Update demo.py

demo.py

@@ -1,56 +1,9 @@
 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)
 
+# Mock function for testing layout
 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"
+    return "Prediction: Human (Mocked)"
 
-# 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; }
@@ -95,28 +48,8 @@ with gr.Blocks(css=css) as app:
             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
+        run_test_power, inputs=[model_name, "Example real text", input_text], outputs=output
     )
     clear_button.click(lambda: ("", ""), inputs=[], outputs=[input_text, output])