# 📊 Social Media Post Analytics Dashboard

A **full-stack web application** that empowers marketers, analysts, and researchers to analyze social media posts from platforms like **Instagram, Twitter (X), Facebook, and TikTok**.  
It leverages **AI-powered sentiment analysis**, **engagement metrics**, and **interactive visualizations** to help optimize content strategies and measure campaign success.

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## ✨ Features

- 🔍 **Sentiment Analysis** → Classifies posts as *positive, negative, or neutral*  
- 📈 **Engagement Metrics** → Tracks likes, comments, shares, views, retweets  
- ⏳ **Time Series Trends** → Visualizes engagement growth over time  
- #️⃣ **Keyword & Hashtag Tracking** → Frequency analysis with charts  
- 📊 **Responsive Visuals** → Pie, Bar, and Line charts using Chart.js  
- 🌍 **Multi-Platform Support** → Works with URLs from multiple social platforms  
- 🔒 **Secure API Integration** → Environment variables for safe key storage  

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## 🚀 Tech Stack

**Frontend:** React.js, Tailwind CSS, react-chartjs-2  
**Backend:** Flask, Python, LangChain, LangGraph  
**APIs:** OpenAI API, Social Media Graph APIs  
**Other Tools:** Virtual Environments, Environment Variables  

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## ⚙️ Setup Guide

### 🔧 Backend Setup

```bash
# 1. Navigate to backend directory
cd backend

# 2. Create and activate a virtual environment
python -m venv venv
source venv/bin/activate   # macOS/Linux
.\venv\Scripts\activate    # Windows PowerShell

# 3. Install dependencies
pip install -r requirements.txt

# 4. Create a .env file with your configs
OPENAI_API_KEY=your_openai_api_key
LANGSMITH_API_KEY=your_langsmith_api_key
FLASK_ENV=development
PORT=5000
SECRET_KEY=your_secret_key

# 5. Run the backend
python app.py

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🎨 Frontend Setup

# 1. Navigate to frontend directory
cd frontend

# 2. Install npm dependencies
npm install

# 3. Create a .env file in frontend root
REACT_APP_BACKEND_URL=http://localhost:5000

# 4. Run the frontend app
npm start

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🖥️ How to Use

1. Open http://localhost:3000 in your browser
2. Paste the URL of a social media post to analyze
3. View sentiment distribution, engagement metrics, and keyword trends
4. If analysis fails, error messages will guide you

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📷 Sample Screenshots

🏠 Landing Page

📊 Sentiment Analysis Pie Chart

📈 Engagement Metrics Bar Chart

⏱️ Engagement Over Time Line Chart

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📜 License

This project is licensed under the MIT License. Feel free to use and modify it for your needs.

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🙏 Acknowledgements

• OpenAI LangChain & LangGraph
• React.js & react-chartjs-2
• Tailwind CSS

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⭐ If you found this project useful, consider giving it a star on GitHub!