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README.md

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 ---
 title: WhiteRabbitNeo
-emoji: 📉
+emoji: 💬
 colorFrom: green
-colorTo: pink
+colorTo: purple
 sdk: gradio
-sdk_version: 6.5.1
+sdk_version: 5.9.1
 app_file: app.py
-pinned: false
+pinned: true
+license: mit
+thumbnail: >-
+  https://cdn-uploads.huggingface.co/production/uploads/64fbe312dcc5ce730e763dc6/VWduEhDSRJXeSqhUzYwCt.png
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## RabbitRedux: A Specialized Cybersecurity Code Classifier
+**RabbitRedux** is an AI-powered model designed to classify and analyze code snippets, with a focus on cybersecurity applications like penetration testing, ransomware analysis, and security automation. Built upon the WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-70B model, RabbitRedux is specialized for cybersecurity and offers high accuracy in analyzing and categorizing both general and cybersecurity-related code functions.
+
+
+**Key Features**
+- Penetration Testing Support: Assists in reconnaissance, enumeration, and task automation during penetration testing.
+- Ransomware Analysis: Tracks and analyzes ransomware trends, providing actionable insights into emerging threats.
+- Code Classification: Efficiently classifies code in general programming and cybersecurity-specific contexts.
+- Adaptive Learning: Utilizes adapter transformers for modular training, making it flexible for quick adaptations to different tasks.
+
+**Datasets Used**
+RabbitRedux leverages a range of datasets focused on both general and cybersecurity-specific tasks:
+
+- Canstralian/Wordlists: A collection of cybersecurity-related wordlists for improved analysis.
+- Canstralian/CyberExploitDB: A database of known cybersecurity exploits for model training.
+- Canstralian/pentesting_dataset: A dataset containing pentesting-specific code snippets and functions.
+- Canstralian/ShellCommands: A dataset dedicated to shell commands commonly used in security operations.
+
+## Model Details
+**Developer:** Canstralian
+**Base Model:** WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-70B, replit/replit-code-v1_5-3b
+**Library:** Adapter Transformers
+**License:** MIT License
+**Metrics:** Precision, Recall, F1 Score
+**Evaluation:** Evaluated for code classification tasks with an emphasis on cybersecurity
+**Tags:** code, text-generation-inference, security, cybersecurity
+
+## Usage
+To use **RabbitRedux** for code classification, simply load the model and apply it for your cybersecurity tasks:
+
+```python
+Copy code
+from adapters import AutoAdapterModel
+
+# Load the base model and RabbitRedux adapter
+model = AutoAdapterModel.from_pretrained("replit/replit-code-v1_5-3b")
+model.load_adapter("Canstralian/RabbitRedux", set_active=True)
+
+# Use the model for classification tasks
+predictions = model.predict(["Your code snippet here"])
+Example Use Case
+This model is perfect for tasks such as:
+
+Classifying code snippets related to penetration testing.
+Analyzing code related to security vulnerabilities or exploits.
+Automatically categorizing code used in ransomware analysis.
+Example:
+python
+Copy code
+code_snippet = """import os
+# Command to start a reverse shell
+os.system('nc -lvp 4444')"""
+
+predictions = model.predict([code_snippet])
+print(predictions)  # Output: ['Reverse Shell', 'Penetration Testing']
+```
+
+## Installation
+**Install dependencies:**
+
+```bash
+pip install transformers
+pip install git+https://github.com/canstralian/RabbitRedux.git
+```
+
+**Load the model:**
+
+```python
+from adapters import AutoAdapterModel
+
+model = AutoAdapterModel.from_pretrained("replit/replit-code-v1_5-3b")
+model.load_adapter("Canstralian/RabbitRedux", set_active=True)
+```
+
+### Evaluation Metrics
+RabbitRedux has been evaluated on code classification tasks using the following metrics:
+
+- Precision: 0.95
+- Recall: 0.92
+- F1 Score: 0.93
+
+These metrics indicate high accuracy in classifying code in the cybersecurity domain.
+
+## Contributions
+**RabbitRedux** is an open-source project, and contributions are welcome! You can contribute by forking the repository, submitting pull requests, or sharing ideas for improvement.
+
+### GitHub Repository: RabbitRedux on GitHub
+### Issues & Feedback: Feel free to open issues or submit feedback directly through the repository.
+
+## Citation
+If you use RabbitRedux in your work or research, please cite it as follows:
+
+### BibTeX:
+
+```bibtex
+@misc{canstralian2024rabbitredux,
+  author = {Canstralian},
+  title = {RabbitRedux: A Model for Code Classification in Cybersecurity},
+  year = {2024},
+  url = {https://github.com/canstralian/RabbitRedux},
+}
+APA: Canstralian. (2024). RabbitRedux: A Model for Code Classification in Cybersecurity. Retrieved from https://github.com/canstralian/RabbitRedux
+```
+
+## License
+RabbitRedux is licensed under the MIT License. See LICENSE for more details.
+
+## Contact
+For more information or to get in touch with the developers, please visit Canstralian's GitHub or reach out through the repository issues page.
+

app.py

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+import gradio as gr
+from huggingface_hub import InferenceClient
+
+# Initialize the InferenceClient with the specified model
+client = InferenceClient("WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-70B")
+
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+):
+    messages = [{"role": "system", "content": system_message}]
+
+    for val in history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0]})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
+
+    messages.append({"role": "user", "content": message})
+
+    response = ""
+
+    try:
+        for message in client.chat_completion(
+            messages,
+            max_tokens=max_tokens,
+            stream=True,
+            temperature=temperature,
+            top_p=top_p,
+        ):
+            token = message['choices'][0]['delta']['content']
+            response += token
+            yield response
+    except Exception as e:
+        yield f"An error occurred: {str(e)}"
+
+# Define the system message with a cybersecurity focus
+system_message = (
+    "You are a cybersecurity expert chatbot, providing assistance on penetration testing, ransomware analysis, and code classification. "
+    "Your responses should be concise, accurate, and tailored to cybersecurity professionals."
+)
+
+# Create the Gradio interface with dark/light mode toggle
+demo = gr.Interface(
+    fn=respond,
+    inputs=[
+        gr.Textbox(value=system_message, label="System Message"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max New Tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (Nucleus Sampling)"),
+        gr.Checkbox(label="Dark Mode", value=False),  # Dark mode toggle
+    ],
+    outputs=[gr.Textbox()],
+    theme="dark",  # Default theme
+)
+
+def toggle_theme(dark_mode):
+    """Toggle between dark and light themes based on user input."""
+    return "dark" if dark_mode else "light"
+
+# Update the theme based on the checkbox value
+demo.change(fn=toggle_theme, inputs=[demo.inputs[4]], outputs=[demo])
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio>=5.9.1
+huggingface_hub>=0.25.1
+aiofiles>=22.0,<24.0
+anyio>=3.0,<5.0
+fastapi>=0.115.2,<1.0
+ffmpy
+gradio_client==1.5.2
+httpx>=0.24.1
+Jinja2<4.0
+markupsafe~=2.0
+numpy>=1.0,<3.0
+orjson~=3.0
+packaging
+pandas>=1.0,<3.0
+pillow>=8.0,<12.0
+pydantic>=2.0
+python-multipart>=0.0.18
+pydub
+pyyaml>=5.0,<7.0
+ruff>=0.2.2; sys.platform != 'emscripten'
+safehttpx>=0.1.6,<0.2.0
+semantic_version~=2.0
+starlette>=0.40.0,<1.0; sys.platform != 'emscripten'
+tomlkit>=0.12.0,<0.14.0
+typer>=0.12,<1.0; sys.platform != 'emscripten'
+typing_extensions~=4.0
+urllib3~=2.0; sys.platform == 'emscripten'
+uvicorn>=0.14.0; sys.platform != 'emscripten'