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.NET Chatbot and Wine Recognizer app

Home > Portfolios > High School > .NET Chatbot and...

1. .NET Chatbot with Corebot and Azure AI CLU

Description:
For our .NET Advanced course in high school, we developed a chatbot designed to simulate the buying and selling of shares and provide market insights. This project allowed us to explore the capabilities of Azure AI and Corebot frameworks. The chatbot operates within Microsoft Teams, creating a seamless user experience for interacting with market data.

Key Features:

  • Natural Language Processing: Implemented with Azure AI Cognitive Language Understanding (CLU), enabling the bot to understand and respond to user queries effectively.
  • Market Simulation: Supports commands for buying and selling shares, retrieving stock prices, and tracking portfolio performance.
  • Azure Hosting: The database, API, and chatbot are fully hosted in Azure, ensuring high availability and scalability.
  • Integration with Teams: The bot was deployed in Microsoft Teams, offering users a familiar and accessible platform for financial simulations.

Technologies Used:

  • .NET Corebot framework
  • Azure AI Cognitive Services (CLU)
  • Azure App Services and SQL Database
  • Microsoft Teams Bot Integration

Learning Outcomes:
This project enhanced our skills in cloud-based development, AI integration, and bot frameworks. It also provided valuable experience in building scalable, enterprise-grade solutions.

2. Wine Recognizer App with MVVM and MAUI

Description:
We also developed a Wine Recognizer app using .NET MAUI and the MVVM architectural pattern. The app allows users to identify wines based on labels or characteristics and provides details such as grape variety, region, and pairing suggestions.

Key Features:

  • Label Recognition: Utilized image recognition to identify wine labels and retrieve corresponding data from a centralized database.
  • Comprehensive Wine Database: Integrated a database with information on various wines, including flavor profiles, regions, and pairings.
  • User-Friendly Interface: The app was designed with .NET MAUI, ensuring cross-platform compatibility and a modern user interface.
  • MVVM Architecture: Followed the Model-View-ViewModel pattern to ensure clean separation of logic, making the app maintainable and scalable.

Technologies Used:

  • .NET MAUI for cross-platform app development
  • SQLite for the local wine database
  • MVVM for app architecture
  • Image recognition APIs for label identification

Learning Outcomes:
This project provided hands-on experience with .NET MAUI and the MVVM model, deepening our understanding of cross-platform development and app design principles. It also improved our skills in integrating APIs and managing local databases.

Summary

Through these projects, we gained comprehensive knowledge of .NET development, cloud integration, AI technologies, and cross-platform app development. These experiences have equipped us with the skills to build innovative and scalable solutions, showcasing our potential as aspiring developers.

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