EngageIQ

Designed With 😇 :

• Flask
• Py

EngageIQ introduces an innovative analytics chatbot that analyzes engagement patterns from simulated social media accounts. By combining LangFlow for workflow automation and DataStax Astra DB for robust database operations, the solution recreates a dynamic social media ecosystem. The chatbot evaluates various post formats—like carousels, reels, and static images—and provides real-time insights through natural language interaction, enabling smarter content strategies and audience engagement optimization with an interactive edge.

GitHub Link 🔗

• https://github.com/yashpunmiya/Team-4i-Datastax-x-Langchain

Deploy Link 🔗

• https://team-4i-langchain-x-datastax.onrender.com/

Problem it solves 🙅‍♂️

• EngageIQ addresses the need for understanding social media engagement trends. By automating the analysis of post-performance data: Social media managers can make data-driven decisions to optimize content strategies.Brands can better understand which post types resonate with their audience.The time and effort required for manual analysis of engagement data is minimized.

Challenges I ran into 🙅‍♂️

• Adapting Langflow workflows with DataStax Astra DB for a web interface was challenging. Issues included handling API calls for web compatibility, ensuring seamless backend-frontend data flow, and securely hosting the project on a cloud platform. Resolution: The Langflow workflow was converted into Flask endpoints, ensuring compatibility with DataStax queries. Dependencies were packaged, and environment variables were securely managed during deployment on Render, enabling smooth functionality and hosting.