Log Parser

An advanced AI-powered solution for parsing and analyzing logs to identify patterns and anomalies. This tool provides actionable insights for diagnosing and resolving issues efficiently, simplifying log analysis for quicker and more accurate problem detection and resolution.

Tools Used

               

Table of Contents

• Demo
• Features
• Installation
• Usage
• Technologies
• Contributing

Demo

You can see the live demo of the application here.

Features

• Parse various logs to identify patterns and anomalies.
• Provides actionable insights for diagnosing issues.
• Simplifies log analysis for quicker problem detection.
• Currently able to parse OVS, Kernel, Sys Logs & DMESG Logs.

Installation

To run this project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/YashasJKumar/Log_Parser.git
   cd Log_Parser

2. Set up a virtual environment:

   python3 -m venv env
   source env/bin/activate  # On Windows use `env\Scripts�ctivate`

3. Install dependencies:

   pip install -r requirements.txt

4. Place your respective API KEYS:

   Replace st.secrets['GROQ_API_KEY'] & st.secrets['GOOGLE_API_KEY'] with your respective API Keys in "helper_functions.py" on Line 20 & 21.

   Link for API Keys :

   1. GROQ_API_KEY
   2. GOOGLE_API_KEY

Usage

1. Run the application:

   streamlit run main.py

2. Navigate to the application: Open your browser and go to http://localhost:8501 to view the Streamlit interface.

Technologies

• Python - For scripting and backend logic.
• Streamlit - For creating an interactive web interface.
• Regular Expressions - For pattern matching of logs.

Contributing

Contributions are welcome! If you have any suggestions or improvements, please create an issue or submit a pull request.

1. Fork the repository.
2. Create your feature branch (git checkout -b feature/your-feature).
3. Commit your changes (git commit -m 'Add your feature').
4. Push to the branch (git push origin feature/your-feature).
5. Open a pull request.