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🚗 EcoCAR EV Onboarding & Automation

📌 Overview

This repository is designed to automate the onboarding and offboarding processes for the EcoCAR EV project using Python, Airflow, and Google APIs. The automation reduces manual effort by 50%, enhances secure communication using Google OAuth2 & Gmail API, and streamlines workflows hosted on GCP.

This system consists of three main components:

1. Leader Approval System – Automates the acceptance/rejection of new members.
2. Automated Email Notifications – Uses Gmail API to send structured onboarding emails.
3. Google Sheets Integration – Manages and updates the team’s headcount automatically.

Additionally, Airflow has been implemented to fully automate the scheduling and execution of onboarding tasks.

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🏆 Technical Highlights

✔️ Automated onboarding/offboarding using Python & Airflow, reducing manual tasks by 50%.
✔️ Integrated Google OAuth2 & Gmail API for secure communication & event scheduling.
✔️ Deployed automation on Google Cloud Platform (GCP) for efficiency & scalability.
✔️ Airflow DAG manages automated workflow execution.

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📂 Project Structure

📂 EcoCAR
│── 📂 Ask Leader to Accept or Decline
│   │── .env                          # Environment variables
│   │── AcceptOrDeclineReq.ipynb       # Script for leader decision automation
│   │── client_secret.json             # OAuth2 client secret for Google API
│   │── headcount.csv                   # Team member data for onboarding
│   │── testing-for-ecocar-ff6b5e5bbe13.json  # Google API service credentials
│   │── token.json                      # OAuth2 authentication token
│   │── token.pickle                    # OAuth2 session persistence
│
│── 📂 Automated Message
│   │── .env                           # Environment variables
│   │── client_secret_171250538774.json # Google API credentials
│   │── email.ipynb                     # Automated email notification script
│   │── token.json                      # OAuth2 authentication token
│
│── 📂 Read Write Google Sheet
│   │── .env                            # Environment variables
│   │── headcount.csv                    # Team headcount data
│   │── test.ipynb                       # Google Sheets data manipulation
│   │── testing-for-ecocar-ff6b5e5bbe13.json # Google API credentials
│
│── 📂 Streamlit
│   │── Konsing Streamlit Test.zip       # Streamlit UI test archive
│   │── photo.jpg                        # UI screenshot
│
│── 📂 Team Repository \ EcoCAR-Onboarding
│   │── .env                             # Environment variables
│   │── client_secret.json               # OAuth2 client credentials
│   │── headcount.csv                     # Primary team data file
│   │── service_credentials.json         # Google API service credentials
│   │── TestingForm.ipynb                 # Form validation notebook
│   │── token.pickle                      # OAuth2 session persistence
│
│── 📂 W’s Alt EcoCAR
│   │── credentials.json                 # Alternative Google API credentials
│   │── headcount.csv                     # Backup team data file
│   │── read.py                           # Data retrieval script
│   │── write.py                          # Data update script
│
│── .gitignore                           # Ignores sensitive files
│── Airflow_DAG_ecocar_onboarding.py     # 🟢 Airflow DAG for automated workflow execution
│── main.py                              # Main script to orchestrate onboarding tasks
│── README.md                            # Project documentation

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🛠️ Installation & Setup Guide

🔑 Prerequisites

Ensure you have:

• Python 3.x installed.
• Required Python libraries (pandas, requests, google-auth, etc.).
• Google API Access with OAuth2 credentials.
• Apache Airflow installed and configured.

🚀 Setup Instructions

1️⃣ Clone the Repository

git clone https://github.com/your-repo/EcoCAR-Onboarding.git
cd EcoCAR-Onboarding

2️⃣ Install Required Dependencies

pip install -r requirements.txt

3️⃣ Set Up Environment Variables

• Create a .env file and add necessary API keys or credentials:

  API_KEY=your_api_key_here
  

4️⃣ Run the Main Onboarding Script

python main.py

5️⃣ Start Jupyter Notebook for Form Testing

jupyter notebook TestingForm.ipynb

6️⃣ Deploy & Run the Airflow DAG (Locally)

• Move the DAG file to Airflow’s DAGs directory:

  mv Airflow_DAG_ecocar_onboarding.py ~/airflow/dags/

• Restart the Airflow Scheduler:

  airflow scheduler

• Start the Airflow Web UI:

  airflow webserver --port 8080

• Open http://localhost:8080 and enable the DAG.

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📊 How Each Component Works

1️⃣ Leader Approval System (Ask Leader to Accept or Decline)

• Sends automated accept/reject requests to team leaders.
• Records responses in headcount.csv for tracking.

2️⃣ Automated Email Notifications (Automated Message)

• Uses Gmail API to send structured onboarding emails.
• Supports dynamic placeholders for personalized messages.

3️⃣ Google Sheets Data Handling (Read Write Google Sheet)

• Reads & writes team headcount using Google Sheets API.
• Ensures real-time updates for team member tracking.

4️⃣ Airflow DAG (Airflow_DAG_ecocar_onboarding.py)

• Automates the onboarding pipeline:
  1. Checks for new onboarding requests.
  2. Emails team leaders for approval.
  3. Processes leader responses (Approve/Reject).
  4. Sends onboarding emails to accepted members.
  5. Updates Google Sheets with final decisions.

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🔍 Purpose of Key Files

TestingForm.ipynb

• Jupyter Notebook designed for validating and testing form data.
• Processes user responses, detects errors, and ensures data consistency.

client_secret.json & service_credentials.json

• Stores Google API credentials for authentication.
• Used for OAuth2 authentication & service-based API access.

.gitignore

• Prevents sensitive files (.env, token.json, .pickle) from being committed.

main.py

• The central automation script that:
  • Handles onboarding/offboarding.
  • Automates leader decisions.
  • Sends email notifications.

Airflow_DAG_ecocar_onboarding.py

• Airflow DAG that fully automates the onboarding pipeline.

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🚀 Future Enhancements

🔹 Auto-sync with Google Calendar – Schedule onboarding meetings dynamically.
🔹 AI-Powered Form Processing – Use NLP to analyze responses for anomalies.

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📜 License

This project is licensed under the MIT License.

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