🤖 Chatbot for Personalized Learning

📝 Project Description

The Chatbot for Personalized Learning is an AI-powered assistant designed to enhance the learning experience. Using advanced machine learning models and APIs, the chatbot:

1. Generates content based on user queries
2. Recommends YouTube videos relevant to the topic

This chatbot aims to make learning interactive, engaging, and tailored to the user's needs.

⭐ Features

• 🧠 Content Generation: Utilizes Hugging Face's google/flan-t5-large model for generating detailed and accurate responses to user queries
• 🎥 Video Recommendations: Integrates YouTube API to provide curated video recommendations based on the user's query
• 🎯 Personalized Learning: Delivers customized educational resources and guidance tailored to individual users' needs
• 🔄 Seamless LMS Integration: Interacts with external educational repositories to provide topic-specific content
• 💡 NLU-Driven Interactions: Uses advanced Natural Language Understanding techniques for intelligent conversations

🛠️ Tech Stack

• 🐍 Language: Python
• 🤖 Framework: RASA for chatbot development
• 🧪 Machine Learning: Hugging Face Transformers (google/flan-t5-large)
• 📺 APIs: YouTube Data API v3
• 💻 IDE: Visual Studio Code
• 🚀 Frontend: Streamlit

📦 Modules Implemented

1. Intent Recognition: Advanced NLU for understanding user queries
2. Dialogue Management: Context-aware conversation handling
3. LMS Integration: Seamless connection with educational repositories
4. Content Generation: AI-powered response generation
5. Video Recommendation: Smart YouTube content curation

⚙️ Setup Instructions

Prerequisites

• Python 3.8+
• Virtual Environment
• API Keys:
  • YouTube Data API key

Installation Steps

1. Clone the repository:

git clone https://github.com/vermaaatul07/Chatbot_for_Personalized_Learning.git
cd Chatbot-for-Personalized-Learning

2. Create and activate virtual environment:

python -m venv venv
# For Windows
venv\Scripts\activate
# For Unix/MacOS
source venv/bin/activate

3. Install dependencies:

pip install -r requirements.txt

4. Configure API keys: Create a .env file in the root directory:

YOUTUBE_API_KEY=your_key_here

5. Train RASA model:

rasa train

🚀 Deployment

Local Deployment with Streamlit

1. Install Streamlit:

pip install streamlit

2. Run the Streamlit app:

# Start RASA server
rasa run --enable-api --cors "*" --port 5005

# In a new terminal, start RASA actions
rasa run actions

# In another terminal, run Streamlit
streamlit run app.py

The application will be available at http://localhost:8501

🌐 Deployment Options

• Local: Run using Streamlit for development
• Cloud: Deploy to Streamlit Cloud for production
• Docker: Containerization available for scalable deployment

🎮 Usage Guide

1. Starting a Conversation:

   • Launch the Streamlit interface
   • Type your question in the chat input
   • Press Enter or click Send

2. Getting Responses:

   • View AI-generated content
   • Explore recommended YouTube videos
   • Follow up with related questions

3. Example Interactions:

User: "Explain quantum computing"
Bot: *Generates detailed explanation*
Bot: "Would you like to see some video resources?"
User: "Yes"
Bot: *Provides relevant YouTube links*

Project Screenshots

Image 1

Image 2

Image 3

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.