🎬 CineBERT API

Sentiment classification API using DistilBERT, deployed via Docker on AWS EC2.

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🚀 Quick Start: Deploy to EC2

🔐 Step 0: SSH into your EC2

ssh -i "your-key.pem" ubuntu@<your-ec2-public-ip>

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📥 Step 1: Navigate & Pull the Latest Code

cd cinebert-api
git pull

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🛠 Step 2: Build and Run the Docker Container

# Stop & remove previous container if needed
sudo docker stop cinebert-api || true
sudo docker rm cinebert-api || true

# Build the new image
sudo docker build -t cinebert-api:latest .

# Run the container (automatically restart if EC2 reboots)
sudo docker run -d --name cinebert-api \
  --restart unless-stopped \
  -p 8000:8000 \
  cinebert-api:latest

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✅ Step 3: Test Your API

Visit the following URL in your browser:

http://<your-ec2-public-ip>:8000/docs

You’ll see the FastAPI Swagger UI, where you can test the /predict and /health endpoints.

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📦 Project Structure

cinebert-api/
├── api/
│   ├── main.py              # FastAPI app
│   ├── requirements.txt     # API dependencies
│   └── saved_model/         # (downloaded from S3)
├── Dockerfile               # For building the image
├── entrypoint.sh            # Downloads model from s3 & starts the server 
└── .github/workflows/       # (Optional CI setup)

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📁 Model Download (via entrypoint.sh)

The Docker container automatically downloads your fine-tuned model from this S3 path:

s3://cinebert-mlflow-models/saved_model/

Make sure:

• The EC2 instance role or environment has S3 read permission.
• The path is correct and contains HuggingFace-style saved model files.

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🧯 Common Commands

# Check container logs
sudo docker logs -f cinebert-api

# Restart container manually
sudo docker restart cinebert-api

# View running containers
sudo docker ps

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🔒 Security Note

Do not commit the following to GitHub or write them here:

• .pem private key
• .env secrets (API keys, AWS credentials)
• Actual model files (they are pulled from S3)