Spoiltracker is a Python package that helps track the expiration dates of products. It calculates expiration dates based on the production dates and shelf life information and generates an expiry report for products that are approaching their expiration dates.

Spoiltracker is useful for businesses that deal with perishable products and need to keep track of their expiration dates. It can be used in various industries such as food and beverage, agriculture, perishable goods logistics, warehousing, pharmaceuticals, and cosmetics. The package helps businesses to:

• Maintain a record of product SKUs, names, brands, and expiration dates.
• Calculate expiration dates based on the production dates and shelf life information.
• Generate an expiry report that lists products approaching their expiration dates within a specified number of days.
• Remove expired entries from the history file to keep the record up to date.
• Clear the history file when necessary.

Spoiltracker is a valuable tool for deli and cheese counters, as well as any industry where spoilage mitigation is crucial. It helps track the expiration dates of perishable products, such as deli meats, cheeses, and other fresh foods, allowing businesses to effectively manage inventory, reduce waste, and ensure product quality and safety.

By utilizing Spoiltracker in deli and cheese counters, businesses can:

• Optimize inventory management: Spoiltracker enables businesses to keep a record of products along with their production dates and shelf life information. This helps deli and cheese counter managers to efficiently manage their inventory by identifying products that are approaching their expiration dates. By staying proactive, managers can ensure that products are used or sold before they spoil, reducing waste and optimizing stock levels.

• Minimize spoilage and waste: Spoiltracker assists in identifying products that are close to their expiration dates. With the generated expiry reports based on specified criteria, such as the number of days until expiration, businesses can take proactive measures to minimize spoilage and waste. This can include implementing promotional activities, such as discounts or special offers, to encourage customers to purchase products before they expire.

• Ensure product quality and safety: Maintaining accurate and up-to-date records of product expiration dates is crucial for ensuring product quality and safety. Spoiltracker allows businesses to monitor and manage expiration dates effectively, reducing the risk of serving or selling expired products to customers. By staying on top of product freshness, businesses can enhance customer satisfaction and reputation.

• Streamline operations: Spoiltracker streamlines the process of managing product expiration dates. With its ability to remove expired entries from the history file and clear the history file, businesses can maintain a clean and organized record of products. This streamlines operations, making it easier for deli and cheese counter staff to access information, plan for product usage, and maintain compliance with food safety regulations.

To install Spoiltracker, follow these steps:

1. Clone the repository from GitHub:

   git clone https://github.com/psibir/spoiltracker.git

2. Create a virtual environment and install the required dependencies:

   python -m venv .venv
   source .venv/bin/activate
   pip install -r requirements.txt

3. Navigate to the spoiltracker/src directory:

   cd spoiltracker/src

4. Run the spoiltracker.py script:

   python spoiltracker.py

   This will execute the SpoilerTracker script.

The package can also be installed using pip:

pip install spoiltracker

The Spoiltracker tool provides various command-line flags to customize its behavior. These flags allow you to specify input files, set thresholds, clear data, and control the output. Here are the available command-line flags:

• --csv_file: Specifies the path to the CSV file containing SKUs. This file should contain SKU data for processing.
• --production_date: Specifies the production date in the format YYYY-MM-DD. This flag is used in conjunction with --csv_file to process the SKUs with the given production date.
• --days: Sets the threshold for the number of days until expiration. The default value is 3. SKUs expiring within this number of days will be included in the report.
• --clear-expired: If provided, this flag removes expired entries from the history.csv file and clears the expiry report file.
• --output-dest: Specifies the destination file for the expiry report. The report will be saved in the specified file. If not provided, the default output file is used.
• --clear-history: If provided, this flag clears the history file, removing all the recorded SKU data.
• --batch: Specifies the directory containing files with SKUs to batch process. The files must be named in the format YYYY-MM-DD, representing the production date for each batch.
• --table: If provided, this flag outputs a pretty-printed expiry report as expiryreport.txt.

Note: The tool can be run with or without the --csv_file and --production_date flags. If these flags are not provided, the tool generates an expiry report based on existing history data.

To use the Expiry Tracker tool, run the script with the desired command-line flags.

1. Process a single CSV file:

   python spoiltracker.py --csv_file data.csv --production_date 2023-06-01

2. Generate an expiry report based on existing history data on products expiring in 7 days:

   python spoiltracker.py --days 7

3. Clear expired entries and the expiry report:

   python spoiltracker.py --clear-expired

4. Batch process files in a directory:

   python spoiltracker.py --batch ./examples/batch/

5. Output a pretty-printed expiry report for products expiring within 10 days:

   python spoiltracker.py --table --days 10

The SpoilTracker package provides the following methods in the ExpiryTracker Class:

• load_shelf_life_data(): Loads the shelf life data from the shelf life file.
• calculate_expiration_date(production_date, shelf_life): Calculates the expiration date based on the production date and shelf life.
• append_to_history(data): Appends data to the history file.
• append_to_expiry_report(data, days, output_dest=None): Appends data to the expiry report file for products that fall within the specified threshold.
• sort_expiry_report(output_dest): Sorts the expiry report file by expiration date.
• generate_expiry_report(days, output_dest=None): Generates the expiry report for products that fall within the specified threshold.
• clear_expired_entries(): Removes expired entries from the history file and clears the expiry report file.
• clear_history_file(): Clears the history file.
• process_csv(csv_file_path, prod_date): Processes a CSV file, calculates expiration dates, and returns the processed data.
• print_table(output_dest=None, show_console=False): Prints a pretty-formatted table of the expiry report and saves it as a text file.
• run(csv_file=None, production_date=None, days=3, clear_expired=False, output_dest=None, clear_history=False, print_table=False): Runs the SpoilTracker functionality based on the provided arguments.

To use Spoiltracker in a Python script, you can import the ExpiryTracker class and create an instance of it. Then, call the run method with the desired parameters.

from spoiltracker import ExpiryTracker

expiry_tracker = ExpiryTracker()
expiry_tracker.run(csv_file="sku_list.csv", production_date="2023-06-01", days=5, remove_expired=True)

Spoiltracker requires shelf life data to calculate expiration dates. You can load youre own csv file of SKUs, products, and their shelf life and keep track of daily production. By default, it expects a CSV file named "shelflife.csv" in the ./csv directory. The file should have the following columns: SKU, Name, Brand, "Shelf Life" (in days).

SKU,Name,Brand,Shelf Life
123,Product 1,Brand 1,10
456,Product 2,Brand 2,7

You can customize the shelf life file path by providing it when creating an instance of ExpiryTracker.

expiry_tracker = ExpiryTracker(shelf_life_file="custom_shelflife.csv")

SpoilTracker has the following dependencies:

• argparse: For parsing command-line arguments.
• csv: For reading and writing CSV files.
• os: For working with file paths and directories.
• datetime, timedelta: For working with dates and calculating expiration dates.
• tabulate: For generating formatted tables.

See MIT License.

See Contributing Guidelines.

If you have any questions about the project, you can reach out to me via email or GitHub with the information below:

GitHub