QL2023/ExplainerPlayground

1import os2import openai3import gradio as gr4 5OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")6 7############################8 9def generate_ai_text(prompt,temperature):10 11 query = prompt12    13 response = openai.ChatCompletion.create(14  model="gpt-4o",15  messages=[16    {17      "role": "system",18      "content": query19    },20    {21      "role": "user",22      "content": " "23    },24  ],25  temperature=temperature,26  max_tokens=3000,27  top_p=1,28  frequency_penalty=0,29  presence_penalty=030 )31 32 text = response["choices"][0]["message"]["content"]33 return text34 35def generate_prompt(risk_category,ini_prompt,no_login,low_activity,repeated_course,low_marks,WAM,forum_count,pre_fail,this_fail):36 37 body = ini_prompt38 39 data = f"The student is predicted to be '{risk_category}'\n Here are the indicators for the student:\n No Moodle Login: {no_login}\n Low Moodle Activity: {low_activity}\n Student is repeating this course: {repeated_course}\n Student has low marks: {low_marks}\n Student's WAM across all courses: {WAM}\n Student's forum activity: {forum_count}\n Count of student's previous fail of all courses: {pre_fail}\n Count of student's previous fail of this courses: {this_fail}\n "40 41 text = body + "\n\n" + data42    43 return text44    45############################46 47title = "Explainer v0.1"48 49with gr.Blocks(title=title) as demo:50    gr.Markdown("""51    # Explainer v0.252    Generate explanation. Select the conditions for a dummy student. Put your prompt in the text input window.53    """)54    with gr.Tab("Features"):55       with gr.Row():56           with gr.Column():57             risk_category = gr.Radio(["May need urgent support", "May fall behind","On track"],58             label="Prediction Status",59             value="May need urgent support",60             type="value"61             )62             no_login = gr.Radio(["Yes", "No"],63             label="No Moodle login in last 7 days",64             value="Yes",65             type="value"66             )67             low_activity = gr.Radio(["Yes", "No"],68             label="Low Moodle activity",69             value="Yes",70             type="value"71             )72             repeated_course = gr.Radio(["Yes", "No"],73             label="Repeated Course",74             value="No",75             type="value"76             )77             low_marks = gr.Radio(["Yes", "No"],78             label="Low marks",79             value="No",80             type="value"81             )82             WAM = gr.Radio(["Low","Medium", "High"],83             label="WAM",84             value="Low",85             type="value"86             )87             forum_count = gr.Radio(["Low","Medium", "High"],88             label="Moodle Forum Action",89             value="Low",90             type="value"91             )92             with gr.Row():93               pre_fail = gr.Slider(0, 10, value=0, step=1, label="Previous Fail (All Course)", info="Choose between 0 and 10")94             with gr.Row():95               this_fail = gr.Slider(0, 10, value=0, step=1, label="Previous Fail (This Course)", info="Choose between 0 and 10")96           with gr.Column():97             ini_prompt = gr.Textbox(label="initial prompt",98             info="Initial prompt",99             lines=15,100             value=" ",101             )102             gr.Examples(["A machine learning prediction model was developed to predict student's academic performance for a course. A list of data points will be given later, please analyze and explain the potential reasons for the student's success or failure. Be concise, reply under 200 words."], inputs=[ini_prompt])103           with gr.Column():104             revised_prompt = gr.Textbox(105             label="Revised Prompt",106             info="Revised Prompt",107             lines=25,108             value=" ")109             with gr.Row():110               temperature = gr.Slider(0, 2, value=0.5, step=0.1, label="LLM temperature", info="Choose between 0 and 2")111             prompt_button = gr.Button("Generate Revised Prompt")112           with gr.Column():113             genai_text = gr.Textbox(114             label="AI-generated Text",115             info="AI-generated Text",116             lines=30,117             value="Depending on message content, please wait up to 30s, please scroll if message is long",118             )119             text_ai_button = gr.Button("Generate Text")120 121    prompt_button.click(generate_prompt, inputs=[risk_category,ini_prompt,no_login,low_activity,repeated_course,low_marks,WAM,forum_count,pre_fail,this_fail], outputs=[revised_prompt])122    text_ai_button.click(generate_ai_text, inputs=[revised_prompt,temperature], outputs=[genai_text], api_name="gen")123      124 125demo.launch()