DataLens is a social media analytics tool that provides actionable insights into engagement metrics. It helps users analyze how different types of posts (carousels, reels, images) perform and offers suggestions to improve social media strategies using GPT.

The goal of this project is to:

Simulate social media engagement data (likes, shares, comments, post types).

Store the data in DataStax Astra DB.

Use Langflow to build workflows that analyze the data and generate insights using GPT.

Technology Purpose DataStax Astra DB Database for storing engagement data Langflow Workflow creation and GPT integration Groq To generate insights based on the data React Frontend Python Server

Here are some sample insights that DataLens can generate:

✅ "Carousel posts have 20% higher engagement than static images." ✅ "Reels generate twice as many comments compared to other formats."

These insights can help users optimize their social media strategies.

The architecture of DataLens is divided into two key layers: Frontend Layer and Backend Layer. Here's a detailed breakdown:

The landing page provides an engaging introduction to DataLens with the following components:

Header with Navigation: Easy navigation to different sections of the website.

Features Showcase: Highlight key features of DataLens.

Team Information: Display team members' names and roles.

Call-to-Action Elements: Encourage users to explore the analytics dashboard.

The core feature of DataLens, providing users with actionable insights into social media performance:

Performance Overview Cards: Quick summary of key metrics such as likes, shares, and comments.

Data Visualization Section: Interactive charts and graphs to represent engagement data.

Analytics Insights Panel: Displays GPT-generated insights based on engagement data.

Data Grid for Detailed View: A tabular format for users to view post-level details.

Assistant: Get insights about the data based on user queries.

The backend includes a proxy server to handle client requests and manage real-time data flow:

Request Handling: Manages incoming requests from the frontend.

Response Streaming: Streams data back to the frontend removing the headers that makes your browser block them for seamless performance.

Error Management: Handles errors and ensures system reliability.

The backend processes engagement data to generate meaningful insights:

Text Splitting and Chunking: Splits large text data into smaller chunks for processing.

Data Parsing: Parses incoming data to prepare it for analysis.

Vector Store Implementation: Stores processed data efficiently.

Thank you for taking the time to explore the DataLens Social Media Analytics Platform. We hope this documentation gives you a clear understanding of how the platform works. If you have any questions or need further details, feel free to dive into the individual sections or reach out to the development team—we're here to help!