mojakob/usability

1import gradio as gr2import pandas as pd3import os4from urllib.parse import urlparse, parse_qs5 6# Function to process responses and store them7def process_feedback(ease, clarity, engagement, satisfaction, cognitive_load, comments, state, request: gr.Request):8    # Extract Prolific ID and Version from URL9    params = parse_qs(urlparse(request.url).query)10    prolific_id = params.get("PROLIFIC_PID", ["Unknown"])[0]11    version = params.get("VERSION", ["Unknown"])[0]12 13    # Prepare and save the data14    data = {15        "Prolific ID": prolific_id,16        "Assessment Version": version,17        "Ease of Use": ease,18        "Clarity of Instructions": clarity,19        "Engagement Level": engagement,20        "Satisfaction with Assessment": satisfaction,21        "Cognitive Load": cognitive_load,22        "Comments": comments23    }24    25    # Save to CSV26    file_name = "usability_feedback.csv"27    if not os.path.exists(file_name):28        pd.DataFrame([data]).to_csv(file_name, index=False)29    else:30        pd.DataFrame([data]).to_csv(file_name, mode="a", header=False, index=False)31 32    # Return confirmation and show the return button33    state["completed"] = True34    return (35        f"Thank you, participant {prolific_id} (Version {version})! Your feedback has been recorded.",36        gr.update(visible=True)37    )38 39# Function to redirect to Prolific40def redirect_to_prolific():41    return gr.update(js="window.location.href='https://app.prolific.com/submissions/complete?cc=CBQG85HO';")42 43# Gradio Interface44with gr.Blocks() as app:45    state = gr.State({"completed": False})46 47    # Instructions48    gr.Markdown("""49    Thank you for completing the assessment. Please answer the following questions on a scale from 1 (= "Not at all") to 10 (= "Very much").50    """)51 52    # Usability Questions (Mix of NASA-TLX and PSSUQ)53    ease = gr.Slider(label="How easy was it to complete the assessment?", minimum=1, maximum=10, step=1)54    clarity = gr.Slider(label="How clear were the instructions?", minimum=1, maximum=10, step=1)55    engagement = gr.Slider(label="How engaging was the assessment experience?", minimum=1, maximum=10, step=1)56    satisfaction = gr.Slider(label="How satisfied are you with this assessment?", minimum=1, maximum=10, step=1)57    cognitive_load = gr.Slider(label="How mentally demanding was the assessment?", minimum=1, maximum=10, step=1)58    comments = gr.Textbox(label="Additional Comments (Optional)", lines=4)59 60    # Submit button and output61    submit_button = gr.Button("Submit")62    output_message = gr.Markdown(visible=False)63    return_button = gr.Button("Return to Prolific", visible=False)64 65    # Submit Button Logic66    submit_button.click(67        fn=process_feedback,68        inputs=[ease, clarity, engagement, satisfaction, cognitive_load, comments, state],69        outputs=[output_message, return_button]70    )71 72    # Return to Prolific Logic73    return_button.click(74        fn=redirect_to_prolific,75        inputs=None,76        outputs=None77    )78 79# Launch the App80app.launch(share=True)