This is a Flask-based API for classifying cacao pod images using an EfficientNetB0 model. The API provides an endpoint to classify an uploaded image and returns the predicted class along with confidence scores.

• Python 3.x
• Flask
• Flask-CORS
• TensorFlow
• scikit-learn
• scikit-image

1. Clone the repository

git clone https://github.com/louispawaon/sikwati-ai-backend.git
cd sikwati-ai-backend

2. Create a virtual environment (optional but recommended)

python -m venv venv
source venv/bin/activate

3. Install Python dependencies

pip install -r requirements.txt

4. Run the application

gunicorn app:app #For Production
flask run --debug #For Debugging Mode

5. (Optional) Expose the local server using Ngrok

ngrok http 5000

Ngrok will provide a public URL (e.g., https://your-ngrok-subdomain.ngrok.io) that you can use to access your API from anywhere.

• Endpoint: /
• Description: Welcome to Sikwati AI API
• Method: GET
• Response:

{
  "message": "Welcome to the Cacao Pod Classification API"
}

• Endpoint: /api/classify_image
• Description: Classify an uploaded cacao pod image
• Method: POST
• Request Parameters:
  • image (type: file, required: true) - The cacao pod image to be classified
• Responses:
  • 200 OK

    {
        "class": "predicted_class",
        "confidence": 0.85
    }

  • 400 Bad Request

    {
        "error": "No file part"
    }

    {
        "error": "No selected file"
    }

    {
        "error": "The image may not contain a cacao pod"
    }

• The classification model is based on the EfficientNetB0 CNN Architecture

Louis Miguel Pawaon