📊 Social Media Performance Analysis

SocioMetrics

Analyze social media engagement metrics with this powerful tool! This project uses cutting-edge technologies to provide deep insights into post performance across platforms like Instagram, Twitter, LinkedIn, and YouTube. Compare post types, identify trends, and make data-driven decisions to boost engagement! 🚀

🛠️ Tech Stack

• Langflow: Workflow creation and GPT integration for generating insights 🤖
• DataStax Astra DB: Cloud-based NoSQL database for storing and querying data 📂
• React (Vite): Frontend framework for building a fast and interactive user interface 🌐
• Node.js & Express.js: Backend for handling API requests and data processing ⚙️

✨ Features

1. Platform-Specific Analysis 📱
   • Instagram, Twitter, LinkedIn, YouTube, and more!
2. Post-Type Comparisons 📸
   • Reels, carousels, static images, videos, and text.
3. Dynamic Query Handling 🔍
   • Ask questions like:
     • "Which platform drives the highest engagement for reels?"
     • "Which post type performs best on Instagram?"
4. Cross-Platform Insights 🌍
   • Compare performance across platforms for better strategies.
5. Actionable Recommendations 📈
   • Data-driven suggestions to optimize engagement.

🚀 How It Works

1. Data Storage 🗄️
   • Engagement data is stored in DataStax Astra DB.
2. Dynamic Queries 🧠
   • User queries are processed via Langflow workflows to fetch and analyze data.
3. Custom Insights 💡
   • Insights are generated using GPT integration based on engagement metrics.
4. Frontend Interaction 🎨
   • A React-based frontend displays intuitive visualizations and reports.
5. Backend Integration 🔗
   • Node.js and Express.js power the APIs connecting the frontend and database.

📂 Project Structure

• /frontend: React (Vite) application.
• /backend: Node.js and Express.js API.
• /workflows: Langflow workflows for GPT integration.
• /database: DataStax Astra DB setup and collections.

⚙️ Installation

1. Clone the repository:

   git clone https://github.com/CoderSwarup/level-supermind-hackerthon.git

2. Install dependencies for both frontend and backend:

   cd Client
   npm install
   
   cd server
   npm install

3. Set up environment variables for Astra DB and Langflow API keys.

4. Start the development servers:

   # Client
   npm run dev
   
   # server
   npm start

5. Access the app at http://localhost:3000.

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📝 Usage

1. Query Engagement Data 📊
   • Enter a question in the search bar, e.g., "Which post type has the best performance?"
2. View Insights 🔍
   • Get platform-specific or cross-platform insights with actionable recommendations.
3. Analyze Trends 📈
   • Leverage data visualizations to understand engagement trends.

📹 Demo

Watch the demo video here: YouTube Demo Link 🎥

Client ScreenShot

🤝 Contribution

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-name

3. Commit your changes:

   git commit -m "Add new feature"

4. Push to the branch:

   git push origin feature-name

5. Open a Pull Request.

📜 License

This project is licensed under the MIT License. 📝

💬 Feedback

We'd love to hear your thoughts! Please open an issue or reach out to us. 💌