WheatRust Identification Web App

This project is a web application that identifies wheat crop health based on an uploaded image. The app utilizes a pre-trained deep learning model to classify the image into categories like healthy, leaf rust, or stem rust. The model is deployed using Streamlit, a framework for building interactive web applications.

Dependencies

• A Google Cloud account and a Google Cloud Project
• Google Cloud SDK installed (gcloud CLI utitly)
• Trained Machine Learning Model, our app uses an image classification model trained on a number of different classes of wheat rust
• Docker installed

Build and deploy

1. Getting the app running

1. Clone this repo

git clone https://github.com/mety0r/Wheat-Rust-Identification.git

2. Create and activate a virtual environment

pip install virtualenv
virtualenv <ENV-NAME>
source <ENV-NAME>/bin/activate

4. Install the required dependencies

pip install -r requirements.txt

5. Activate Streamlit and run app.py

streamlit run app.py

Command to build the application in GCP.

gcloud builds submit --tag gcr.io/da21cs154-wheatrust/Wheatrust  --project=da21cs154-wheatrust

Command to deploy the application

gcloud run deploy --image gcr.io/da21cs154-wheatrust/Wheatrust --platform managed  --project=da21cs154-wheatrust --allow-unauthenticatedst