Potato Disease Classification

This project aims to classify potato diseases using machine learning techniques. It provides a Python-based setup for training the model and running an API for inference. Additionally, it includes instructions for setting up a frontend ReactJs website for interacting with the model.

Setup for Python:

1. Install Python: Ensure Python is installed on your system.

2. Install Python packages:

   pip3 install -r training/requirements.txt
   pip3 install -r api/requirements.txt

Training the Model:

1. Run Jupyter Notebook in Browser:

   jupyter notebook

2. Open training/potato-disease-training.ipynb in Jupyter Notebook.

3. Run all the Cells one by one.

Running the API:

Using FastAPI:

1. Navigate to the API folder:

   cd api

2. Run the FastAPI Server using uvicorn:

   uvicorn main:app --reload --host localhost

   Your API is now running at localhost:8000.

Using FastAPI & TF Serve:

1. Navigate to the API folder:

   cd api

2. Run the TF Serve:

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

3. Run the FastAPI Server using uvicorn:

   • Run it from main.py or main-tf-serving.py using PyCharm run option.
   • Or run it from the command prompt as shown below:

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

   Your API is now running at localhost:8000.

Running the Frontend:

1. Navigate to the frontend folder:

   cd frontend

2. Install dependencies:

   npm install

3. Start the frontend ReactJs website:

   npm start

Project Video

Discover how to classify potato diseases using machine learning in this demo: