Delete demo/demo.py

demo/demo.py

@@ -1,123 +0,0 @@
-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()