This repository contains the code for a web application designed to predict Chronic Wasting Disease (CWD) status in counties based on environmental, geographical and human factors. The application is built using Streamlit and utilizes a light gradient boosting model trained on relevant features to perform the prediction.
- CWD Prediction: Predicts whether a county is likely to be CWD positive or negative based on user input.
- Interactive UI: Easy-to-use sliders and select boxes for inputting data.
- Immediate Results: Instantly displays CWD status prediction upon user request.
Before running this application, you need the following installed:
- Python 3.6+
- streamlit
- numpy
- Pillow
The application has a simple interface where the user can input the following parameters:
- Number of Cervid Facilities
- Total Harvest
- Hunting Enclosures
- Baiting
- Feeding
- Whole Carcass Import
- Urine Lures
- Captive Status
- Forest (Average Proportion)
- Clay (Average Percent)
- Streams (Average distance to the nearest water)
After inputting the data, the user can click the 'CWD Test Result' button to receive a prediction.
The model used in this application is a Random Forest Classifier, which has been trained and serialized as 'lgbm_model4.pkl'. Upon starting the app, the model is loaded, and it is used to make predictions based on the user's input.
Upon prediction, an image will be displayed to indicate a positive or negative result:
- A positive prediction displays 'deerp.png'.
- A negative prediction displays 'deern.png'.
Additionally, the app includes a banner image 'CWD1.png' to enhance the user interface.
This project is open source and available under the MIT License.
Contributions to this project are Multistate Conversation Grant # F23AP00488-00.
Thanks to all contributors and researchers in the field of wildlife disease management whose work supported the development of this application.
For support or to report issues, please file an issue on the GitHub issue tracker for this repository.