EduAssist Hub (Work in Progress 🚧)

EduAssist Hub is a comprehensive educational platform that leverages cutting-edge technologies like Natural Language Processing (NLP) and Computer Vision (CV) to streamline the learning experience for students. Designed for exam preparation and active learning, the platform simplifies interactions with study materials, quizzes, and videos.

🌟 Key Features

1️⃣ StudyEase 📚

Seamless PDF navigation with RAG (Retrieval-Augmented Generation) integration, allowing students to query their PDF content directly.

• No need for back-and-forth between screens—just ask questions from your PDFs and get instant answers.
• Improves efficiency by focusing only on relevant content.

from langchain.chains import RetrievalQA
# Example usage of RAG for PDFs
rag_chain = RetrievalQA()
response = rag_chain.run("What is the summary of this document?")
print(response)

2️⃣ PDF to QnA ✍️

Automatically generates both objective and subjective questions from PDFs using NLP.

• Great for self-assessment and quick revision before exams.
• Saves time by creating structured study material from raw notes.

from langchain.chains import QAGenerationChain
# Example usage of QnA generator
qa_chain = QAGenerationChain()
questions = qa_chain.generate_questions("Sample PDF text here")
print(questions)

3️⃣ PDF to Quiz with Headpose Detection 🎯

• Uses NLP to create quizzes from study materials.
• Computer Vision (CV) detects head movements to ensure focus during quiz attempts.
• Helps students stay engaged and prevents distractions.

import cv2
# Example usage of head pose detection
cap = cv2.VideoCapture(0)
while True:
    ret, frame = cap.read()
    if not ret:
        break
    cv2.imshow('Head Pose Detection', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

4️⃣ RAG Agent for Video Learning 🎥

• Interact with educational videos using Retrieval-Augmented Generation (RAG).
• Ask questions while watching and get real-time responses.
• Enhances interactive learning and deepens comprehension.

from langchain.video import VideoQA
# Example usage of RAG for videos
video_qa = VideoQA("educational_video.mp4")
response = video_qa.ask("Explain this topic in simple terms.")
print(response)

🔨 Work in Progress 🚧

We are continuously improving EduAssist Hub to provide an even more enhanced learning experience. Here's what we are currently working on:

🚀 Upcoming Features:

• RAG Agent Video Chat: Allowing real-time Q&A interaction directly with videos.

• Real-time Text Extraction from Videos: Extract and display text content inside videos for instant feedback.

• RAG Agent Video Chat: Expanding the RAG agent functionality to allow for dynamic question-answer interaction directly with videos. Students will be able to ask questions while watching the video, and the system will respond in real-time.

• Real-time Text Extraction from Videos: developing a feature to extract and display text content inside videos in real-time. This will further assist students in getting immediate feedback from video learning materials.

🏗️ Architecture Diagram

Below is the architecture of EduAssist Hub, illustrating the data flow across various modules.

💻 Tech Stack

• Backend: Python, Flask
• Frontend: HTML, CSS, JavaScript (Bootstrap, Font Awesome)
• Technologies Used:
  • NLP for question generation and content querying
  • Computer Vision (CV) for headpose detection during quizzes
  • RAG using LangChain for video content interaction

🚀 Usage

1️⃣ StudyEase: Navigate PDFs easily and query directly from the content.
2️⃣ PDF to QnA: Upload a PDF, and generate objective & subjective questions.
3️⃣ PDF to Quiz: Create AI-powered quizzes with headpose tracking.
4️⃣ RAG Agent: Interact with video content through smart Q&A.

📢 Join the AI Education Revolution!

EduAssist Hub is evolving into an AI-powered learning assistant! Stay tuned for more updates. 🎓🚀

#AI #AgenticAI #LLM #EdTech #NLP #RAG #AIInnovation #AIForEducation