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import gradio as gr
from transformers import AutoTokenizer, AutoModel
from utils_MMD import extract_features # Adjust the import path
from MMD_calculate import mmd_two_sample_baseline # Adjust the import path
MINIMUM_TOKENS = 64
THRESHOLD = 0.5 # Threshold for classification
def count_tokens(text, tokenizer):
"""
Counts the number of tokens in the text using the provided tokenizer.
"""
return len(tokenizer(text).input_ids)
def run_test_power(model_name, real_text, generated_text, N=10):
"""
Runs the test power calculation for provided real and generated texts.
Args:
model_name (str): Hugging Face model name.
real_text (str): Example real text for comparison.
generated_text (str): The input text to classify.
N (int): Number of repetitions for MMD calculation.
Returns:
str: "Prediction: Human" or "Prediction: AI".
"""
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name).cuda()
model.eval()
# Ensure minimum token length
if count_tokens(real_text, tokenizer) < MINIMUM_TOKENS or count_tokens(generated_text, tokenizer) < MINIMUM_TOKENS:
return "Too short length. Need a minimum of 64 tokens to calculate Test Power."
# Extract features
fea_real_ls = extract_features([real_text], tokenizer, model)
fea_generated_ls = extract_features([generated_text], tokenizer, model)
# Calculate test power list
test_power_ls = mmd_two_sample_baseline(fea_real_ls, fea_generated_ls, N=N)
# Compute the average test power value
power_test_value = sum(test_power_ls) / len(test_power_ls)
# Classify the text
if power_test_value < THRESHOLD:
return "Prediction: Human"
else:
return "Prediction: AI"
# CSS for custom styling
css = """
#header { text-align: center; font-size: 1.5em; margin-bottom: 20px; }
#output-text { font-weight: bold; font-size: 1.2em; }
"""
# 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.Markdown(
"""
[Paper](https://openreview.net/forum?id=z9j7wctoGV) | [Code](https://github.com/xLearn-AU/R-Detect) | [Contact](mailto:[email protected])
"""
)
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",
"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",
)
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}
}
```
"""
)
submit_button.click(
run_test_power, inputs=[model_name, "The cat sat on the mat.", input_text], outputs=output
)
clear_button.click(lambda: ("", ""), inputs=[], outputs=[input_text, output])
app.launch()
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