Section Introduction

In this lesson, we will develop the backend of our application step by step. Our objectives include:

• Configuring a virtual environment.
• Setting up the project structure.
• Creating a Flask API.
• Implementing OpenCV to modify images.
• Debugging an identified issue.
• Validating images.
• Implementing error handling.
• Testing the endpoint with Postman.

We will achieve these tasks with the assistance of Generative AI and leverage cutting-edge tools such as:

• Tabnine – Explore more at www.tabnine.com
• BlackboxAI – Learn more at www.blackbox.ai
• GitHub Copilot – Get started at github.com/features/copilot

Sample Python Function to Parse Expenses

Below is an example Python function that demonstrates how to parse a string of expenses into a list of tuples containing the date, amount, and currency:

def parse_expenses(expenses_string):
    # Parse the list of expenses and return a list of triples (date, amount, currency)
    return [tuple(line.split()) for line in expenses_string.splitlines() if line]

By the end of this lesson, you'll have built a fully functioning API endpoint that accepts an image, reduces its quality through compression, and displays the output. The complete project code is available on GitHub at:

Kode Repository - Super Image Optimizer

Uploading an Image Using the API Endpoint

Below is an example demonstrating how to upload an image using our API endpoint. Replace <your_api_key> and <path_to_image_file> with your actual API key and file path respectively. Additionally, adjust the quality parameter to control the compression level.

POST http://172.0.1:5000/upload
key: <your_api_key>
image: <path_to_image_file>
quality: <compression_quality>

# Example using a file:
File: cockpit.jpeg 
Text: 100

# Expected Response:
200 OK

Let's dive in and start building a robust backend solution!