WPI_Hackher413

Quicksend your resume

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Multi-Project Repository

This repository contains two distinct projects:

  1. QuickSend AI – https://quick-send.co.in An AI-powered application that helps job seekers create personalized emails, cover letters, and manage resumes.
  2. VirtualDress – A virtual fitting room application that creates a 2D model of the user and overlays recommended clothing items.

Table of Contents

QuickSend AI

QuickSend AI is a powerful application that leverages AI technology to generate professional emails and cover letters for job seekers. It integrates with Firebase for secure authentication, data storage, and resume management.

Features

  • 🤖 AI-Powered Generation: Create personalized emails and cover letters.
  • ✍ Cover Letter Creation
  • 📄 Resume Upload & Management
  • 🔍 Resume Search Functionality
  • 💰 Subscription-Based Pricing
  • 🔒 Secure Authentication
  • 💾 Cloud Storage Integration

Prerequisites

Before you begin, ensure you have installed:

  • Node.js (v18 or higher)
  • npm (v9 or higher)
  • Python
  • Flask
  • GenAI

Installation & Setup

  1. Install Dependencies
   npm install
  1. Set up Firebase
  • Create a project at the Firebase Console.
  • Enable Authentication, Firestore, and Storage.
  • Copy your Firebase configuration.
  1. Configure Environment Variables

Create an .env file in the project root with:

   VITE_FIREBASE_API_KEY=your_api_key
   VITE_FIREBASE_AUTH_DOMAIN=your_auth_domain
   VITE_FIREBASE_PROJECT_ID=your_project_id
   VITE_FIREBASE_STORAGE_BUCKET=your_storage_bucket
   VITE_FIREBASE_MESSAGING_SENDER_ID=your_messaging_sender_id
   VITE_FIREBASE_APP_ID=your_app_id
  1. Start the Development Server
   npm run dev

Project Structure

src/
├── components/     # Reusable UI components
├── hooks/          # Custom React hooks
├── lib/            # Utility functions and configurations
├── pages/          # Application pages/routes
└── main.tsx        # Application entry point

Key Components

  • AppBar: Main navigation component.
  • AuthLayout: Layout wrapper for authentication pages.
  • Button: Reusable button component.
  • Input: Form input component.
  • ResumeUpload: Handles resume uploads.
  • EmailTypeSelector: Allows selection of email formats.
  • GeneratedEmail: Displays the generated email content.

Authentication & API Integration

  • Firebase Authentication: Supports email/password sign-in, registration, login, password reset, and session management.
  • API Endpoints: The app integrates with various APIs for resume and AI-based email generation (details can be expanded as needed).

Development Scripts

# Start development server
npm run dev

# Build for production
npm run build

# Preview production build
npm run preview

# Run linting
npm run lint

Contributing

  1. Fork the repository.
  2. Create a feature branch: git checkout -b feature/AmazingFeature
  3. Commit your changes: git commit -m 'Add some AmazingFeature'
  4. Push to the branch: git push origin feature/AmazingFeature
  5. Open a Pull Request.

License & Support

VirtualDress

VirtualDress is a virtual fitting room application that allows users to create a 2D virtual model by uploading a face image and entering personal measurements. The app processes the image using a Python + OpenCV microservice and recommends clothing items based on user input and AI analysis.

Features

  • User Input: Height, weight, and face image upload.
  • Automatic Processing: Calculates BMI, selects a body silhouette, and uses a Python + OpenCV microservice to process the face image.
  • Clothing Recommendations: Fetches items from MongoDB and optionally uses AI (e.g., Gemini) for outfit suggestions.
  • 2D Virtual Fitting: React frontend overlays clothing images on the base model.
  • Optional Checkout: "Buy" button to simulate a purchase flow.

Prerequisites

  • Node.js (v12 or later)
  • npm (comes with Node.js)
  • MongoDB (local instance or MongoDB Atlas)
  • Python (for the OpenCV microservice)
  • (Optional) Git

Installation

  1. Clone the Repository
   git clone <repository_url>
   cd virtualdress
  1. Install Node Dependencies
   npm install
  1. Configure the Backend

Set up your MongoDB connection and other backend configurations as required (refer to the backend documentation).

  1. Set Up the Python Microservice

Ensure you have Python and OpenCV installed. Follow the microservice-specific instructions (usually provided in a separate README or documentation).

Usage Overview

  • Input Data: Users enter their height, weight, and upload a face image.
  • Image Processing: The Python microservice processes the image to crop the face and overlay it onto a recommended silhouette.
  • Outfit Recommendation: The backend retrieves clothing recommendations from MongoDB (and may use AI for suggestions).
  • Virtual Fitting: The React frontend displays the assembled 2D model with recommended outfits.
  • Checkout (Optional): Users can simulate a purchase flow via the "Buy" button.

Future Work

  • Enhanced AI Integration: Improve outfit recommendations with advanced AI.
  • User Customization: Allow users to adjust body measurements and clothing fits.
  • Payment Integration: Implement a full checkout process for purchasing items.

Feel free to explore, contribute, and open issues for improvements or suggestions for either project. Happy coding!

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