jpcadena/ice-breaker

A Flask-based web app leveraging LangChain and OpenAI's LLM for innovative IceBreaker interactions via social media insights.

ice-breaker

LLM IceBreaker

LLM IceBreaker with a Flask web application
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Contributing
5. Security
6. Code of Conduct
7. License
8. Contact

About The Project

IceBreaker is a Flask-powered web application that harnesses the power of LangChain and OpenAI's Large Language Models (LLM) to create engaging and personalized IceBreaker strategies. By integrating with social media APIs such as Google Search, Twitter, and LinkedIn, IceBreaker offers a unique approach to connecting with people online, leveraging the advanced capabilities of ChatGPT for intelligent, context-aware interactions.

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Built with

Components

• Flask Backend: Serves as the backbone of the application, handling HTTP requests, API interactions, and serving dynamic content using Jinja2 templating.

• LangChain with OpenAI LLM: Utilizes advanced language models for generating personalized and contextually relevant IceBreaker suggestions.

• Social Media Integration: Leverages APIs from Google Search, Twitter, and LinkedIn to gather insights and tailor conversations effectively.

• Frontend: Built with HTML and CSS, providing a user-friendly interface for interaction.

This application stands out by combining cutting-edge AI with practical social media insights, offering a novel way to break the ice in digital communications.

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Getting started

Prerequisites

• Python 3.11

Installation

1. Clone the repository

   git clone https://github.com/jpcadena/ice-breaker.git
   

2. Change the directory to root project

   cd ice-breaker
   

3. Install Poetry package manager

   pip install poetry
   

4. Install the project's dependencies

   poetry install
   

5. Activate the environment

   poetry shell
   

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Usage

1. Setting up environment variables:

   If you find a .env.sample in the project directory, make a copy of it and rename to .env.

   cp .env.sample .env
   

   This .env file will be used to manage your application's environment variables.

2. Configuring your credentials:

   Open the .env file in a text editor and replace the placeholder values with your actual credentials.

   # .env file
   TWITTER_API_KEY=your_x_api_key
   OPENAI_API_KEY=your_openai_api_key
   

   Be sure to save the file after making these changes.

3. Starting the WSGI server:

   To start the local server on your machine, run the following command in your terminal:

   flask run --debug
   

   The --debug flag enables debug mode and hot reloading, which means the server will automatically update whenever you make changes to the code.

4. Interacting with the app:

   Once your server is running, you can interact with it using the Javascript client at your web browser at http://localhost:5000.

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Contributing

Please read our contributing guide for details on our code of conduct, and the process for submitting pull requests to us.

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Security

For security considerations and best practices, please refer to our Security Guide for a detailed guide.

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Code of Conduct

We enforce a code of conduct for all maintainers and contributors. Please read our Code of Conduct to understand the expectations before making any contributions.

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License

Distributed under the MIT License. See LICENSE for more information.

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Contact

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