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UROP: Telegram chatbot for novice programmers to learn coding. The deployed Streamlit Web Application for the Telegram Chatbot with Chatbase custom GPT LLM model API and Firebase, using Figma for prototyping of the web application.

uzkgcu6dkiqhyappnhnx5c9.streamlit.app/

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22.-Deployed-Streamlit-Web-Application-for-Telegram-Chatbot-with-Chatbase-custom-GPT-LLM-model-API-and-Firebase-with-Figma-for-prototyping My Skills My Skills My Skills

Remaining 2 issues before going to deployment and testing and documentation is:

  • Telegram button when streamlit website deletes or adds it don't appear/disappear immediately...
  • streamlit website refreshing, and cant really see the database for long

Tell prof this is my restricted version. If want to make a text version I think might be abit tricky ah... likely will want to just stop the UROP with this restricted version of the Telegram Chatbot cuz need to move on with studies

Next few month is just to find a time to deploy, test with prompt engineering, documentation of the project

for the code to look nicer in the streamlit website databade can add triple back quotes by default into the code submissions by the students:

JUST NEED TO SLOWLY CLEAN UP ALL THE FILES AND UPLOAD THEM HERE!! AND UPDATE WITH THE RELEVANT INFORMATION ABOUT THOSE FILES/FOLDERS!

ALSO MIGHT NEED TO SEPERATE ONLY THE NECESSARY FILES FOR DEPLOYMENT IN A SEPARATE GITHUB REPO IN ORDER TO PREVENT ERROR. CUZ ADDING EXTRA MISCELLANEUOUS FILES MAY CAUSE DEPLOYMENT ERROR!! (SAME FOR THE '18. KARAOKE EVENT STREAMLIT APP', WHICH NOW HAS ERRORS CUZ I ADDED EXTRA MISCELLANEUOUS FILES TO ITS GITHUB REPO... OOPS...)

maybe can put pic of the streanlit website, firebase, chatbase and figma too and the

plan to change it from transferable between courses to only works for one course... cuz to make it transferable between courses is too hard alr

List of features to add to the UROP Telegram chatbot (if can do all these then not bad alr!! No need try to do more cuz need focus on the other UROP and studies too 😫):

Schedule: August:

  • Try to deploy the Telegram Chatbot and Streamlit website (and firebase)
  • Allow teachers of a course to add (manually i guess?) assignments for the programming course (Maybe a seperate page of Programming Question and Test cases), and hopefully it shows up in the streamlit lists of assignment id for the programming course

September:

  • Try to get Telegram bot to instead do conversational with free text input, make it more like limited with text options and fill in the blanks style, with name, courseid, assignmentid and their answers (basically figure out how the entire conversation with the Telegram chatbot is gonna be like)
  • From a conversation with ChatGPT: Can Telegram bots be programmed to not give the user freedom of text but instead with premade options? ChatGPT said: ChatGPT Yes, Telegram bots can be programmed to offer users a limited set of options instead of allowing free text input. This can be achieved using inline keyboards, custom keyboards, or callback queries.

Inline Keyboards: These are buttons that appear within the chat and can trigger actions or responses when pressed. They don't require user input beyond clicking a button.

Custom Keyboards: These replace the default keyboard with a set of predefined buttons that users can select from.

Callback Queries: These are used with inline keyboards to handle button presses and provide appropriate responses based on the user’s selection.

By using these features, you can create a controlled interaction flow where users only choose from the given options.

  • Allow the teacher to customise behavior of the chatbot via the details page (or maybe don't even need this anymore? and details page will just be about the Telegram chatbot and website?)

October:

  • Testing of the Telegram chatbot and any other last changes to complete the project? (Plan to do less here due to examinations)

November:

  • Documentation for future improvements on the project (Plan to do less here due to examinations)

Bonus task for even more improvement: Allow teachers from other courses to add their own course + assignments with the respective ids + get the 'search course' bar to work, where by you search for a course and all its uploaded assignments will show up at the left side of the streamlit website

This is a project done as part of my University's Undergraduate Research Opportunities Programme (UROP), titled 'UROP: Telegram chatbot for novice programmers to learn coding', supervised and initiated by Professor Oka Kurniawan (here is his Github account), spanding 8 months of (on-off) work.

Here is the description of the UROP project:
'''
Learning coding can be improved significantly when learners have an immediate and personalized feedback. The number of learners for coding make it impossible for human instructors to give an immediate and personalized feedback to learners. The rise of AI gives us the possibility to create a chatbot Tutor that can support learners anywhere and at any time. The purpose of this project is to create a Telegram chatbot where learners can get feedback related to their coding courses. The feedback should be accurate as reflected in the notes or instructor problem set.

The chatbot can be used for any institution that teaches programming.
'''

This project is made up of 2 Github repositories:

This Github repository is hosting the code for the Streamlit Web Application for the Telegram Chatbot with Chatbase custom GPT LLM model API and Firebase (API)'s Realtime Database.


My Approach to developing this UROP project:

The approach to developing this UROP project is split into 2 components:

  1. Telegram Chatbot integrated with Chatbase custom GPT LLM model API and Firebase (API)'s Realtime Database
  • Create a regular Telegram Bot -> integrating a Chatbase custom GPT LLM model to generate the Telegram Bot's responses to incoming texts/prompts from students (which basically gives the Telegram Bot the 'chatbot' functionality) -> incoming texts/prompts from students and the Chatbase custom GPT LLM model's responses will be saved into a database
  1. Streamlit website
  • There will be a separate website application containing 2 pages
    1. the first 'Details' page where it can take inputs from human instructors to manipulate the setting/behaviour of the Chatbase custom GPT LLM model via Prompt Engineering, and train the Chatbase custom GPT LLM model with their own course materials and notes without having to worry about their own course materials and notes leaking to the public
    2. the second 'Database' page where it displays the incoming texts/prompts from students and the Chatbase custom GPT LLM model's responses for the human instructors to evaluate the students' incoming texts/prompts (which the data is retrieved from the database)

This project's deployed Telegram Bot, Chatbase custom GPT LLM model, Streamlit (Python Framework)'s Website Application and Firebase (API) links:


Table of Contents

Here is a directory to explain the purpose of each file in this repository:

  1. Files that are required in the creation of the Streamlit Web Application for the Telegram Chatbot with Chatbase custom GPT LLM model API and Firebase (API)'s Realtime Database

    1. '.streamlit' folder
      1. 'config.toml' file
    2. 'README.md' file
    3. 'firebase_key.json' file
    4. 'requirements.txt' file
    5. 'streamlit_web_application.py' file

  2. Additional files that are not part of the creation of the Streamlit Web Application for the Telegram Chatbot with Chatbase custom GPT LLM model API and Firebase (API)'s Realtime Database, but includes the past iterations/versions/prototypes of the Streamlit Web Application for the Telegram Chatbot with Chatbase custom GPT LLM model API and Firebase (API)'s Realtime Database

    1. Figma (maybe put a pic of the Figma prototyping of the website, while attaching an image as a jpg file uploaded in this repo)

  3. Additional files that are not part of the creation of the Streamlit Web Application for the Telegram Chatbot with Chatbase custom GPT LLM model API and Firebase (API)'s Realtime Database, but includes my learning journey of some of the required technology being used in this project that I was not familiar with

    1. Prompt Engineering learn (from Prompt Engineering course by DeepLearning.AI in collaboration with OpenAI (using ChatGPT as the LLM))
    2. Figma learn

  4. Deployment Process of the Streamlit Web Application for the Telegram Chatbot with Chatbase custom GPT LLM model API and Firebase (API)'s Realtime Database on Streamlit Cloud


1. Files that are required in the creation of the Streamlit Web Application for the Telegram Chatbot with Chatbase custom GPT LLM model API and Firebase (API)'s Realtime Database

1. '.streamlit' folder
i. 'config.toml' file

The main Python file for the Streamlit Web Application for the Telegram Chatbot with Chatbase custom GPT LLM model API and Firebase (API)'s Realtime Database itself.


2. 'README.md' file
The 'README.md' file.


3. 'firebase_key.json' file

{
  "type": "service_account",
  "project_id": "urop-telegram-chatbot",
  "private_key_id": "d370c3cff86ea75089c60973d19105f90d84fdc7",
  "private_key": "-----BEGIN PRIVATE KEY-----\nMIIEvgIBADANBgkqhkiG9w0BAQEFAASCBKgwggSkAgEAAoIBAQC2Yqgt09bJB335\nbN61dlqWY0yFcLv8DPqvHKtpUqzSAvQGdK00zaPlezkd4SKGfohd8z4o0srpUPt9\n0fLhIPJJJhTjRN7rZpvJOPWiECQfHFXc2OJhOdabDnJoIeth+29RfD5D4t2HUdWO\n/OiSKJfNYNAf2sKELJLlPOnXwpa/wfzNhHap50WLjed8OzF+Y57ucFHut/USlsWp\n3uiRdjHod3Sfs0eL+jKfeOdkni7o/E1ytN1Ascvc1OLpbi9h6GyoLLZf8g2O1Nyn\nfe26tfRnkYvT1viErxnhQBGUh3hz3SVIsZ4FYyVrlWVwH+U8CJg2foBtbtOZjmEK\nPNAC0fi3AgMBAAECggEATMs0iftA3CtJ6Rxkl55qXRkZbrQ9is7CPLpDGFlFyDWT\nHybAiVOu12Cvd3vEiEG91GUnfpPm+R9ujRc5/33aVl9w+xKUFCUDolHX5zGJrAnH\nw3IUu6BZUrdeB6eEjyCJyhMYuofLA/+6fnbRzDzIUiMZ7tezAGkuPtSLl5vo0ns5\n1Mw13sSlssc3bjd+bCa23xDamzKWpMFlHJztnbsRVjKXfzMx2f45+snT1Lqo4kk/\ncCpM8QFlMHxLgei7JOKSkEj6LYM9Xn1dRmPJGGSAtlx/2W0/DFrVD4GCLXReKNsz\n4afFN42cZB88pKT/8YB3KoH3o/wA79k4AdsagccGbQKBgQDz3p5Yi1YgvmxWPKxd\npO1cYek2itrpH9YUZirehU01rrdHJvyufFVEoUHoeoXcDHVt++1iHZvZPwt/ZgZq\nP2tM5XWY1hlgdDgOb5J2td8jdMJuKBkzGLpnSg0qSEVlsBEsJhajvGIzBIz2Ervk\nhz1F3pIwXDZNEFS5ywMrW55WgwKBgQC/dRym9V3g0Y6g2mjAqIANRZ605R3MZpst\nN5r9GFSBB/JXo22shLCA0/RL0A3Q8aGblVv9VwHVOi1qaeU8DIcQqJvdKfspIi0J\nL8mp3ZraGzknCEHkj0oKGqNCE2Nsfx+dQ6du+u7UOecb22pjqV2UeL6gMerAMf16\nX1ZTL7hevQKBgFyIVObd/9Euz+as4O4rXVEXaakjaMraJJ3a4ltKkzBSWgKqfWgr\njyMaWOrASrhjFc+krr7y4ya8cD1n1flMlQc5bbSPUFOz5W080oMuoTtP21J27pDf\nyiLVC0fG4mYiN3HcBe0c1tnq2R2poBenZQ101V16L7RwBOX2bP5vphXHAoGBALX1\nCncOqNr6rm/nQzkeqxxx9yR6v7g8J+xwdWdm0SEUOVjbJGeab9jwF7RZllfm3S1t\nZNC/+Sj6MqF45PkN+ut1IzStKltsdJrPhPxgdUQmLUoQSfd7yuURbellXc+GfbhL\nzPvnlkWyhhduj40KMLrjil/bMPzaRcogg31p0/KNAoGBAKNTzPgZSif0PGXPEvhK\nzEi5ojwvC9iOIKvmjgXLu+e0zAI27PiiryrfrpQW6CK7RpK9KO2lQ2NUdyOi0MVI\nkqMGPLzjVwBKtWMhIwvaZxFJBvCNIg6nE5RSKoXFSYKWqmLPp+2ydPLXGPgAzNL/\n/7+TBiy9OmEkqIbNmwzgiYgR\n-----END PRIVATE KEY-----\n",
  "client_email": "firebase-adminsdk-rvtdl@urop-telegram-chatbot.iam.gserviceaccount.com",
  "client_id": "108928779375842545414",
  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
  "token_uri": "https://oauth2.googleapis.com/token",
  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/firebase-adminsdk-rvtdl%40urop-telegram-chatbot.iam.gserviceaccount.com",
  "universe_domain": "googleapis.com"
}

This file represents the authetication key. Apparently, when accessing to APIs (such as the Firebase API), you will need to have a sort of, authentication key, which is what this file is to ensure that only authorized users can access the API. Refer to this video to understand how the Firebase API authentication key is used with your Python code: https://www.youtube.com/watch?v=s-Ga8c3toVY&t=336s (Code First with Hala)


4. 'requirements.txt' file

streamlit==1.27.2
streamlit_autorefresh==1.0.1
firebase_admin==6.2.0

This is a compulsory file, in accordance to the deployment of Streamlit Web Application for the Telegram Chatbot with Chatbase custom GPT LLM model API and Firebase (API)'s Realtime Database in Streamlit Cloud as described in the documentation on how to deploy a Streamlit (Python Framework) Web Application on Streamlit Cloud (refer to the section below '4. Deployment Process of the Streamlit Web Application for the Telegram Chatbot integrated with Chatbase custom GPT LLM model API and Firebase (API)'s Realtime Database on Streamlit Cloud' for more information on the deployment process of this Streamlit Web Application for the Telegram Chatbot integrated with Chatbase custom GPT LLM model API and Firebase (API)'s Realtime Database on Streamlit Cloud): https://docs.streamlit.io/streamlit-community-cloud/deploy-your-app (Streamlit Cloud), which allows you to tell Streamlit (Python Framework) to download the necessary external libraries/framework/packages specified in this 'requirements.txt' file in the deployment environment that is required for the deployment of this Streamlit Web Application for the Telegram Chatbot integrated with Chatbase custom GPT LLM model API and Firebase (API)'s Realtime Database.

Apparently, the 'requirements.txt' file is a common practice across various deployment platforms in Python, not just for Streamlit Cloud. Whether you are deploying your applications on platforms like Heroku, AWS, Vercel, or others, specifying dependencies in a 'requirements.txt' file allows the platform to understand and install the necessary packages.


5. 'streamlit_web_application.py' file
The main Python file for the Streamlit Web Application for the Telegram Chatbot with Chatbase custom GPT LLM model API and Firebase (API)'s Realtime Database itself.


Source(s):


2. Past iterations/versions/prototypes of the Streamlit Web Application for the Telegram Chatbot with Chatbase custom GPT LLM model API and Firebase (API)'s Realtime Database

https://www.figma.com/community/file/1406323732296662518/telegram-chatbot-website-with-prompt-engineering-prototyping (Figma prototype file link)


3. My learning journey of some of the required technology being used in this project that I was not familiar with

Due to my lack of knowledge in some of the required technology being used in the Streamlit Web Application for the Telegram Chatbot integrated with Chatbase custom GPT LLM model API and Firebase (API)'s Realtime Database, I had to first learn them, which includes:

  • Prompt Engineering learn (from Prompt Engineering course by DeepLearning.AI in collaboration with OpenAI (using ChatGPT as the LLM))
  • Figma learn

  1. Prompt Engineering learn (from Prompt Engineering course by DeepLearning.AI in collaboration with OpenAI (using ChatGPT as the LLM))

    Consists of my learning journey of the Prompt Engineering paradigm (online course where I learnt the Prompt Engineering paradigm from: https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/ by DeepLearning.AI, in collaboration with OpenAI titled 'ChatGPT Prompt Engineering for Developers' (only up till the 7th video in the playlist)

    What is Prompt Engineering?
    Prompt Engineering is the process of writing/refining a generative AI prompt to improve its accuracy and effectiveness and obtain desired outputs from AI models.

    What are prompts?
    An AI (including Large Language Models (LLM)) prompt is a question, command, or statement that you input into an AI (including Large Language Models (LLM)) model to initiate a response or action, harnessing the power of Natural Language Processing (NLP).

    (Note: OpenAI GPT model API (Python Framework) requires monthly payment subscription: The codes in this tutorial require the OpenAI GPT model API (Python Framework) in order to obtain responses from ChatGPT. However, the OpenAI GPT model API (Python Framework) access requires monthly payment subscription. Hence, the codes in this folder will not be able to run, without the OpenAI GPT model API (Python Framework) access, which requires payment. You can do the monthly payment subscription to get the OpenAI GPT model API (Python Framework) access here: https://platform.openai.com/docs/overview (OpenAI Platform)

    Hence, most of the 'output' from the code in this tutorial will be the simulated output shown in the videos in this tutorial, written in Jupyter Notebook files)

    Source(s):


  1. Figma learn

    Consists of my learning journey of Figma (main Youtube playlist where I learnt the Prompt Engineering paradigm from: https://www.youtube.com/playlist?list=PLKId0A0XCIbUYx3c_NYn13W9Z_kkIiA2m by Aliena Cai, titled 'Figma UX Tutorial by Aliena' (only up till the 4th video in the playlist)

    What is Figma?
    Figma is a collaborative web application for interface design, with additional offline features enabled by desktop applications for macOS and Windows. The feature set of Figma focuses on user interface (UI) and user experience (UX) design, with an emphasis on real-time collaboration, utilising a variety of vector graphics editor and prototyping tools.

An important rule on Figma is that you should never start anything from scratch! Always remember to take from references, and copy from existing website templates to boost the quality and the creation time of the website prototype! (as advised from the AJ&Smart Youtube video)

- Here is the link of my [Figma](https://figma.com/) account of the username: 'WindJammer6' - https://www.figma.com/@windjammer6

Source(s):  
- https://www.youtube.com/playlist?list=PLKId0A0XCIbUYx3c_NYn13W9Z_kkIiA2m (Aliena Cai) (Youtube playlist titled 'Figma UX Tutorial by Aliena')
    - https://www.youtube.com/watch?v=D4NyQ5iOMF0&list=PLKId0A0XCIbUYx3c_NYn13W9Z_kkIiA2m&index=1 (Aliena Cai) (Youtube video titled 'Figma UX tutorial for beginners - Wireframe')
    - https://www.youtube.com/watch?v=oZAKb_gs2Uo&list=PLKId0A0XCIbUYx3c_NYn13W9Z_kkIiA2m&index=2 (Aliena Cai) (Youtube video titled 'Figma UX tutorial for beginners - Mockup')
    - https://www.youtube.com/watch?v=HwiHqfax7Uk&list=PLKId0A0XCIbUYx3c_NYn13W9Z_kkIiA2m&index=3 (Aliena Cai) (Youtube video titled 'Figma tutorial for beginners - auto layout & components')
    - https://www.youtube.com/watch?v=v1UKB-0EUhQ&list=PLKId0A0XCIbUYx3c_NYn13W9Z_kkIiA2m&index=4 (Aliena Cai) (Youtube video titled 'Figma UX tutorial for beginners - Prototype')
- https://www.youtube.com/watch?v=FTFaQWZBqQ8 (AJ&Smart) (Youtube video titled 'Figma UI Design Tutorial: Get Started in Just 24 Minutes!')
- https://loremipsum.io/ (Lorem Ipsum text generator)
- https://en.wikipedia.org/wiki/Figma (Wikipedia) (What is Figma definition)

4. Deployment Process of the Streamlit Web Application for the Telegram Chatbot with Chatbase custom GPT LLM model API and Firebase (API)'s Realtime Database on Streamlit Cloud My Skills

What is Streamlit Cloud?
From the official Streamlit Cloud website: 'Streamlit Cloud is a new product that lets you build, deploy, and share data from Streamlit Web Applications in minutes.'

Honestly, the documentation on how to deploy a Streamlit (Python Framework) Web Application on Streamlit Cloud (link: https://docs.streamlit.io/streamlit-community-cloud/deploy-your-app) explains very clearly step by step on how to deploy a Streamlit Web Application on Streamlit Cloud. Once deployed correctly, I got a direct 'streamlit.io' link to the Streamlit Web Application, which I can then share with others to try out this Streamlit Web Application.


Source(s):

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UROP: Telegram chatbot for novice programmers to learn coding. The deployed Streamlit Web Application for the Telegram Chatbot with Chatbase custom GPT LLM model API and Firebase, using Figma for prototyping of the web application.

uzkgcu6dkiqhyappnhnx5c9.streamlit.app/

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