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PriceParser πŸš€

Welcome to PriceParser, an advanced tool designed to parse and extract pricing information with precision and efficiency. This project is tailored to meet the needs of developers looking to handle and process pricing data seamlessly.

Built for a job interview demo purpose by Leo Hanhart

Table of Contents πŸ“š


Introduction

PriceParser is built to streamline the process of extracting and delivering pricing data across various systems. Whether you're integrating with external sources, processing raw data, or building dashboards, PriceParser provides a robust framework to handle these tasks efficiently.


Key Features 🌟

  • Parses Price Out Of Plain Text: Designed to handle unstructured data efficiently.
  • Docker Support: Simplified deployment with containerization.
  • Easy-to-Use Interface: Straightforward setup and execution process.

Dependencies πŸ“¦

Ensure you have the following installed:

  • Python 3.11+
  • Docker (for containerized runs)
  • Docker Compose (for Docker Compose runs)

Run with Docker 🐳

Build the Docker Image:

docker build -t price-parser .
docker run -p 8050:8050 --name price-parser price-parser

Access the Application: Open your browser and navigate to http://localhost:8050.

Stop the Container: To stop the container, run:

docker stop price-parser

Remove the Container: If you want to remove the container, run:

docker rm price-parser

Run with Docker Compose 🐳

If a docker-compose.yml is already included in the project:

Start the Service:

docker-compose --env-file .env up --build

Access the Application: Open your browser and navigate to http://localhost:8050.

Run without Docker πŸ–₯️

docker rm price-parser

Clone the Repository:

git clone https://github.com/your-repo/PriceParser.git
cd PriceParser

Set Up a Virtual Environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install Dependencies:

pip install -e .

Run the Application:

python src/PriceParser/services/parser_demo.py
Access the Application: Open your browser and navigate to http://localhost:8050.

credits to : Leo Hanhart.