Sentra

A social media sentiment analysis dashboard that aggregates posts from Reddit and YouTube, analyzes sentiment using AI, and visualizes trends in real-time.

Features

• Multi-platform Analysis – Fetch posts from Reddit and YouTube comments
• AI-Powered Sentiment – Uses HuggingFace's RoBERTa model for accurate classification
• Interactive Dashboard – Pie charts, trend lines, and filterable post lists
• Historical Tracking – View sentiment changes over time
• Rate Limiting & Caching – Built-in protections against API abuse

Tech Stack

Layer	Technology
Frontend	Next.js 14 (App Router) + Tailwind CSS + shadcn/ui
Backend	FastAPI (Python)
Database	PostgreSQL
AI/NLP	distilbert-base-uncased-finetuned-sst-2-english
Charts	Recharts
Containerization	Docker & Docker Compose

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Setup

Prerequisites

• Docker Desktop (includes Docker Compose)
• Git

Step 1: Clone the Repository

git clone https://github.com/your-username/Sentra.git
cd Sentra

Step 2: Configure Environment Variables

Copy the example environment file:

cp .env.example .env

Edit the .env file and add your API credentials:

DATABASE_URL=postgresql://postgres:postgres@db:5432/sentra
REDDIT_CLIENT_ID=your_reddit_client_id
REDDIT_CLIENT_SECRET=your_reddit_client_secret
REDDIT_USER_AGENT=sentra:v1.0.0
YOUTUBE_API_KEY=your_youtube_api_key

Step 3: Get API Credentials

Reddit API (Free)

1. Go to https://www.reddit.com/prefs/apps
2. Click "Create App" or "Create Another App"
3. Fill in:
   • Name: Sentra
   • App type: Select "script"
   • Redirect URI: http://localhost:8000
4. Click "Create app"
5. Copy the client ID (under the app name) and secret

YouTube Data API (Free - 10,000 units/day)

1. Go to https://console.cloud.google.com/
2. Create a new project (or select existing)
3. Enable the "YouTube Data API v3"
4. Go to "Credentials" → "Create Credentials" → "API Key"
5. Copy the API key

Note: The app works without API keys, but you won't be able to fetch data from those sources.

Step 4: Build and Start Services

docker-compose up --build

This will:

• Start PostgreSQL database
• Build and start the FastAPI backend (downloads AI model on first run ~500MB)
• Build and start the Next.js frontend

First startup takes 5-10 minutes due to:

• Downloading the sentiment analysis model
• Installing dependencies
• Building the frontend

Step 5: Initialize the Database

Once services are running, run Prisma migrations:

docker-compose exec frontend npx prisma db push

Step 6: Verify Setup

Open your browser and check:

Service	URL	Expected
Frontend	http://localhost:3000	Dashboard UI
Backend Health	http://localhost:8000/health	JSON with status
API Docs	http://localhost:8000/docs	Swagger UI

The health endpoint should show:

{
  "status": "ok",
  "database": "connected",
  "sentimentModel": "loaded",
  "reddit": "configured",
  "youtube": "configured"
}

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Usage

1. Search: Enter a keyword (e.g., "ChatGPT", "climate change", "Tesla")
2. Select Source: Choose Reddit, YouTube, or All
3. Analyze: View sentiment distribution and individual posts
4. Filter: Filter posts by sentiment type
5. Track: Search the same keyword over time to build history

---

Development

Running Locally (without Docker)

Backend

cd backend
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt
uvicorn main:app --reload --host 0.0.0.0 --port 8000

Frontend

cd frontend
npm install
npx prisma generate
npm run dev

Useful Commands

# View logs
docker-compose logs -f

# View specific service logs
docker-compose logs -f backend

# Restart a service
docker-compose restart backend

# Stop all services
docker-compose down

# Stop and remove volumes (reset database)
docker-compose down -v

# Rebuild after code changes
docker-compose up --build

Database Management

# Open Prisma Studio (GUI for database)
docker-compose exec frontend npx prisma studio

# Reset database
docker-compose exec frontend npx prisma db push --force-reset

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API Endpoints

Endpoint	Method	Description
/health	GET	Service health check
/analyze	POST	Analyze single text
/analyze/batch	POST	Analyze multiple texts
/search	GET	Search and analyze from sources
/history	GET	Get historical sentiment data

Example API Calls

# Health check
curl http://localhost:8000/health

# Analyze single text
curl -X POST http://localhost:8000/analyze \
  -H "Content-Type: application/json" \
  -d '{"text": "I love this product!"}'

# Search Reddit for a keyword
curl "http://localhost:8000/search?q=python&source=reddit"

# Get 7-day history
curl "http://localhost:8000/history?q=python&days=7"

---

Troubleshooting

"Sentiment model not loaded"

The model downloads on first startup. Wait a few minutes and check logs:

docker-compose logs -f backend

"Database connection failed"

Ensure PostgreSQL is running:

docker-compose ps

"Reddit/YouTube not configured"

Add API credentials to .env and restart:

docker-compose down
docker-compose up

Frontend not connecting to backend

Check that NEXT_PUBLIC_API_URL is set in frontend/.env.local:

NEXT_PUBLIC_API_URL=http://localhost:8000

Port already in use

Stop other services using the ports or change ports in docker-compose.yml:

• Frontend: 3000
• Backend: 8000
• PostgreSQL: 5432

---

Project Structure

sentra/
├── docker-compose.yml      # Container orchestration
├── .env.example            # Environment template
├── frontend/               # Next.js application
│   ├── src/
│   │   ├── app/           # Pages and API routes
│   │   ├── components/    # React components
│   │   ├── lib/           # Utilities and API client
│   │   └── types/         # TypeScript types
│   └── prisma/            # Database schema
└── backend/                # FastAPI application
    ├── main.py            # Application entry
    └── app/
        ├── routers/       # API endpoints
        ├── services/      # Business logic
        └── models/        # Pydantic schemas

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License

MIT License - feel free to use this project for learning or commercial purposes.