Twinfluence App Readme

Welcome to Twinfluence! This is a web application that generates responses for influencers to answer questions from their followers. This document serves as a guide to understanding the functionalities, tech stacks used, and other essential information about the app.

Table of Contents

1. Introduction
2. Demo Links
3. Tech Stacks
4. Getting Started
5. How it Works
6. Attribution
7. Topic Studied

Introduction

Twinfluence is a powerful application designed to assist influencers in responding to questions from their followers effectively. Using the app, influencers can craft personalized and engaging responses through the assistance of OpenAI's ChatGPT language model.

Demo Links

• Frontend: https://twinfluence.netlify.app/home
• Backend: https://twinfluence.onrender.com/

Tech Stacks

Twinfluence is built using a combination of frontend and backend technologies to create a seamless user experience. The tech stacks employed in the development of the app are as follows:

• Frontend:

  • HTML
  • CSS
  • Angular

• Backend:

  • Python
  • Flask
  • MongoDB (presumably for database storage)

Getting Started

To get started with Twinfluence, follow these steps:

1. Clone the Repository: If you want to deploy the application locally, you can start by cloning the frontend and backend repositories.

2. Install Dependencies: Navigate to the respective directories (frontend and backend) and install the required dependencies. For the frontend, you'll need Angular-related dependencies, and for the backend, you'll need Flask and other required packages.

3. Set Up MongoDB: Ensure you have MongoDB installed and running. Update the backend configuration to connect to your MongoDB database.

4. Start the App: Once all dependencies are installed and configurations are set up, run the frontend and backend servers to launch Twinfluence.

How it Works

Twinfluence utilizes OpenAI's ChatGPT to generate responses for influencers. When a follower asks a question, the app sends the question to the backend, where the ChatGPT model processes it. The model generates a response based on the prompt engineering, considering the influencer's tone, style, and personality. The backend then sends the generated response to the frontend, where the influencer can review and edit if necessary before posting it as a reply to the follower.

Attribution

Twinfluence is made possible by the integration of various technologies and services. The key attributions for the app are as follows:

• OpenAI: For providing the ChatGPT language model, enabling the generation of influencer responses.
• Netlify: For hosting the frontend of the Twinfluence app and providing a reliable deployment platform.
• Render: For hosting the backend of the Twinfluence app and supporting the Python Flask application.

Topic Studied

The development of Twinfluence involves studying the topic of prompt engineering. Prompt engineering is the art of crafting effective prompts to get desired outputs from language models like ChatGPT. By mastering prompt engineering, Twinfluence can generate more relevant and appropriate responses for influencers to engage with their followers better.

Feel free to reach out to the developers for any questions, feedback, or support related to Twinfluence.

Happy Twinfluencing!