/husky-ai

Primary LanguageTypeScriptMIT LicenseMIT

Husky AI

Husky AI Documentation

Overview

There are now many ways to understand a topic because data and courses are developed quickly these days. Considering that the user's required level of knowledge is not specified by a curated list. In order to address this, we developed an AI web application that responds to user prompts about the courses and subjects they wish to learn. It then creates a personalized course that includes YouTube videos for support, a concept checker, and a chatbot to help with any concepts that may have been overlooked during one of the units.

When students wish to apply for jobs and are prompted to upload a cover letter during the application process, this presents another challenge. We developed a tool that can automatically generate a unique cover letter and a linked in coffee message based on the resume and job description that are attached, saving time and effort over producing a fresh cover letter for each application. The student's application process for jobs is streamlined as a result.

Our Husky AI Platform majorly provides assistance with three parts of a students or a learners career:

  1. The user can curate a custom course by prompting the topic and the units they want to learn
  2. Go through the video lectures from the curated course and take a quiz that is curated based on the topic
  3. Interact with a chatbot to check with any questions you have
  4. Curate a custom cover letter and linkedin coffee message to interact and connect with people on various social platforms.

YT Link : https://www.youtube.com/watch?v=nwFuxuBZW-o

Technologies used:

The application is built based on the following technologies.

  1. Next.Js
  2. Open.ai whisper api
  3. Vertex api
  4. Planetscale
  5. Pinecone
  6. PlanetScale
  7. Langchain
  8. Prisma
  9. zod

Getting Started

  1. Prerequisites

    • Node.js (version specified in package.json or .nvmrc)
    • npm (comes with Node.js)
    • A supported database installed and running (Prisma configuration suggests...)

  2. Installation

    # Clone the repository
git clone <repository-url>

# Navigate into the project directory
cd husky-ai

# Install dependencies
npm install

# Setup and migrate your database (if using Prisma)
npx prisma migrate dev

# Start the development server
npm run dev

    This will start the development server, usually accessible at http://localhost:3000.

Project Structure

  • src/: Contains the source code of the application.
    • app/: Core application logic and entry points.
    • components/: Reusable UI components.
    • lib/: Shared libraries and utilities.
    • validators/: Validation logic for data inputs.
  • prisma/: Contains Prisma schema file(s) for database models and migrations.
  • public/: Static files like images and icons.
  • next.config.js & tailwind.config.js: Configuration files for Next.js and TailwindCSS, respectively.

Development Guidelines

  • Component Development: When developing new components, place them in the src/components directory. Use TailwindCSS for styling according to the project's design system.
  • Database Management: Use Prisma CLI for creating models and handling migrations. The schema.prisma file within the prisma directory defines your database schema.
  • Styling: This project uses TailwindCSS for styling. Define custom styles within component classes and extend Tailwind's config in tailwind.config.js as needed.

Modules Overview

1. app

This module seems to be the core of the application, containing key functional components and utilities:

  • settings, course, danjob, gallery, create: These directories likely contain pages or components related to specific features or sections of the application (e.g., course management, job listings, image galleries).
  • api: This directory might contain API route definitions used by the Next.js application for handling HTTP requests.
  • layout.tsx: A React component for the application's layout, possibly used as a wrapper for pages to include common UI elements like headers or footers.
  • ssspage.tsx, page.tsx: These components could be templates or specific implementations of server-side rendered and client-side pages.
  • globals.css: A stylesheet for global CSS rules applied throughout the application.

2. components

The components module contains reusable React components for the UI:

  • ui: A directory that might contain more granular UI components or utilities.
  • Components like Navbar.tsx, GalleryCourseCard.tsx, ChapterCard.tsx, SubscriptionButton.tsx, etc., suggest a variety of UI elements designed for navigation, displaying course information, user interactions, and more.

3. lib

This module likely contains library functions and utilities supporting the application's backend logic or integrations:

  • gpt.ts, stripe.ts, youtube.ts, unsplash.ts: Integration utilities with external services like GPT (presumably for AI text generation), Stripe (for payments), YouTube (for video content), and Unsplash (for images).
  • utils.ts, db.ts, auth.ts, subscription.ts: General utilities for database operations, authentication, and subscription management.

4. validators

Contains validation logic for the application:

  • course.ts: This file suggests validation rules or functions related to course data, ensuring that inputs or data modifications adhere to expected formats and rules.