Try it Now! *Check out our live deployment: Try it
**After Future Updates
Welcome to the Media Minds! This project aims to provide valuable insights into social media post performance by analyzing engagement data (likes, shares, comments) across different post types like carousel posts, reels, and static images. The module integrates Langflow for intelligent insights generation and uses DataStax Astra DB for efficient data storage and querying.
Whether you're a social media manager or a data enthusiast, this module can help optimize social media strategies by analyzing engagement trends and providing actionable insights.
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Database:- DataStax Astra DB: A cloud-native database for scalable data storage and fast querying. We use it to store and manage social media engagement data.
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Workflow:- Langflow: A powerful tool to create intelligent workflows that can interact with GPT models. In this project, Langflow is used to process the data and generate insights.
3*. Backend:- Python: The core language for implementing workflows and interacting with the database.
4*. Frontend:- Html, Next.js, Css, Tailwind, Javascript
5*. Deployment:- Vercel
**After Future improvements.
Social Media Data Storage: Store engagement data like likes, shares, comments, and post types in DataStax Astra DB.
Post Performance Analytics: Analyze post types and calculate average engagement metrics.
Automated Insights: Generate insights based on engagement data using Langflow and GPT integration.
Example insights:
"Carousel posts generate 20% higher engagement than static posts."
"Reels drive 2x more comments compared to other formats."
git clone https://github.com/manavmax/MediaMinds.git cd MediaMinds
pip install -r requirements.txt Set Environment Variables: Create a .env file in the root directory and add the required variables.
python app.py
Visit http://127.0.0.1:5000 in your browser.
Install the Vercel CLI:
npm install -g vercel
vercel
Advanced Data Analysis: Add more complex analysis, such as sentiment analysis on comments or engagement prediction.
Interactive Dashboard: Implement a dashboard to visualize trends and insights in real-time.
Enhanced GPT Insights: Use more advanced GPT models for deeper insights into engagement patterns.
Watch our project in action: https://youtu.be
Feel free to fork this repository and contribute to improving the module. If you have ideas or improvements, please submit a pull request or open an issue.
This project is licensed under the MIT License. See the LICENSE file for more details.
Langflow for enabling easy workflow creation with GPT models.
DataStax Astra DB for providing a scalable database solution.
By following these steps, you’ll have a powerful analytics module that can provide valuable insights into social media post performance. Happy coding! 🚀