Create app.py

app.py

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+__all__ = ["app"]
+
+import gradio as gr
+import torch
+import numpy as np
+from transformers import AutoConfig, AutoTokenizer, DataCollatorWithPadding, DebertaV2ForSequenceClassification
+
+MINIMUM_TOKENS = 48
+FOUNDATION_MODEL_NAME = "binh230/deberta-base"
+# Load the tokenizer and model for DeBERTa
+tokenizer = AutoTokenizer.from_pretrained(FOUNDATION_MODEL_NAME)
+config = AutoConfig.from_pretrained(FOUNDATION_MODEL_NAME)
+config.num_labels = 2  # For binary classification
+model = DebertaV2ForSequenceClassification.from_pretrained(FOUNDATION_MODEL_NAME, config=config)
+model.to("cuda")
+
+# Text processing and prediction function
+def count_tokens(text):
+    return len(text.split())
+
+def run_detector(input_str):
+    if count_tokens(input_str) < MINIMUM_TOKENS:
+        return f"Too short length. Need minimum {MINIMUM_TOKENS} tokens to run Binoculars."
+    
+    # Tokenize input text
+    inputs = tokenizer(input_str, return_tensors="pt", padding=True, truncation=True).to("cuda")
+    
+    # Run model and get prediction
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        prediction = torch.argmax(logits, dim=-1).item()
+    
+    # Interpret prediction
+    return "Most likely AI-Generated" if prediction == 1 else "Most likely Human-Generated"
+
+# Gradio app interface
+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;
+  align-content: center;
+  padding-top: 12px;
+  justify-content: flex-end;
+  margin: 0 10px;
+  text-decoration: none;
+  color: #000;
+}
+"""
+
+capybara_problem = '''Dr. Capy Cosmos, a capybara unlike any other, astounded the scientific community with his groundbreaking research...'''
+
+with gr.Blocks(css=css, theme=gr.themes.Default(font=[gr.themes.GoogleFont("Inconsolata"), "Arial", "sans-serif"])) as app:
+    with gr.Row():
+        with gr.Column(scale=3):
+            gr.HTML("<h1>Mambaformer Detecting AI generated text</h1>")
+        with gr.Column(scale=1):
+            gr.HTML("""
+                <p>
+                <a href="https://github.com/DanielBinh2k3/Mamba-AI-generated-text-detection" target="_blank">code</a>
+                <a href="mailto:truonggiabjnh2003@gmail.com" target="_blank">contact</a>
+                </p>
+            """, elem_classes="hyperlinks")
+
+    with gr.Row():
+        input_box = gr.Textbox(value=capybara_problem, placeholder="Enter text here", lines=8, label="Input Text")
+    with gr.Row():
+        submit_button = gr.Button("Run Detection", variant="primary")
+        clear_button = gr.ClearButton()
+    with gr.Row():
+        output_text = gr.Textbox(label="Prediction", value="Most likely AI-Generated")
+
+    with gr.Accordion("Disclaimer", open=False):
+        gr.Markdown("""
+            - `Accuracy`: AI-generated text detectors aim for accuracy, but no detector is perfect.
+            - `Use Cases`: This tool is most useful for detecting AI-generated content in moderation scenarios.
+            - `Known Weaknesses`: Non-English texts and highly memorized texts (like constitutions) may yield unreliable results.
+        """)
+
+    with gr.Accordion("Cite our work", open=False):
+        gr.Markdown("""
+            ```bibtex
+            @misc{BamBa2024llm,
+                  title={Enhancing AI Text Detection through MambaFormer and Adversarial Learning Techniques}, 
+                  author={Truong Nguyen Gia Binh},
+                  year={2024},
+                  eprint={},
+                  archivePrefix={},
+                  primaryClass={}
+            }
+            ```
+        """)
+
+    submit_button.click(run_detector, inputs=input_box, outputs=output_text)
+    clear_button.click(lambda: ("", ""), outputs=[input_box, output_text])
+
+# Run the Gradio app
+if __name__ == "__main__":
+    app.launch(share=True)