Welcome to the SST Prediction App! 🎉
This application enables users to predict Sea Surface Temperatures (SST) based on historical data. With a specialized ConvLSTM model, the app processes 5 days of SST data to predict the SST for the 6th day.
🔧 Trained by Boris
| Layer (type) | Output Shape | Param # |
|---|---|---|
| ConvLSTM2D | (None, 401, 451, 32) | 38,144 |
| BatchNormalization | (None, 401, 451, 32) | 128 |
| Dropout | (None, 401, 451, 32) | 0 |
| Conv2D | (None, 401, 451, 64) | 18,496 |
| BatchNormalization_1 | (None, 401, 451, 64) | 256 |
| Dropout_1 | (None, 401, 451, 64) | 0 |
| Conv2D_1 | (None, 401, 451, 32) | 18,464 |
| BatchNormalization_2 | (None, 401, 451, 32) | 128 |
| Dropout_2 | (None, 401, 451, 32) | 0 |
| Conv2D_2 | (None, 401, 451, 1) | 289 |
Total params: 75,905
Trainable params: 75,649
Non-trainable params: 256
The data used hails from the MUR-JPL-L4-GLOB-v4.1 dataset on earthdata.nasa.gov.
- Clone the repo:
git clone https://github.com/NaNa7Miiii/upwelling_prediction.git- Navigate to the directory：
cd upwelling_prediction- Install the dependencies:
pip install -r requirements.txt- Launch the app:
streamlit run app.py- Please comment out this chunk of code which aims to create a .netrc file to access NASA's data, but if you want to deploy the repo in streamlit cloud please don't do anything on that:
NETRC_PATH = os.path.expanduser("~/.netrc")
def create_netrc(machine, login, password, path=NETRC_PATH):
netrc_content = f"""
machine {machine}
login {login}
password {password}
"""
with open(path, "w") as file:
file.write(netrc_content)
os.chmod(path, 0o600)
secrets = st.secrets["earthdata_test"]
create_netrc(secrets["machine"], secrets["login"], secrets["password"])
After all that, you are ready to go!