Lernit

An edtech platform using prompt engineering. 🤔 What is this? Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. However, using these LLMs in isolation is often insufficient for creating a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.

Flask Application for GPT-Based Question and Answer System

This is a Flask-based web application that integrates with the Hugging Face API to generate multiple-choice questions based on user input and provide both correct and incorrect answers. The project uses Flask for the web framework and LangChain to handle the GPT-3 model from Hugging Face.

Features

• User Authentication: Login and signup pages for user interaction.
• GPT Integration: Uses GPT-3 to generate multiple-choice questions with answers.
• Multiple GPT-3 Models: Integrates different prompt templates to generate both correct and incorrect answers for the questions.
• Web Interface: Provides a clean user interface to interact with the GPT model and view the generated questions and answers.
• API Integration: Handles POST requests for generating questions and answers dynamically.

Project Setup

To run this project locally, follow these steps:

Prerequisites

• Python 3.7 or higher
• Flask
• Hugging Face API (Flan-T5 model)
• LangChain
• dotenv (for environment variable management)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/Flask-GPT-Application.git

2. Navigate to the project directory:

   cd Flask-GPT-Application

3. Install the required dependencies:

   pip install -r requirements.txt

4. Set up the .env file with your Hugging Face API key:

   HUGGINGFACE_API_KEY=your_api_key_here

Running the Application

1. Run the Flask app:

   python app.py

2. Visit http://127.0.0.1:5000 in your web browser.

API Endpoints

• GET /: Home page
• GET /login: Login page
• GET /signup: Signup page
• GET /youtube: YouTube page
• GET /features: Features page
• GET /resources: Resources page
• POST /gpt: Generates multiple-choice questions based on the user's search term.
• POST /gpt3: Another endpoint to generate unique multiple-choice questions based on a different prompt.

Example Request for /gpt:

• Method: POST
• Body (form data):

  search: "Science"
  

Example Response:

{
  "question": "Q: Generate random unique hard Multiple choice questions with answers on Science topic?",
  "answers": [
    {"text": "Answer A", "correct": true},
    {"text": "Answer B", "correct": false},
    {"text": "Answer C", "correct": false},
    {"text": "Answer D", "correct": false}
  ]
}

Technologies Used

• Flask: Python web framework for building web applications.
• LangChain: A framework for building language model chains.
• Hugging Face: A platform that provides access to state-of-the-art machine learning models (used here with flan-t5-xxl).
• HTML/CSS: For the frontend design.

Contributing

1. Fork the repository.
2. Create a new branch (git checkout -b feature-name).
3. Make your changes.
4. Commit your changes (git commit -am 'Add new feature').
5. Push to the branch (git push origin feature-name).
6. Create a new pull request.

License

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

Acknowledgments

• Hugging Face for providing the language models.
• LangChain for simplifying the integration of language models into Flask applications.