📊 Log Intelligence System

An AI-powered log analysis dashboard that helps engineers detect anomalies, group similar errors, and identify root causes from system logs.

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🚀 Live Demo

https://log-intelligence-app-l6mqdpuchvmpcptzjnfxqr.streamlit.app/

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📌 Features

• 🔍 Semantic Log Understanding Uses transformer embeddings to understand log meaning beyond keywords

• 📂 Log Clustering Groups similar logs using KMeans for faster debugging

• 🚨 Anomaly Detection Detects unusual logs using Isolation Forest

• 📈 Time-based Trend Analysis Identifies spikes and patterns over time

• 🤖 Root Cause Analysis Highlights dominant error clusters causing system issues

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🧠 Tech Stack

• Python
• Streamlit
• FAISS (vector similarity search)
• Sentence Transformers
• Scikit-learn (KMeans, Isolation Forest)
• Pandas & NumPy

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🏗️ How It Works

1. Upload a log file (.txt)

2. Logs are converted into embeddings using a transformer model

3. System performs:

   • Clustering (KMeans)
   • Anomaly detection (Isolation Forest)

4. Results are visualized in an interactive dashboard

5. Root cause is inferred from dominant anomaly clusters

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📂 Project Structure

log-intelligence-app/
│── app.py
│── requirements.txt
│── README.md

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⚙️ Run Locally

pip install -r requirements.txt
streamlit run app.py

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🌐 Deployment

This app is deployed using Streamlit Community Cloud.

Steps:

1. Push code to GitHub
2. Connect repository to Streamlit Cloud
3. Deploy using app.py as entry point

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🧪 Sample Log Format

2024-01-01 10:00:00 INFO User login successful
2024-01-01 10:05:00 ERROR Database connection timeout
2024-01-01 10:10:00 WARNING High memory usage detected

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💼 Resume Highlight

Built a production-style log intelligence system that clusters logs using KMeans on embeddings, detects anomalies via Isolation Forest, and performs root cause analysis with time-based trend visualization.

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🔮 Future Improvements

• Real-time log streaming
• LLM-based debugging assistant
• Advanced clustering (DBSCAN / HDBSCAN)
• Docker deployment

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📜 License

This project is for educational and demonstration purposes.