY | |
| def predict(input, msg_history=STARTING_PROMPT): | |
| msg_history.append({"role": "user", "content": f"{input}"}) | |
| print(msg_history) | |
| completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=msg_history, temperature=0.8) | |
| response = completion.choices[0].message.content | |
| msg_history.append({"role": "assistant", "content": f"{response}"}) | |
| return [response, msg_history] | |
| def prompt_builder_predict(questionType=None, difficulty=0, topic=None, prerequisites=None, caseStudy=False, additionalPrompt=None, msg_history=STARTING_PROMPT): | |
| level = ['Very Easy', 'Easy', 'Medium', 'Difficult', 'Extremely Difficult'] | |
| prompt = 'randomly generatate a math question ' | |
| if topic: | |
| prompt = prompt + f'on the topic of {topic}. ' | |
| if difficulty: | |
| prompt = prompt + f'The difficulty level of the question should be: {level[difficulty-1]}, which means that it must require at least {difficulty} steps to solve. ' | |
| if questionType: | |
| prompt = prompt + f'The question type should be in {questionType} format. ' | |
| if prerequisites: | |
| prompt = prompt + f"This question will require to use the following methods to solve: {' and '.join(prerequisites)}. " | |
| if caseStudy: | |
| prompt = prompt + 'This question must be in the form of case study where it tries to test the application of the topic in the real life scenario. ' | |
| if additionalPrompt: | |
| prompt = prompt + f"In addition, {additionalPrompt}." | |
| return predict(prompt, msg_history) | |
| with gr.Blocks() as demo: | |
| msg_history = gr.State(STARTING_PROMPT) | |
| gr.Markdown( | |
| """ | |
| # Math Question Generator | |
| This webapp demostrates an API plugin that can be used with LearningANTs to generate questions. The response will contain three parts: [Question, Step by Step Solution, Final answer]. | |
| """) | |
| with gr.Row(): | |
| questionType = gr.Radio(["MCQ", "True or False", "Short Response"], value='Short Response', label="Question Type") | |
| difficulty = gr.Slider(1, 5, value=3, step=1, label="Difficult Level", info="Choose between 1 and 5") | |
| with gr.Row(): | |
| topic = gr.Dropdown(["Simultaneous Equation", "Linear Equation", "Derivatives", "Integrals", "Optimization"], value='Simultaneous Equation', label="Main Testing Topic") | |
| prerequisites = gr.Dropdown(["Elimination", "Subsitution", "Linear Equation", "Algebra", "Geometry", "Trigonometry", "Logarithms", "Power Rule", "Sum Rule", 'Difference Rule', "Product Rule", "Quotient Rule", 'Reciprocal Rule', "Chain Rule", "Implicit Differentiation", "Logarithmic Differentiation"], multiselect=True, interactive=True, label="Prerequisite Topics") | |
| caseStudy = gr.Checkbox(label="Case Study", info="Does this question test the application of theory in real life scenarios?") | |
| additionalInfo = gr.Textbox(label="Additional information (prompt)", placeholder="Give a scenario where Jim and John are working in a garden....") | |
| gen_btn = gr.Button("Generate A New Question") | |
| with gr.Row(): | |
| question = gr.TextArea(label="Generated Question") | |
| gen_btn.click(fn=prompt_builder_predict, inputs = [questionType, difficulty, topic, prerequisites, caseStudy, additionalInfo, msg_history], outputs= [question, msg_history]) | |
| with gr.Row(): | |
| prompt = gr.Textbox(label='Additional Prompt', info='Not satified with the result? Enter instructions to modify the question.', placeholder='Include the case study of....', visible=False) | |
| with gr.Row(): | |
| modify_btn = gr.Button('Modify Question', visible=False) | |
| modify_btn.click(fn=predict, inputs = [prompt, msg_history], outputs= [question, msg_history]) | |
| # restart_btn = gr.Button("Generate Another Question", visible=False) | |
| def show_display(): | |
| return gr.update(visible=True) | |
| def hide_display(): | |
| return gr.update(visible=False) | |
| def clear_value(): | |
| return gr.update(value='') | |
| question.change(fn=show_display, outputs=prompt) | |
| question.change(fn=show_display, outputs=modify_btn) | |
| demo.launch( share=False) |