Climate Action Through Large Language Models
Install Ollama.
Once you have installed Ollama, you can run the following command to install Llama 3 language model:
ollama run llama3.1:8b-instruct-q4_0
We use Python 3.11.6 and Poetry to manage dependencies.
We recommend using pyenv to manage your python versions. To switch to Python 3.11.6, run
pyenv install 3.11.6
pyenv local 3.11.6
To After pulling from github, in your callm folder, do the following to install the dependencies:
poetry build
poetry install # install dependencies
poetry shell # make a virtual environment
If you would like to exit the virtual environment, run
exit # exit shell
Lastly, create a .env file in the root directory of the project and add the following:
OPENAI_API_KEY=<your openai api key>
model=<your model name # e.g. gpt-4-1106-preview>
Please check OpenAI Model Pricing before choosing a model.
Add src to your path by
export PYTHONPATH="${PYTHONPATH}:src/"
All data are available under the data folder. You can download all the data from this Box Link.
We use Streamlit to create a web app. To run the web app, run
streamlit run src/modules/Welcome.py
- Add Heat Index Data
- unlike the other data, this data comes with 3 variables
- Summer daily maximum heat index
- Summer seasonal maximum heat index
- Number of summer days with daily max heat index above 95 / 105 / 115 / 125 F
- unlike the other data, this data comes with 3 variables
- Add Drought Data
- unlike the other data, this data is in the form of a time series
- which will be visualized as a line chart
- and analyzed using time series analysis