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Insighter: Social Media Engagement Analytics

This repository contains the project developed by Team Forbes (Parth Ratra, Pranay Rajvanshi, Rahul Sharma, and Harsh). The goal of this project is to create a basic analytics module using LangFlow and DataStax to analyze engagement data from mock social media accounts.

Project Overview

The project involves:

  1. Generating mock social media engagement data.
  2. Storing and managing the data in a serverless database.
  3. Using LangFlow to create analytics workflows.
  4. Developing an interactive dashboard to visualize the data.

Key Features

1. Data Generation

  • A Python script generates mock social media data including:
    • Engagement metrics: Likes, comments, shares, saves.
    • Sentiment metrics.
    • Demographics and device distribution.
  • The generated data is stored in a CSV file.

2. Database Integration

  • A serverless database is created in Astra DB.
  • A collection is set up, and the generated CSV file is uploaded to the database.

3. LangFlow Implementation

  • Components used:
    • AstraDB component.
    • Data Parsing component.
    • Chat input prompt component.
    • ChatGPT component.
    • Chat output component.
  • The LangFlow Playground feature was utilized for testing, e.g., analyzing the performance of reels vs. carousels.

4. API and Dashboard

  • The LangFlow API is used to create a Next.js application.
  • An interactive dashboard is built with the following components:
    • Post type performance.
    • Engagement over time.
    • Content performance.
    • Device distribution.
    • Engagement vs. comparison rate.
  • The dashboard helps users easily understand the data.

5. Chat Assistant

  • A chat assistant powered by the LangFlow API answers queries like:
    • "What do people in the age group 16 to 26 engage more with: reels, posts, or carousels?"
  • The assistant provides data-driven insights, such as "Carousels are much better than any other form of post."

Tech Stack

  • LangFlow: For building analytics workflows.
  • DataStax Astra DB: For managing the serverless database.
  • Python: For data generation and processing.
  • Next.js: For creating the interactive dashboard.

Installation and Usage

  1. Clone the repository:

    git clone https://github.com/parthratra11/Insighter.git

  2. Navigate to the project directory:

    cd Insighter

  3. Install dependencies:

    pip install -r requirements.txt

  4. Set up Astra DB and upload the generated CSV file.

  5. Run the application:

    python app.py

  6. Access the dashboard and chat assistant features in your browser.

Contact

For further information or queries, please contact the team:

Thank you for reviewing our project.