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import gradio as gr
# Mock function for testing layout
def run_test_power(model_name, real_text, generated_text, N=10):
return "Prediction: Human (Mocked)"
css = """
#header { text-align: center; font-size: 2em; margin-bottom: 15px; }
#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">Human or AI Text Detector</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:[email protected]" target="_blank">Contact</a>
</div>
"""
)
with gr.Row():
input_text = gr.Textbox(
label="Input Text",
placeholder="Enter the text to check",
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",
)
with gr.Row():
submit_button = gr.Button("Run Detection", variant="primary")
clear_button = gr.Button("Clear", variant="secondary")
with gr.Row():
output = gr.Textbox(
label="Prediction",
placeholder="Prediction: 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])
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()
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