Initial deployment with app, requirements, license, and README

LICENSE

@@ -0,0 +1,19 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   Copyright 2025 Rithvick Kumar
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

README.md

@@ -1,14 +1,90 @@
----
-title: Ai Cybersecurity Agent
-emoji: 👀
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 5.32.1
-app_file: app.py
-pinned: false
-license: apache-2.0
-short_description: AI Cybersecurity Agent is an intelligent threat detection ch
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+title: AI Cybersecurity Agentemoji: 
+🔒colorFrom: greencolorTo: graysdk: gradiosdk_version: 4.0.0app_file: app.pypinned: falselicense: apache-2.0short_description: An intelligent AI-driven cybersecurity agent enhancing DSATP for IoT threat detection in distributed networks.
+AI Cybersecurity Agent
+Welcome to the AI Cybersecurity Agent, a Gradio-based application developed for the Gradio/Hugging Face Hackathon. This project enhances DSATP (Decentralized Swarm AI Threat Prediction), an open-source cybersecurity system, by integrating it with a powerful AI-driven "Security Analyst" persona. The agent leverages Large Language Models (LLMs), log parsing, and file scanning to detect and respond to threats in IoT and distributed networks, all through a sleek Gradio interface.
+🚀 Project Overview
+The AI Cybersecurity Agent transforms DSATP into an interactive, user-friendly tool for real-time threat detection and response. Key features include:
+
+A Gradio-powered chatbot interface for natural-language queries.
+Integration with DSATP’s LLM-based threat analysis and YARA rule scanning.
+Support for log file parsing and JSON-based threat severity outputs.
+A planned multi-pane UI with network graphs and real-time threat dashboards.
+
+This project showcases innovation, technical depth, and usability, addressing the pressing need for decentralized cybersecurity in IoT environments.
+🌟 Features
+
+Chatbot Interface: Interact with an AI "Security Analyst" to analyze suspicious logs or alerts.
+Threat Analysis: Uses DSATP’s mock threat detection (currently sentiment-based, soon LLM-driven) to assess risks.
+JSON Outputs: Displays threat severity scores and remediation suggestions in a structured format.
+MCP Compatibility: Exposes DSATP functions as MCP tools for agentic LLM integration.
+Planned Enhancements (in progress):
+File upload for log parsing and YARA-based malware scanning.
+Network topology graph visualizing DSATP’s peer-to-peer swarm.
+Real-time threat severity charts.
+Dark-themed, high-tech cybersecurity UI.
+
+
+
+🛠️ Setup
+Prerequisites
+
+Python 3.10+
+Git
+Hugging Face account (for Spaces deployment)
+
+Local Installation
+
+Clone the repository:git clone https://huggingface.co/spaces/YOUR_USERNAME/ai-cybersecurity-agent
+cd ai-cybersecurity-agent
+
+
+Create and activate a virtual environment:python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+
+
+Install dependencies:pip install -r requirements.txt
+
+
+Run the app:python app.py
+
+
+Open http://localhost:7860 in your browser to test the app.
+
+Hugging Face Spaces
+The app is deployed on Hugging Face Spaces at: YOUR_SPACE_URL (update with your Space URL after deployment). Simply visit the URL to interact with the chatbot and test threat detection.
+📖 Usage
+
+Open the Gradio app (locally or on Hugging Face Spaces).
+Enter a log message or alert in the textbox (e.g., “Critical error: Unauthorized access detected in server logs. System compromised!”).
+Click Analyze to receive a threat analysis from the Security Analyst.
+View the conversation in the chatbot and detailed results in the JSON output.
+
+Example Inputs:
+
+Positive: The system is running smoothly with no errors detected. All services are operational.
+Negative: Critical error: Unauthorized access detected in server logs. System compromised!
+Neutral: System log entry: User logged in at 14:30. No further details available.
+
+🔐 License
+This project is licensed under the Apache License 2.0. See the LICENSE file for details.
+🤝 Contributing
+We welcome contributions! Please:
+
+Fork the repository.
+Create a feature branch (git checkout -b feature-name).
+Commit changes (git commit -m 'Add feature').
+Push to the branch (git push origin feature-name).
+Open a Pull Request.
+
+For issues or suggestions, open an issue on the Space repository.
+🙌 Acknowledgments
+
+DSATP for the open-source cybersecurity framework.
+Hugging Face for hosting and Gradio for the intuitive UI framework.
+The Gradio/Hugging Face Hackathon for inspiring this project.
+
+📬 Contact
+For questions or feedback, reach out via the Hugging Face Space or open an issue.
+
+Happy hacking, and stay secure! 🔒

app.py

@@ -0,0 +1,45 @@
+import gradio as gr
+from textblob import TextBlob
+
+# Placeholder DSATP function
+def mock_dsatp_scan(text: str) -> dict:
+    """Mock vulnerability scan using sentiment analysis as a placeholder."""
+    blob = TextBlob(text)
+    sentiment = blob.sentiment
+    return {
+        "vulnerability_score": round(sentiment.polarity, 2),
+        "details": "Positive sentiment suggests low risk; negative suggests high risk."
+    }
+
+# Chatbot function
+def chatbot_response(user_input, history):
+    """Basic chatbot response with mock DSATP integration."""
+    if history is None:
+        history = []  # Initialize history if None
+    scan_result = mock_dsatp_scan(user_input)
+    response = f"Security Analyst: Analyzed input. Vulnerability score: {scan_result['vulnerability_score']}. Details: {scan_result['details']}"
+    # Append new message tuple to history
+    updated_history = history + [(user_input, response)]
+    return updated_history, scan_result  # Return updated history and scan_result
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# AI Cybersecurity Agent")
+    with gr.Row():
+        with gr.Column():
+            chatbot = gr.Chatbot(label="Security Analyst Chat")
+            user_input = gr.Textbox(placeholder="Enter text to analyze (e.g., log data)...")
+            submit_btn = gr.Button("Analyze")
+        with gr.Column():
+            gr.Markdown("### Placeholder for Network Graph and Charts")
+            output_json = gr.JSON(label="Scan Results")
+    
+    submit_btn.click(
+        fn=chatbot_response,
+        inputs=[user_input, chatbot],
+        outputs=[chatbot, output_json]
+    )
+
+# Launch the app with MCP server
+if __name__ == "__main__":
+    demo.launch(mcp_server=True)

requirements.txt

@@ -0,0 +1,2 @@
+gradio[mcp]>=4.0.0
+textblob