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Use of computer vision to provide metrics of student focus inside the class, with the implementation of Generative AI to create personalized lesson plans or content for sessions.

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EduVision: Power your teaching potential 📒

Created for LinceHacks 2024

Authors

Features

  • Optimized API costs using gpt4-o
  • Light and Dark mode enabled
  • Available in all devices

Classroom Attention

  • Use of computer vision tools to monitor sentiment analysis and attention levels of students
  • Fetch data on a dynamic dashboard to view different metrics
  • Mantain privacy of students using only tags and not saving biometric data
  • Generate reports and reccomendations in relation to the results from lessons
  • Identify in which section of the class the students where most engaged, and what was the content being displayed

Generate Content

  • Use of generative artificial intelligence to create content for your lessons
  • Direct conversation with an assistant to generate the desired content
  • Text integration and audios to increase dynamic interaction
  • Selection between creating a study plan or lesson content by filling a form
  • Visualization of desired topics and pptx creation

Structure

streamlit_app 
├─ home.py
├─ .streamlit
│   └─ secrets.toml
├─ algorithms
│  └─ embeedings.py
│  └─ mongodb_genai.py
│  └─ vectorization_mongo.py
├─ gpt4o
│  └─ image_process.py
│  └─ pptx_genai.py
│  └─ simple_text.py
├─ assets
│  └─ files
│  └─ images
├─ pages
│  └─ Chat.py
│  └─ instructions.py
│  └─ Panel.py
├─ utils
├─ AiManager.py
├─ DataManager.py
├─ index.html
├─ vision_main.py
└─ requirements.txt

Tools

  • OpenAI API
  • Streamlit
  • OpenCV
  • MongoDB
  • Threading
  • Queue
  • Deepface
  • pandas
  • numpy

Deployed with: Streamlit Cloud

Demo

YouTube

License

MIT

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Use of computer vision to provide metrics of student focus inside the class, with the implementation of Generative AI to create personalized lesson plans or content for sessions.

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