Vibemeter: Employee Engagement Platform

Vibemeter is a comprehensive employee engagement platform that collects, analyzes, and visualizes employee sentiment data through interactive chatbot conversations and surveys. It empowers HR teams to monitor engagement trends, track employee mood, and take timely action when needed.

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Features

• Smart Chatbot:
  AI-powered conversation analysis to understand employee sentiment.

• HR Dashboard:
  Comprehensive view of organization-wide engagement metrics.

• Analytics:
  Data visualization of employee engagement trends.

• Sentiment Tracking:
  Monitors employee mood and satisfaction levels.

• Escalation System:
  Alerts HR teams when employees need additional support.

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Backend Setup

Prerequisites

• Python 3.10 or higher
• PostgreSQL
• Git

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Installation

1. Clone the Repository:

   git clone https://github.com/LAL-BAHADUR-SHASTRI/Opensoft-2025
   cd vibemeter

2. Create and Activate a Virtual Environment:

   python -m venv venv
   source venv/bin/activate  # On macOS/Linux
   venv\Scripts\activate     # On Windows

3. Install Dependencies:

   pip install -r requirements.txt

Database Setup

Local Development Database

1. Create a PostgreSQL Database.
2. Configure your environment variables in a .env file in the backend directory with your database credentials and other settings.

Docker Database Setup

If using Docker, run the following command to start your PostgreSQL container along with the backend:

docker-compose up -d db

Database Initialization

Run the initialization script to create all tables and add default users:

• Creates all database tables based on SQLAlchemy models.
• Adds an HR user: username: hruser, password: hruser.
• Adds a sample employee: username: emp0048, password: emp0048.

python init_db.py

Reset Database

Warning: This deletes all data.
To completely reset the database, run the reset script provided.

python reset_db.py

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Modal Deployment (ChatBot AI Deployment)

Modal is a cloud execution environment that allows you to run Python code at scale without worrying about infrastructure.

📦 Installation

1. Install Modal CLI and SDK:

   pip install modal

2. Authenticate with Modal:

   modal token new

   Follow the browser link to sign in with your GitHub or Google account.

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🚀 Deploy Chatbot Code

To deploy the chatbot code to Modal:

modal deploy Backend/app/chatbot/chatbot.py

You’ll receive a deployment URL or function ID that you can invoke from your FastAPI backend (e.g. from /start_chat or /chat routes). Insert that URLs in the .env

(Optional) You can test the chatbot function locally using:

modal run Backend/app/chatbot/chatbot.py

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Running the Application

Backend

• Development Mode:
  Run your application in development mode with hot reloading.

• Production Mode:
  The application runs with 4 worker processes to handle simultaneous connections.

Frontend Setup

Prerequisites

• Node.js 20.x or later
• npm or yarn

Installation

1. Navigate to the Frontend directory.
2. Install dependencies:

   npm install --force

Running the Frontend

• Development Mode:
  Start the development server:

  npm run dev

• Production Build:
  Build the frontend for production:

  npm run build

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Docker Deployment

Both the frontend and backend can be deployed using Docker. Use the provided docker-compose.yml file to start:

• PostgreSQL database
• Backend API
• Frontend application

docker-compose up --build

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API Endpoints

Authentication

• POST /token
  Login to get access token.

• POST /logout
  Logout and clear authentication cookie.

• GET /users/me
  Retrieve current user information.

Data Management

• POST /upload-csv/
  Upload CSV files (HR role required).

• GET /jobs/{job_id}
  Check status of a background job.

• GET /jobs/
  Get status of all background jobs.

• POST /users/create-async/
  Create user accounts from employee data (async).

• GET /tables/
  Get list of all tables and record counts.

• GET /data/{table_name}
  Get data from a specific table with pagination.

• GET /employee/data
  Get data for the logged-in employee.

Reporting

• GET /report/collective
  Generate a collective organizational report.

• POST /report/employee
  Generate an individual employee report.

• POST /report/selective
  Generate a report for selected employees.

Chat

• POST /start_chat
  Start a new chatbot session.

• POST /chat
  Send a message and get the next question or final analysis.

• GET /chathistory
  Get chat history for an employee.

• GET /chatdates
  Get dates when an employee had chatbot conversations.

HR Management

• POST /hr/resolve-escalation/{employee_id}
  Clear HR escalation flag for a specific employee.

• GET /hr/employees/need-attention
  Get a list of employees requiring HR attention.

CSV Data Import

The system supports importing data from CSV files.

1. Log in as an HR user.
2. Use the POST /upload-csv/ endpoint to upload CSV files.
3. After upload, user accounts are automatically created for all employee IDs.

Supported CSV files:

• activity_tracker_dataset.csv
• leave_dataset.csv
• onboarding_dataset.csv
• performance_dataset.csv
• rewards_dataset.csv
• vibemeter_dataset.csv

Sample CSV files are available in the data directory.

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Environment Variables

Create a .env file in the Backend directory with your configuration variables. For the Frontend, create a .env file in the Frontend directory with the required settings.

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Chatbot System

The application includes an AI-powered chatbot that:

• Conducts sentiment analysis on employee responses.
• Identifies key themes and concerns.
• Provides HR with actionable insights.
• Flags employees who may need additional support.
• Maintains complete conversation history for reference.

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Troubleshooting

Database Connection Issues

• Ensure PostgreSQL is running.
• Verify database credentials in .env.
• If using Docker, ensure the db service is running.

CSV Upload Issues

• Ensure the CSV headers match the expected format.
• Verify that the HR user has the correct permissions.
• Check the logs for detailed error messages.

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License

This project is licensed under the MIT License.