Customer Operations Simulator

A full-stack, real-time simulation platform for customer operations teams. This project combines backend automation (FastAPI) and an interactive dashboard (Streamlit) to simulate rule-based portfolio operations, ticket handling, and performance analytics.

---

🚀 Features

🔧 Backend (FastAPI)

• Customer and Rule Simulation Engine
• Daily Rule Execution with Log Tracking
• Support Ticket Auto-Generation on Failure
• Status Update API for Ticket Management
• MySQL Database Integration
• Scheduled Background Tasks with APScheduler

📊 Frontend (Streamlit)

• Dashboard for Executions, Tickets, and KPIs
• Filters by Customer Priority, Status, Name
• Success/Failure Bar Chart (Stacked Altair)
• Execution Trend Line Chart (Daily View)
• CSV Export for Reporting
• Summary Tables and Drilldowns

---

🗂️ Project Structure

customer_ops_simulator/
├── main.py                  # FastAPI app entrypoint
├── db.py                   # MySQL connection handler
├── engine.py               # Rule execution logic
├── scheduler.py            # Background job scheduler
├── routers/                # FastAPI routes
│   ├── customers.py
│   ├── rules.py
│   ├── logs.py
│   └── tickets.py
├── dashboard/              # Streamlit app
│   └── dashboard.py
├── requirements.txt        # Python dependencies
├── .gitignore              # Ignore venv, cache, .env, etc
└── README.md               # Project overview

---

🧪 Tech Stack

• Backend: FastAPI, MySQL, SQLAlchemy
• Frontend: Streamlit, Altair, Pandas
• Scheduler: APScheduler
• Deployment: Render (or local via Uvicorn/Streamlit)

---

⚙️ Setup Instructions

1. Clone the Repo

git clone https://github.com/yourusername/customer-ops-simulator.git
cd customer-ops-simulator

2. Create Virtual Environment

python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows

3. Install Dependencies

pip install -r requirements.txt

4. Configure Database

• Ensure MySQL is running locally.
• Create database: customer_ops_simulator
• Update db.py with your DB credentials

5. Run Backend (FastAPI)

uvicorn main:app --reload

6. Run Dashboard (Streamlit)

streamlit run dashboard/dashboard.py

---

🌐 Deployment

Render Setup:

• Web Service 1 (FastAPI): main.py
• Web Service 2 (Streamlit): dashboard/dashboard.py
• Add requirements.txt, set ports to 10000/10001 respectively

---

📷 Screenshots

📅 Rule Executions Over Time

Displays the daily volume of rule executions. Highlights anomalies and trends in rule processing.

✅ Execution Success/Failure per Customer

Stacked bar chart showing how each customer performed (green = success, red = failure).

📊 Summary Table

Tabular view of execution metrics per customer including computed success rate.

📄 Raw Execution Logs

Full audit trail of rule executions by customer with timestamps and statuses.

🔍 Customer Drilldown (Filtered View)

Filtered insights per customer using name search and drop-down filters.

---

📄 Data Exports

• Execution logs are exportable as .csv
• Success/Failure summary available for reports

---

🤝 Author & Credits

Sahran Altaf
Data Science & Robotics @ Symbiosis Institute of Technology
GitHub: DarSahran

---

📜 License

MIT License. Free to use, improve, and distribute.