This project provides a comprehensive solution for receiving, processing, and managing reports of issues with delivery bots. By leveraging natural language processing techniques, the system efficiently categorizes each report into software, hardware, or field-related problems. It automatically generates a detailed ticket containing the problem's location, type, and a summary of the issue based on the user's description and the bot's status data. This documentation outlines the purpose, technologies used, and setup instructions for the project.

The Bot Issue Tracker API is designed to streamline the process of handling and tracking reports of operational issues with delivery bots. This system ensures that each problem is quickly identified, categorized, and documented, allowing for efficient resolution management. By automating the initial steps of the issue-reporting process, the API facilitates a more responsive and effective maintenance protocol.

For a demonstration of the API usage, please watch the Bot Issue Tracker API Demo Video.

For more documentation of the Bot Issue Tracker API and an list of API endpoints, please visit our API Documentation Wiki.

• Python: High level programming language used to develop the core logic of the API and process natural language inputs.
• Flask: A lightweight WSGI web application framework in Python, used to create the API endpoints and handle HTTP requests and responses.
• OpenAI's GPT: Utilized for natural language processing to interpret the descriptions of issues reported by users and to assist in the automatic categorization of problems.
• pytest: A framework for easily building simple and scalable test cases for the application's API endpoints.

To run this project on your local machine, follow these steps:

1. Clone the Repository

   Start by cloning this repository to your local machine. Use the following command:

   git clone <repository-url>
   

2. Environment Setup

   Ensure that Python 3.8 or later is installed on your system. It's recommended to use a virtual environment for Python projects to manage dependencies efficiently.

   To create a virtual environment, navigate to the project's root directory and run:

   python3 -m venv .venv
   

   Activate the virtual environment:

   • On Windows:

     .\.venv\Scripts\activate
     

   • On macOS/Linux:

     source .venv/bin/activate
     

3. Install Dependencies

   With the virtual environment activated, install the project dependencies using:

   pip install -r requirements.txt
   

4. Environment Variables

   Create a .env file in the project root directory and add the OpenAI API key:

   OPENAI_API_KEY="your_openai_api_key_here"
   

   You could also just run:

   echo 'OPENAI_API_KEY="your_openai_api_key_here"' > .env
   

5. Running the Application

   To start the Flask application, execute:

   python run.py
   

   The API is now running and accessible at http://127.0.0.1:5000.

6. Testing

   Run the tests to ensure everything is set up correctly:

   pytest
   

   If the command is not working you might need to adjust how Python interprets the structure of your project when running tests with pytest.

   Before running pytest, you can set the PYTHONPATH environment variable to include the root of your project. This tells Python where to look for modules. In your terminal, navigate to the root directory of your project and run:

   export PYTHONPATH=$PYTHONPATH:$(pwd)
   pytest
   

   This command temporarily adds the current directory ($(pwd)) to the PYTHONPATH. It's a way to ensure that Python includes your project's directory when searching for modules.