Create demo.py

demo/demo.py

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+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:[email protected]" 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)