https://github.com/coderswarup/level-supermind-hackerthon
https://github.com/coderswarup/level-supermind-hackerthon
Last synced: 12 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/coderswarup/level-supermind-hackerthon
- Owner: CoderSwarup
- Created: 2025-01-05T17:27:31.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-05T17:29:32.000Z (about 1 year ago)
- Last Synced: 2025-01-05T18:26:24.638Z (about 1 year ago)
- Language: TypeScript
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 📊 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:
```bash
git clone https://github.com/CoderSwarup/level-supermind-hackerthon.git
```
2. Install dependencies for both frontend and backend:
```bash
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:
```bash
# Client
npm run dev
# server
npm start
```
5. Access the app at `http://localhost:3000`.
---
## 📝 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:
```bash
git checkout -b feature-name
```
3. Commit your changes:
```bash
git commit -m "Add new feature"
```
4. Push to the branch:
```bash
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. 💌