This repository contains the code for a personalized learning chatbot designed to guide users through various programming courses. The chatbot leverages Rasa for conversational AI and Streamlit for the interactive user interface.
The chatbot provides information about different programming courses, allows users to simulate enrollment, track their progress, and even take simple quizzes.
Key Features:
- Course Discovery: Users can ask about specific topics (e.g., "web development", "Python") to view relevant courses and materials.
- Free Access Links: It provides free access links to external resources where users can learn about their specific area of interest.
- Simulated Enrollment: Users can simulate enrolling in a course, which makes a note of it and can be displayed in a list.
- Module Completion: It allows for simulated module completion tracking with visual feedback, which helps users visualize the learning path.
- Simple Quizzes: Users can test their knowledge with basic multiple choice quizzes after they are enrolled in a course.
- Interactive UI: The application is using Streamlit for a user-friendly chat interface with an animated floating window for the "About" section.
- Rasa Powered: The chatbot conversation logic is powered by Rasa, an open-source conversational AI framework.
- Python: Primary language for the application and chatbot logic.
- Streamlit: For the interactive user interface.
- Rasa: For conversational AI and chatbot handling.
- Git: For version control and code management.
- Google Workspace For the logo for the project.
- JSON: For handling data files.
- Vecteezy: Used for the background image for the application
Use code with caution. Markdown your_project_directory/ ├── actions.py # Custom Rasa action code ├── app.py # Streamlit application code ├── style.css # Custom CSS style definitions ├── static/ | └── styles.css # Custom CSS style definitions ├── utils/ | └── lms_utils.py # Manages the JSON file for persistence ├── data/ # Rasa training data │ └── nlu.yml # Training data for NLU │ └── stories.yml # Defines conversation flows │ └── rules.yml # Rules file for bot behavior └── domain.yml # Rasa domain file
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Install Git: If you haven't already, install Git on your local machine using these instructions: https://git-scm.com/downloads
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Clone the Repository: Clone the repository to your local machine using the command:
git clone [YOUR_GITHUB_REPOSITORY_URL]
(Replace
[YOUR_GITHUB_REPOSITORY_URL]with the URL of your repository.) -
Navigate to the Project Folder: Go to your project directory:
cd your_project_directory -
Install Dependencies: Install the necessary Python packages using pip:
pip install streamlit requests rasa rasa-sdk
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Train your Rasa model: Run the following command to train the rasa model, and it should pick up all the changes made.
rasa train
Use code with caution. Running the Application
Start the Rasa Action Server: Open a new terminal and navigate to your project directory. Start the Rasa action server with:
rasa run actions Use code with caution. Bash Start the Rasa server: Open another terminal window, and start the Rasa server with:
rasa run Use code with caution. Bash Start the Streamlit App: Open a new terminal and navigate to your project directory, then start the Streamlit app with:
streamlit run app.py Use code with caution. Bash This will launch the application in your default web browser.
Using the Chatbot
Initial Greeting: The bot will display the welcome message with all available courses.
Course Discovery: Use the chat input to ask about a specific topic. E.g. "tell me about web development", "start learning python", etc.
Enrollment: Click the Enroll button to simulate enrollment in the course.
Module Completion: You can click on the module buttons to see the information about each module, and mark modules as complete by clicking the "Mark as Complete" button.
View Enrolled Courses: Type "show my courses" to see a list of your enrolled courses and their current status, as well as the list of completed modules for each course.
Start Quizzes: Click on "Start Quiz" button to start the quiz for the enrolled course.
About Window: Click on the "About Learning Assistant" button to view the information about the chat bot in the floating window.
Provide Feedback: Feel free to use keywords like "thanks", "great", "good", etc. to give feedback to the bot.
Future Improvements (Optional)
Persistent User Data: Implement a database or API for saving user progress and data across sessions.
User Authentication: Add a login system so each user can have their own progress tracking.
External APIs: Integrate with learning platforms APIs (like Coursera, Udemy) for real-time progress tracking.
Improved Quizzes: Make the quizzes more comprehensive and include different types of questions.
More Courses: Increase the list of courses and add more details to them, including detailed syllabus, module content and learning objectives.
Dynamic Content: Fetch the courses from external resources so that the content is dynamic and you do not need to redeploy every time you need to add a course.
Credits
This project uses the following open-source libraries: Streamlit, Rasa.
The background image used in this application was downloaded from Vecteezy
License
This project is under MIT License
Contribute
If you want to contribute to this project, you can open a pull request and I will review and merge it after reviewing the codes.