Delete demo.py

demo.py

@@ -1,88 +0,0 @@
-import gradio as gr
-
-from MMD_calculate import MMDMPDetector 
-
-detector = MMDMPDetector()  # Initialize your MMD-MP detector
-MINIMUM_TOKENS = 64  # Minimum number of tokens for detection
-
-def count_tokens(text):
-    return len(text.split())  # Count the number of tokens (words) in the text
-
-def run_detector(input_text):
-    # Check if input meets the token requirement
-    if count_tokens(input_text) < MINIMUM_TOKENS:
-        return f"Error: Text is too short! At least {MINIMUM_TOKENS} tokens are required."
-    
-    # Perform detection (replace this with your model's prediction logic)
-    prediction = detector.predict(input_text)
-    return f"Result: {prediction}"
-
-def change_mode(mode):
-    if mode == "Low False Positive Rate":
-        detector.set_mode("low-fpr")  # Adjust detector mode
-    elif mode == "High Accuracy":
-        detector.set_mode("accuracy")
-    return f"Mode set to: {mode}"
-
-css = """
-.green { color: black!important; line-height:1.9em; padding: 0.2em 0.2em; background: #ccffcc; border-radius:0.5rem;}
-.red { color: black!important; line-height:1.9em; padding: 0.2em 0.2em; background: #ffad99; border-radius:0.5rem;}
-.hyperlinks {
-    display: flex;
-    align-items: center;
-    justify-content: flex-end;
-    padding: 12px;
-    margin: 0 10px;
-    text-decoration: none;
-    color: #000;
-}
-"""
-
-with gr.Blocks(css=css, theme=gr.themes.Default(font=[gr.themes.GoogleFont("Inconsolata"), "Arial", "sans-serif"])) as app:
-    # Header Row
-    with gr.Row():
-        with gr.Column(scale=3):
-            gr.HTML("<h1>Binoculars: Zero-Shot LLM-Text Detector</h1>")
-        with gr.Column(scale=1):
-            gr.HTML("""
-                <p class="hyperlinks">
-                <a href="https://arxiv.org/abs/2401.12070" target="_blank">Paper</a> |
-                <a href="https://github.com/AHans30/Binoculars" target="_blank">Code</a> |
-                <a href="mailto:contact@example.com" target="_blank">Contact</a>
-                </p>
-            """)
-
-    # Input Section
-    with gr.Row():
-        input_text = gr.Textbox(placeholder="Enter text here...", lines=8, label="Input Text")
-
-    # Mode Selector and Buttons
-    with gr.Row():
-        mode_selector = gr.Dropdown(
-            choices=["Low False Positive Rate", "High Accuracy"], 
-            label="Detection Mode", 
-            value="Low False Positive Rate"
-        )
-        submit_button = gr.Button("Run Binoculars", variant="primary")
-        clear_button = gr.Button("Clear")
-
-    # Output Section
-    with gr.Row():
-        output_text = gr.Textbox(label="Prediction", value="Results will appear here...")
-
-    # Disclaimer Section
-    with gr.Accordion("Disclaimer", open=False):
-        gr.Markdown("""
-            - **Accuracy**: This detector uses state-of-the-art techniques, but no model is perfect.
-            - **Mode Information**:
-                - High Accuracy: Maximizes accuracy by adjusting thresholds.
-                - Low False Positive Rate: Reduces human-written text being falsely flagged as AI-generated.
-            - **Limitations**: Detection is best on texts with 64–300 tokens. Very short or extremely long texts may lead to inaccurate results.
-        """)
-
-    # Bind Functions to Buttons
-    submit_button.click(run_detector, inputs=input_text, outputs=output_text)
-    clear_button.click(lambda: ("", ""), outputs=[input_text, output_text])
-    mode_selector.change(change_mode, inputs=mode_selector, outputs=mode_selector)
-
-