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:

• Team Forbes
• Email: [[email protected]]

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