Streamlining Incident Documentation in NOCs

Problem Statement:
Incident documentation in Network Operations Centers (NOCs) is often time-consuming and inconsistent, leading to inefficient knowledge transfer. This project implements an intelligent system monitoring solution with real-time analysis, automated reporting, and collaborative features.

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Features

User Authentication System

• Secure login and registration
• Session management
• User-specific report tracking
  

Intelligent System Monitoring

• Real-time system status prediction
• Comprehensive metric tracking (CPU, Memory, Network, etc.)
• Automated anomaly detection
• Status classification (Normal, Warning, Critical)
  

Advanced Reporting

• Automated report generation
• Custom report editing
• Feedback analysis using Google's Gemini AI
• Issue status tracking (Resolved/Unresolved)
• Report voting system with trust scores
  

Interactive Q&A System

• AI-powered query system using Gemini Pro
• Context-aware responses
• Historical data analysis
• Trend identification
  

Data Management

• SQLite database for persistent storage
• Historical data tracking
• Search and filtering capabilities
• Report deletion and management
  

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Key Technologies

• Streamlit: Web interface and interactive components
• SQLite: Database management
• Google Gemini AI: Natural language processing and Q&A system
• Python Libraries:
  • pandas: Data manipulation and analysis
  • python-dotenv: Environment variable management
  • streamlit: Web application framework

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Prerequisites

• Python 3.8+
• Google API key for Gemini AI
• Required Python packages (see requirements.txt)

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Installation

1. Clone the repository:

   git clone https://github.com/your-username/incident-docs-noc.git
   cd incident-docs-noc

2. Create and configure environment variables:

   cp .env.example .env
   # Edit .env and add your Google API key
   GOOGLE_API_KEY=your_api_key_here

3. Install dependencies:

   pip install -r requirements.txt

4. Run the application:

   streamlit run login.py

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Usage Guide

Authentication

• Register a new account or login with existing credentials
• System maintains session state for authenticated users

System Monitoring

• Input system metrics in the Prediction tab
• View real-time status predictions
• Generate detailed system reports

Report Management

• Edit and customize generated reports
• Add feedback and observations
• View AI-generated feedback analysis

Q&A System

• Choose data source (Current Session/Historical/All Data)
• Ask questions about system status
• Receive AI-powered responses with context-aware analysis

Report Trust System

• Reports receive upvotes and downvotes from users
• Trust scores calculated based on voting patterns
• Visual indicators for low-trust reports
• Warning system for potentially unreliable information
  

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Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create your feature branch:

   git checkout -b feature/AmazingFeature

3. Commit your changes:

   git commit -m 'Add some AmazingFeature'

4. Push to the branch:

   git push origin feature/AmazingFeature

5. Open a Pull Request

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Dependencies

• streamlit>=1.10.0
• pandas>=1.4.0
• google-generativeai>=0.3.0
• python-dotenv>=0.19.0
• sqlite3

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License

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