Log Analyzer with AI is a powerful, Streamlit-based tool designed to help users analyze CSV-based log files using AI. This tool visualizes log activity, detects anomalies, and interacts with users through a chatbot interface powered by DeepSeek LLM (via Ollama API).
This project is built to assist developers, network analysts, and security teams in quickly understanding log data and detecting suspicious patterns using AI. It offers:
- Easy file upload (CSV logs)
- Timestamp-based activity graphs
- Column-wise log analysis
- AI chatbot for anomaly detection
- Smart querying with natural language
- Python – Core language
- Streamlit – For building the web UI
- Pandas – Log parsing & transformation
- Matplotlib & Plotly – Visualizations
- Ollama with DeepSeek LLM – AI chatbot and anomaly detection
✅ Upload and analyze any CSV log file
✅ Automatic timestamp parsing and formatting
✅ Interactive log activity graphs with time series
✅ Column-wise data selection and filtering
✅ AI-powered chatbot (via Ollama + DeepSeek)
✅ Detects anomalies and highlights patterns
✅ Clean, user-friendly UI built with Streamlit
''bash git clone https://github.com/your-username/Log-Analyzer.git cd Log-Analyzer
bash Copy Edit pip install -r requirements.txt
bash Copy Edit streamlit run app1.py
🧠 AI Chatbot Instructions Once a CSV log file is uploaded:
Select a column or timestamp range
Use the chat interface to ask natural language questions like:
"What errors occurred the most?"
"Find any suspicious login attempts."
"Summarize unusual spikes."
"Explain high activity periods."
The chatbot responds using DeepSeek LLM to summarize patterns and anomalies.
📝 Usage Guide Upload a .csv log file (make sure it has headers).
Choose the time and data columns for visualizations.
Interact with the AI assistant using questions related to the data.
Review charts and chatbot insights on the same dashboard.
📜 Example Logs You Can Use Server logs
Network traffic logs
Application error logs
Auth/access logs
✅ Format: .csv with proper timestamps and column headers.
🤝 Contributing Contributions are always welcome!
Steps: Fork the repository
Create your feature branch: git checkout -b feature-name
Commit your changes: git commit -m "Add some feature"
Push to the branch: git push origin feature-name
Open a Pull Request ✅
📄 License This project is licensed under the MIT License. Feel free to use, modify, and distribute it with proper attribution.
💡 Future Improvements Add support for JSON and TXT log formats
Real-time streaming log analysis
Advanced anomaly detection (AutoML or fine-tuned LLM)
Export AI findings to PDF/Excel
🙌 Acknowledgments Streamlit
DeepSeek
Ollama
Plotly
Hugging Face
🔗 Made with ❤️ by @dadicharan
Let me know if you'd like help generating a requirements.txt, adding badges (like license or version), or creating a demo video link or screenshots section.