AI Chatbot using NLP & TensorFlow

An intelligent chatbot built with Python, TensorFlow, and Natural Language Processing (NLP). This project demonstrates how to build and deploy a simple conversational AI capable of understanding user intent and generating relevant responses.

Additionally, this repository includes a Mac Mini M2 optimized security assistant CLI tool that performs comprehensive file system analysis and security risk assessment.

Features

AI Chatbot

• Intent recognition using custom-trained LSTM model
• Clean, structured NLP pipeline (tokenization, stemming, vectorization)
• FastAPI-powered backend for real-time chatbot responses
• Easy to train and customize with new intents
• Deployment-ready for Render, Hugging Face Spaces, or local Docker

Security Assistant CLI (Mac Mini M2 Optimized)

• Comprehensive file system scanning and analysis
• Real-time security risk assessment and monitoring
• Detailed vulnerability reporting with mitigation recommendations
• Continuous monitoring capabilities
• M2 chip optimized for performance

Tech Stack

• Python 3.10+
• TensorFlow
• scikit-learn
• FastAPI
• Jupyter Notebooks
• JSON for intent training data

Project Structure

AI-Chatbot/
├── data/
│   └── intents.json               # Sample training data (intents, responses)
├── model/
│   └── chatbot_model.h5           # Trained TensorFlow model (generated)
├── notebooks/
│   └── training.ipynb             # Notebook for training and evaluation
├── app/
│   ├── main.py                    # FastAPI app serving the chatbot
│   └── requirements.txt           # Dependencies
├── assistant/
│   ├── cli.py                     # Security assistant CLI tool
│   ├── scanner.py                 # File system scanner
│   ├── security_analyzer.py       # Security risk assessment engine
│   ├── reporter.py                # Report generation
│   └── requirements.txt           # Assistant dependencies
├── README.md
└── .gitignore

Getting Started

Chatbot Setup

1. Clone the repository

git clone https://github.com/jdgiles26/AI-Chatbot.git
cd AI-Chatbot

2. Create a virtual environment and install dependencies

python3 -m venv venv
source venv/bin/activate
pip install -r app/requirements.txt

3. Train the model Open notebooks/training.ipynb and run all cells to train and save the model.

4. Run the FastAPI server

uvicorn app.main:app --reload

Then visit http://127.0.0.1:8000/docs to test your chatbot API.

Security Assistant Setup (Mac Mini M2)

1. Install the assistant CLI dependencies

pip install -r assistant/requirements.txt

2. Make the assistant executable

chmod +x assistant/cli.py

3. Run security scan

python3 assistant/cli.py scan --path /path/to/scan

4. Generate security report

python3 assistant/cli.py report --output security_report.json

5. Start continuous monitoring

python3 assistant/cli.py monitor --interval 300

Usage

Chatbot API

Send POST requests to /predict endpoint:

{
  "message": "Hello"
}

Security Assistant

The assistant CLI provides comprehensive security analysis:

• Scans file system for potential security risks
• Identifies suspicious files and permissions
• Monitors file changes in real-time
• Generates detailed reports with mitigation steps

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For questions and support, please open an issue on GitHub.