Email Automation & Reminder System

📌 Project Overview

This project implements a production-style Email Automation & Reminder System designed to automate reminder workflows, scheduled notifications, recurring alerts, and campaign-based communication pipelines using Python.

The objective is to simulate a real-world workflow automation platform used in domains such as:

• HR onboarding workflows
• Invoice reminder systems
• Webinar notification pipelines
• Operations automation
• Productivity engineering
• Business communication systems

The system follows a modular workflow where reminder data is:

→ Generated → Validated → Preprocessed → Scheduled → Processed by Worker Engines → Rendered using Jinja2 Templates → Delivered / Simulated → Logged & Reported → Visualized in Dashboard Analytics

This reflects industry-grade backend automation architectures used in workflow orchestration, scheduling systems, productivity platforms, and business process automation environments.

---

📊 Results & Outputs

This section summarizes the key outputs generated from the automation pipeline. All results are stored inside the outputs/ directory for reproducibility.

---

🔹 Synthetic Workflow Dataset Generation

Generated datasets:

• data/raw/contacts.csv
• data/raw/reminders.csv
• data/raw/campaigns.csv
• data/raw/templates.csv

These datasets simulate:

• Automated reminder workflows
• Campaign-based email pipelines
• HR onboarding notifications
• Webinar reminders
• Invoice follow-up systems

Key observations:

• Realistic business-style workflow simulation
• Scheduler-ready reminder pipelines
• Modular synthetic data generation
• Repeatable workflow testing environment

---

🔹 Data Preprocessing & Validation

Generated processed datasets:

• data/processed/cleaned_contacts.csv
• data/processed/processed_reminders.csv

The preprocessing pipeline performs:

• Email validation
• Duplicate removal
• Datetime validation
• Reminder normalization
• Workflow sanitization

This ensures:

• Scheduler stability
• Reliable downstream execution
• Production-style data hygiene
• Safe workflow processing

---

🔹 Dry-Run Workflow Execution

The system includes a secure dry-run execution mode that simulates:

• Reminder execution
• Email delivery workflows
• Worker processing
• SMTP execution
• Reporting pipelines

without sending real emails.

The execution flow follows:

Scheduler → Worker Engine → Jinja2 Template Rendering → SMTP Simulation → Logging → Report Generation

This functionality is useful for:

• Safe testing
• Local development
• GitHub demonstrations
• Portfolio showcasing
• Workflow debugging

---

🔹 Reporting & Analytics

Generated reports:

• outputs/reports/sent_report.csv
• outputs/reports/failed_report.csv
• outputs/reports/delivery_status.csv
• outputs/reports/campaign_summary.csv
• outputs/reports/reminder_summary.csv

Generated Excel export:

• outputs/exports/email_activity_export.xlsx

The reporting pipeline provides:

• Delivery tracking
• Workflow analytics
• Campaign summaries
• Reminder execution insights
• Failure monitoring

---

🔹 Logging & Observability

Generated logs:

• logs/app.log
• logs/scheduler.log
• logs/sent_emails.log
• logs/failed_emails.log

The logging layer tracks:

• Scheduler execution
• Reminder processing
• Workflow failures
• SMTP events
• Operational diagnostics

---

🔹 Premium Streamlit Dashboard

The project includes a Power BI–style Streamlit dashboard for workflow monitoring and operational analytics.

Dashboard capabilities:

• KPI cards
• Delivery monitoring
• Campaign analytics
• Reminder intelligence
• Workflow health tracking
• Log inspection
• Interactive Plotly visualizations

📊 Dashboard Preview

---

🛠 Tools & Technologies

• Python
• Pandas
• SQLite
• APScheduler
• smtplib
• Jinja2
• Streamlit
• Plotly
• openpyxl
• email-validator

---

▶️ How to Run

1. Clone or Download the Project

2. Install Dependencies

pip install -r requirements.txt

3. Configure Environment Variables

Create a .env file:

SMTP_EMAIL=your_email@example.com
SMTP_PASSWORD=your_password
SMTP_HOST=smtp.gmail.com
SMTP_PORT=587
DRY_RUN=True

4. Run Complete Automation Pipeline

python main.py

5. Launch Streamlit Dashboard

streamlit run app/streamlit_app.py

---

📂 Project Structure

Email-Automation-Reminder-System/
│
├── app/
│   ├── streamlit_app.py
│   └── dashboard_utils.py
│
├── config/
│   ├── settings.py
│   └── logging_config.yaml
│
├── data/
│   ├── raw/
│   ├── processed/
│   └── README.md
│
├── db/
│   └── email_automation.db
│
├── logs/
│
├── outputs/
│   ├── reports/
│   ├── exports/
│   └── screenshots/
│
├── src/
│   ├── utils.py
│   ├── validators.py
│   ├── logger_config.py
│   ├── database.py
│   ├── csv_handler.py
│   ├── preprocess.py
│   ├── data_generator.py
│   ├── template_engine.py
│   ├── email_sender.py
│   ├── dry_run.py
│   ├── worker.py
│   ├── scheduler_engine.py
│   └── report_generator.py
│
├── templates/
│
├── .env
├── .env.example
├── .gitignore
├── main.py
├── requirements.txt
├── README.md
├── LICENSE
└── architecture.txt

---

📌 Key Highlights

• Modular backend automation pipeline
• Scheduler → Worker execution architecture
• Jinja2-based dynamic email rendering
• Safe dry-run workflow simulation
• SQLite workflow persistence
• Reporting & observability system
• Premium Power BI–style dashboard
• Production-style folder organization

---

📌 Author

Akash Das