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Change","sub_category":"Climate Data Access and Visualization","monthly_downloads":15,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# ClimSight\n\nClimSight is an AI-powered climate decision-support system that combines Large Language Models with multi-source climate data to deliver localized, actionable insights. It serves researchers, policymakers, agricultural planners, urban developers, and anyone who needs to understand how climate change affects real-world decisions.\n\n![Image](https://github.com/user-attachments/assets/f9f89735-ef08-4c91-bc03-112c8e4c0896)\n\n### What makes it different\n\n- **Agentic AI pipeline** — specialized agents (smart agent, data analysis agent, search agent) orchestrate data retrieval, code execution, and synthesis autonomously\n- **Multi-source climate data** — nextGEMS, ICCP, AWI-CM CMIP6, DestinE, and ERA5 reanalysis via Arraylake\n- **RAG-augmented responses** — retrieves context from IPCC reports and scientific literature\n- **Interactive map-based interface** — click anywhere on the globe and ask a climate question\n- **Python REPL execution** — the agent writes and runs analysis code in a sandboxed Jupyter kernel\n\n---\n\n## Architecture\n\nClimSight has two interface modes:\n\n| | **React UI** (new) | **Streamlit UI** (legacy) |\n|---|---|---|\n| Frontend | React + TypeScript + Tailwind CSS + Vite | Streamlit |\n| Backend  | FastAPI + WebSocket (real-time streaming) | Streamlit server |\n| Command  | `uvicorn` + `npm run dev` | `streamlit run` |\n\nThe **React UI** is the actively developed interface with real-time streaming, a dark/light theme toggle, and a modern component architecture. The Streamlit UI is kept for backwards compatibility.\n\n---\n\n## Quick Start (React UI)\n\n### Prerequisites\n\n- **Python 3.11+**\n- **Node.js 18+** and **npm**\n- An **OpenAI API key** (or other supported LLM provider)\n\n### 1. Clone \u0026 set up the Python environment\n\n```bash\ngit clone https://github.com/CliDyn/climsight.git\ncd climsight\n\n# Option A: conda/mamba (recommended)\nmamba env create -f environment.yml\nconda activate climsight\n\n# Option B: pip + venv\npython -m venv venv\nsource venv/bin/activate   # Windows: venv\\Scripts\\activate\npip install -r requirements.txt\npip install -r requirements-api.txt\n```\n\n### 2. Download the climate data\n\n```bash\npython download_data.py\n\n# Optional: DestinE data (~12 GB)\npython download_data.py DestinE\n```\n\n### 3. Configure API keys\n\nCreate a `.env` file in the repo root (or export the variables):\n\n```bash\nOPENAI_API_KEY=\"sk-...\"\n\n# Optional — enables ERA5 time series retrieval\nARRAYLAKE_API_KEY=\"your-arraylake-key\"\n```\n\n### 4. Install frontend dependencies\n\n```bash\ncd frontend\nnpm install\ncd ..\n```\n\n\u003e **Important:** Run `npm install` *before* starting the backend. If uvicorn is already running with `--reload`, the creation of `node_modules/` triggers a server restart, causing temporary `ETIMEDOUT` proxy errors in the frontend.\n\n### 5. Start the FastAPI backend\n\n```bash\nuvicorn api.main:app --host 0.0.0.0 --port 8000 --reload --reload-exclude 'frontend/node_modules/*'\n```\n\nThe API runs on `http://localhost:8000`. Health check: `GET /health`.\n\n### 6. Start the React frontend\n\n```bash\ncd frontend\nnpm run dev\n```\n\nOpen **http://localhost:5173** in your browser. The Vite dev server automatically proxies `/api` and `/artifacts` requests to the backend.\n\n---\n\n## Running the Legacy Streamlit UI\n\nIf you prefer the original interface:\n\n```bash\nstreamlit run src/climsight/climsight.py\n```\n\nOpens on `http://localhost:8501`.\n\n---\n\n## Docker (Stable Release v1.0.0)\n\n\u003e [!NOTE]\n\u003e The Docker image ships the Streamlit UI (v1.0.0). The React UI is not yet containerized.\n\n```bash\nexport OPENAI_API_KEY=\"sk-...\"\ndocker pull koldunovn/climsight:stable\ndocker run -p 8501:8501 -e OPENAI_API_KEY=$OPENAI_API_KEY koldunovn/climsight:stable\n```\n\n---\n\n## Configuration\n\nClimSight reads `config.yml` from the working directory. Key settings:\n\n```yaml\nllm_smart:\n  model_type: \"openai\"\n  model_name: \"gpt-5.2\"         # main reasoning model\n\nuse_smart_agent: true            # enable the agentic pipeline\nuse_era5_data: true              # ERA5 time series via Arraylake\nuse_powerful_data_analysis: true # agent writes \u0026 executes Python code\n\nclimate_data_source: \"nextGEMS\"  # nextGEMS | ICCP | AWI_CM | DestinE\n```\n\nSee `config.yml` for the full reference (climate data sources, RAG settings, system prompts, etc.).\n\n---\n\n## Project Structure\n\n```\nclimsight/\n├── api/                    # FastAPI backend\n│   ├── main.py             #   app factory, CORS, static files\n│   └── routes/             #   REST + WebSocket endpoints\n├── frontend/               # React UI (Vite + Tailwind + TypeScript)\n│   ├── src/\n│   │   ├── App.tsx          #   main app shell\n│   │   ├── components/      #   MapPanel, QueryForm, ReportView, SettingsPanel, StatusBar\n│   │   └── api/             #   API client\n│   ├── package.json\n│   └── vite.config.ts\n├── src/climsight/          # Core Python engine\n│   ├── smart_agent.py      #   agentic orchestration (LangGraph)\n│   ├── agent_helpers.py    #   tool definitions \u0026 agent utilities\n│   ├── session_manager.py  #   per-session state \u0026 memory\n│   └── tools/              #   ERA5 retrieval, reflection, REPL, search\n├── data/                   # Climate datasets (downloaded via download_data.py)\n├── rag_db/                 # ChromaDB vector stores for RAG\n├── config.yml              # Main configuration\n├── environment.yml         # Conda environment spec\n├── requirements.txt        # Pip dependencies (core)\n├── requirements-api.txt    # Pip dependencies (FastAPI backend)\n└── pyproject.toml          # Package metadata\n```\n\n---\n\n## Batch Processing\n\nThe `sequential/` directory contains tools for generating, validating, and processing climate questions in bulk. See [sequential/README.md](sequential/README.md).\n\n---\n\n## Citation\n\nIf you use or refer to ClimSight in your work, please cite:\n\n\u003e Kuznetsov, I., Jost, A.A., Pantiukhin, D. et al. Transforming climate services with LLMs and multi-source data integration. _npj Clim. Action_ **4**, 97 (2025). https://doi.org/10.1038/s44168-025-00300-y\n\n\u003e Koldunov, N., Jung, T. Local climate services for all, courtesy of large language models. _Commun Earth Environ_ **5**, 13 (2024). https://doi.org/10.1038/s43247-023-01199-1\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.1038/s44168-025-00300-y","https://doi.org/10.1038/s43247-023-01199-1"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["ocean-model"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/303799","html_url":"https://ost.ecosyste.ms/projects/303799"}