This project aims to develop a web application that provides personalized recipe recommendations. It uses a combination of Next.js with TypeScript for the frontend, FastAPI for the backend, and AWS services including DynamoDB for database management and Amplify for frontend hosting.
- Frontend: Next.js with TypeScript
- Backend: FastAPI
- Database: AWS DynamoDB
- Hosting: AWS Amplify for the frontend, AWS Elastic Beanstalk for the backend
- Machine Learning: Python (Scikit-Learn, TensorFlow, or PyTorch)
- Authentication: AWS Cognito
- Node.js and npm
- Python 3.x
- AWS Account
- AWS CLI
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Clone the repository
git clone https://github.com/thetajwar2003/khuda-lagche.git cd khuda-lagche
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Install dependencies
Frontend:
cd frontend npm install
Backend:
cd backend pip install -r requirements.txt
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Configure AWS
Set up AWS credentials and configure the AWS CLI with your account.
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Set up DynamoDB
Create the necessary tables in DynamoDB.
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Environment Variables
Set up the required environment variables for both frontend and backend.
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Start the Frontend
cd frontend npm run dev
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Start the Backend
cd backend uvicorn main:app --reload
- User account management
- Recipe searching and browsing
- Recipe rating and saving
- Responsive UI
- RESTful API for user and recipe management
- Recipe recommendation engine
- Secure user data handling
- Personalized recipe recommendations
- Continuous learning and adaptation
- Frontend: Deploy on AWS Amplify
- Backend: Host on AWS Elastic Beanstalk