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Project Integrating new datasets into ClimateGPT using MCP server

This project processes climate and emissions data for seven South Asian countries using ERA5 and EDGAR datasets, with server-client scripts for data handling and a ClimateGPT API for generating humanized responses.

Data Sources and Preprocessing

  1. ERA5 Monthly Means Data

    • Source: Download from Copernicus Climate Data Store.
    • Preprocessing: Preprocess the data for seven South Asian countries (not specified here) to be used in:
      • era5mcp.py (server)
      • era5optim.py (client)

  2. EDGAR Emissions Data

    • Source: Obtain from EDGAR GHG 2024 Dataset.
    • Usage: Processed for emissions calculations in:
      • emissions_mcp.py (server)
      • EDGARclient.py (client)

API Integration for Humanized Responses

The project uses the ClimateGPT model to generate humanized responses:

  • API Endpoint: https://erasmus.ai/models/climategpt_8b_latest/v1/chat/completions
  • Headers: {"Content-Type": "application/json"}
  • Authentication: 
    auth = (os.getenv("API_USER"), os.getenv("API_KEY"))
  • Ensure the API_USER and API_KEY environment variables are set to correspond with the credentials stored in your auth.enc file.