AI Log Analyzer

A Streamlit-based web application that uses Google's Gemini AI to analyze test log files and provide intelligent summaries of test failures, root causes, and suggested solutions.

Features

• AI-Powered Analysis: Uses Google Gemini 2.5 Pro to analyze log files
• Intelligent Summarization: Extracts key information including test name, device, status, root cause, and solutions
• User-Friendly Interface: Clean, intuitive Streamlit web interface
• Visual Status Indicators: Color-coded status badges (PASS/FAIL)

Quick Start

Prerequisites

• Python 3.7 or higher
• Google Gemini API key

Installation

1. Clone the repository

   git clone https://github.com/ElzaJose/LogAnalyzerAI.git
   cd LogAnalyzerAI

2. Install dependencies

   pip install -r requirements.txt

3. Set up your API key

   You need to obtain a Google Gemini API key and set it as an environment variable.

   Option 1: Environment Variable (Recommended)

   export GEMINI_API_KEY="your_api_key_here"

   Option 2: Add to your shell profile

   echo 'export GEMINI_API_KEY="your_api_key_here"' >> ~/.bashrc
   source ~/.bashrc

How to Get Google Gemini API Key

1. Go to Google AI Studio
2. Sign in with your Google account
3. Click on "Get API Key"
4. Create a new API key
5. Copy the generated API key

How to Run

1. Start the application

   streamlit run logAnalyzer.py

2. Access the web interface

   • The application will automatically open in your default browser
   • If not, navigate to http://localhost:8501

3. Upload and analyze logs

   • Click "Choose a test log file"
   • Select a .txt log file
   • Wait for AI analysis
   • View the formatted summary report

Configuration

LLM Configuration

The application is configured to use Google Gemini 2.5 Pro model. Key configuration parameters:

• Model: gemini-2.5-pro
• API Key: Set via GEMINI_API_KEY environment variable
• Input: Text files (.txt format)
• Output Format: Structured summary with predefined fields

Customizing the Analysis Prompt

You can modify the analysis prompt in logAnalyzer.py around line 25. The current prompt structure extracts:

• TESTNAME: Name of the test
• DEVICE: Device information
• STATUS: Test status (PASS/FAIL)
• ROOT CAUSE: Why the test failed (max 2 sentences)
• SOLUTION: Suggested fix or next steps

Output Format

The AI analyzer provides structured output in the following format:

TESTNAME: Example Test Name
DEVICE: Test Device Model
STATUS: FAILED
ROOT CAUSE: Brief explanation of why the test failed
SOLUTION: Suggested resolution steps

Dependencies

• streamlit: Web application framework
• google-generativeai: Google Gemini AI integration
• os: Environment variable handling (built-in)
• re: Regular expressions for text formatting (built-in)

Troubleshooting

Common Issues

1. API Key Error

   ⚠️ GEMINI_API_KEY not found in environment variables.
   

   Solution: Ensure your API key is properly set as an environment variable

2. File Upload Issues

   • Only .txt files are supported
   • Ensure your log file is in plain text format
   • The app does not support huge log files

3. Model Connection Issues

   • Check your internet connection
   • Verify your API key is valid and has quota remaining

Debug Mode

To run in debug mode with verbose output:

streamlit run logAnalyzer.py --logger.level=debug

Contributing

Contributions are welcome! Please open an issue or submit a pull request if you have suggestions or improvements.

License

This project is licensed under the MIT License

Future Enhancements

• Support for multiple log file formats (CSV, JSON, XML)
• Support huge log files
• Batch processing of multiple log files
• Export analysis results to PDF/Excel
• Custom prompt templates
• Integration with other LLM providers
• Historical analysis tracking
• Advanced filtering and search capabilities

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