Potato Disease Classification

Setup for Python:

1. Install Python (Setup instructions)

2. Install Python packages

pip3 install -r api/requirements.txt

3. Install Tensorflow Serving

Setup for ReactJS

1. Install Nodejs
2. Install NPM
3. Install dependencies

cd frontend
npm install --from-lock-json
npm audit fix

4. Copy .env.example as .env.

5. Change API url in .env.

Training the Model

1. Download the data from kaggle.
2. Only keep folders related to Potatoes.
3. Run Jupyter Notebook in Browser.

jupyter notebook

4. Open training/potato-disease-training.ipynb in Jupyter Notebook.
5. In cell #2, update the path to dataset.
6. Run all the Cells one by one.
7. Copy the model generated and save it with the version number in the models folder.

Running the API

Using FastAPI

1. Get inside api folder

cd api

2. Run the FastAPI Server using uvicorn

uvicorn main:app --reload --host 0.0.0.0

3. Your API is now running at 0.0.0.0:8000

Using FastAPI & TF Serve

1. Get inside api folder

cd api

2. Copy the models.config.example as models.config and update the paths in file.
3. Run the TF Serve (Update config file path below)

docker run -t --rm -p 8501:8501 -v C:/Code/potato-disease-classification:/potato-disease-classification tensorflow/serving --rest_api_port=8501 --model_config_file=/potato-disease-classification/models.config

4. Run the FastAPI Server using uvicorn For this you can directly run it from your main.py or main-tf-serving.py using pycharm run option (as shown in the video tutorial) OR you can run it from command prompt as shown below,

uvicorn main-tf-serving:app --reload --host 0.0.0.0

5. Your API is now running at 0.0.0.0:8000

Running the Frontend

1. Get inside api folder

cd frontend

2. Copy the .env.example as .env and update REACT_APP_API_URL to API URL if needed.
3. Run the frontend

npm run start