demo.py

import gradio as gr
import spaces


# TOKENIZER = 
# MINIMUM_TOKENS = 64

# def count_tokens(text):
#     return len(TOKENIZER(text).input_ids)


# Mock function for testing layout
def run_test_power(model_name, real_text, generated_text, N=10):
    return "Prediction: Human (Mocked)"



# Change mode name
# def change_mode(mode):
#   if mode == ""



css = """
#header { text-align: center; font-size: 1.5em; margin-bottom: 20px; }
#output-text { font-weight: bold; font-size: 1.2em; }
.links { 
    display: flex; 
    justify-content: flex-end; 
    gap: 10px; 
    margin-right: 10px; 
    align-items: center;
}
.separator {
    margin: 0 5px;
    color: black;
}
"""

# Gradio App
with gr.Blocks(css=css) as app:
    with gr.Row():
        gr.HTML('<div id="header">R-detect On HuggingFace</div>')
    with gr.Row():
        gr.HTML(
            """
            <div class="links">
                <a href="https://openreview.net/forum?id=z9j7wctoGV" target="_blank">Paper</a>
                <span class="separator">|</span>
                <a href="https://github.com/xLearn-AU/R-Detect" target="_blank">Code</a>
                <span class="separator">|</span>
                <a href="mailto:1730421718@qq.com" target="_blank">Contact</a>
            </div>
            """
        )
    with gr.Row():
        input_text = gr.Textbox(
            label="Input Text",
            placeholder="Enter Text Here",
            lines=8,
        )
    with gr.Row():
        model_name = gr.Dropdown(
            [
                "gpt2-medium",
                "gpt2-large",
                "t5-large",
                "t5-small",
                "roberta-base",
                "roberta-base-openai-detector",
                "chatgpt-detector-roberta",
                "gpt3-small-finetune-cnndaily-news",
                "gpt-neo-125m",
                "falcon-rw-1b",
            ],
            label="Select Model",
            value="gpt2-medium",
        )
        submit_button = gr.Button("Run Detection", variant="primary")
        clear_button = gr.Button("Clear", variant="secondary")
    with gr.Row():
        output = gr.Textbox(
            label="Inference Result",
            placeholder="Maked by Human or AI",
            elem_id="output-text",
        )
    submit_button.click(
        run_test_power, inputs=[model_name, input_text, input_text], outputs=output
    )
    clear_button.click(lambda: ("", ""), inputs=[], outputs=[input_text, output])
    # model_name.change(change_mode, inputs=[model_name], outputs=[model_name])

    with gr.Accordion("Disclaimer", open=False):
        gr.Markdown(
            """
            - **Disclaimer**: This tool is for demonstration purposes only. It is not a foolproof AI detector.
            - **Accuracy**: Results may vary based on input length and quality.
            """
        )
    with gr.Accordion("Citations", open=False):
        gr.Markdown(
            """
            ```
            @inproceedings{zhangs2024MMDMP,
                title={Detecting Machine-Generated Texts by Multi-Population Aware Optimization for Maximum Mean Discrepancy},
                author={Zhang, Shuhai and Song, Yiliao and Yang, Jiahao and Li, Yuanqing and Han, Bo and Tan, Mingkui},
                booktitle = {International Conference on Learning Representations (ICLR)},
                year={2024}
            }
            ```
            """
        )

app.launch()