Facebook Sentiment Analysis Dashboard

An end-to-end Full-Stack application that scans Facebook comments and uses a Deep Learning backend to perform real-time sentiment analysis. Featuring a high-performance React dashboard with live-updating charts and a notification system.

---

Key Features

• Live Scanning: Analyze Facebook post comments via a custom scanning tool.
• AI Backend: FastAPI server integrated with a trained TensorFlow/Keras model.
• Dynamic Analytics: Interactive Bar and Doughnut charts powered by Chart.js.
• Date Filtering: Historical sentiment trends with custom date range selection.
• Real-time Notifications: Instant feedback on community health and scan status.
• Containerized: Built with Docker for one-click deployment.

---

Project Structure

sentiment-analysis/
├── backend/                # FastAPI & AI Logic
│   ├── app/                # Application code
│   ├── Dockerfile          # Production container setup
│   └── requirements.txt    # Python dependencies
├── frontend/               # React Dashboard
│   ├── src/                # Components & Hooks
│   └── package.json        # NPM dependencies
└── .env                    # Secret keys & DB URLs

## Installation & Setup

### 1. Backend Setup (FastAPI)
Navigate to the backend directory and set up a virtual environment:

```bash
cd backend
python -m venv venv

# Activate the virtual environment
# On Linux/macOS:
source venv/bin/activate  
# On Windows:
venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Environment Variables (.env)

Create a file named .env in the backend/ root directory and add the following configuration:

DATABASE_URL=postgresql+asyncpg://user:password@localhost:5432/sentiment
SECRET_KEY=your_secret_key_here
ALGORITHM=HS256
ACCESS_TOKEN_EXPIRE_MINUTES=60

Run Locally

Start the development server with hot-reload enabled. This will automatically restart the server whenever you save changes to your code:

python -m uvicorn app.main:app --reload

Frontend Setup (React)

Navigate to the frontend directory to install dependencies and launch the development server:

cd frontend
# Install all required npm packages
npm install
# Start the React development server
npm start

Deployment (Render.com)

Backend (Docker Runtime)

Since we are using Docker for the backend, Render will build the container automatically using our Dockerfile.

1. Runtime: Select Docker from the runtime dropdown.
2. Instance Type: Select at least the Starter (2GB RAM) instance.

   Important: TensorFlow requires significant memory to load models; using the Free Tier (512MB) will likely result in an Out of Memory (OOM) error.

3. Environment Variables: Navigate to the Environment tab in the Render Dashboard and manually add your .env keys:
   • DATABASE_URL
   • SECRET_KEY
   • ALGORITHM
   • ACCESS_TOKEN_EXPIRE_MINUTES

---

Frontend (Static Site)

Deploy the React dashboard as a high-performance static site.

1. Build Command: npm run build
2. Publish Directory: build
3. Environment Variables: If your API URL is different in production, add REACT_APP_API_URL to point to your Render Backend URL.

---

Note: Ensure your Backend is deployed first so you can provide the correct API URL to your Frontend during its build process.