Doctor Ticket – Smart Ticket Classification System

Overview

Doctor Ticket is a small showcase project designed to simulate the workflow of an automated ticket-processing system. It performs three main tasks:

• acquiring maintenance tickets from an API (mock or real)
• cleaning and analyzing ticket text
• storing and managing structured tickets inside a database This project is not meant to be a production system, but a clear demonstration of text classification, scoring rules, and basic database integration.

Features

• text normalization and small typo-correction
• automatic keyword detection
• ticket scoring based on predefined dictionaries
• priority assignment
• database insertion and retrieval
• simple ticket management through SQL queries
• mock API for local testing

How It Works

1. Connect to a mock API (or a real server).
2. Fetch raw ticket descriptions.
3. Normalize the text and correct minor misspellings.
4. Extract known problems and compute a score.
5. Assign a priority level.
6. Store the ticket in an SQLite database.
7. Allow database queries for ticket management.

🗂 Project Structure

Doctor_Ticket/
│
├── core/               # main ticket and DB logic
│   ├── ticket.py
│   └── sqldb.py
│
├── definition/         # dictionaries and scoring rules
│   ├── models.py
│   └── scores_rules.py
│
├── connection_to/      # server/database connectors
│   ├── to_db.py
│   └── to_web.py
│
├── mock_api/           # fake API for local testing
│   ├── mock_apy.py
│   └── tickets_data.json
│
├── tests/              # unittest test suite
│
└── main.py             # entry point

🔧 Installation

• Python 3.10+
• SQLite (bundled with Python)
• Optional: a real API endpoint if not using the mock API

Instal the project:

git clone <your-repo-url>
cd Doctor_Ticket

Usage

python main.py This will:

• fetch tickets from the mock API (or from the real server if configured)
• process and clean the text
• compute score and priority
• insert the results into the database Processed tickets can then be inspected directly inside the SQLite database.

🧪 Tests

python -m unittest discover -s tests -t .

📝 Notes

Planned improvements:

• more robust problem classification
• better handling of technicians' decisions
• stronger negative-sentiment analysis
• data visualization for ticket statistics
• optional web dashboard

📄 License

Released under the MIT License.