BERT Sentiment Analysis API

A FastAPI application that provides sentiment analysis using a fine-tuned BERT model.

Features

• Sentiment analysis endpoint with confidence scores
• Input validation and error handling
• Health check endpoint
• Containerized with Docker
• Ready for cloud deployment

API Endpoints

POST /analyze

Analyze sentiment of input text.

Request Body:

{
  "text": "I love this product!"
}

Response:

{
  "label": "positive",
  "confidence": 0.9876
}

GET /health

Check API health and model status.

Response:

{
  "status": "healthy",
  "model_loaded": true
}

Local Development

1. Install dependencies:

pip install -r requirements.txt

2. Run the API:

uvicorn main:app --reload

3. Visit http://localhost:8000/docs for interactive API documentation.

Docker Deployment

1. Build the image:

docker build -t bert-sentiment-api .

2. Run the container:

docker run -p 8000:8000 bert-sentiment-api

Cloud Deployment

Render.com

1. Connect your GitHub repository to Render
2. Create a new Web Service
3. Set the following:
   • Runtime: Docker
   • Build Command: (leave empty, uses Dockerfile)
   • Start Command: (leave empty, uses CMD from Dockerfile)
4. Add environment variable if needed (none required)
5. Deploy

AWS (ECS/Fargate)

1. Build and push Docker image to ECR:

aws ecr get-login-password --region your-region | docker login --username AWS --password-stdin your-account.dkr.ecr.your-region.amazonaws.com
docker tag bert-sentiment-api:latest your-account.dkr.ecr.your-region.amazonaws.com/bert-sentiment-api:latest
docker push your-account.dkr.ecr.your-region.amazonaws.com/bert-sentiment-api:latest

2. Create ECS cluster, task definition, and service
3. Configure load balancer and security groups

Google Cloud Platform (Cloud Run)

1. Build and push to GCR:

gcloud builds submit --tag gcr.io/your-project/bert-sentiment-api

2. Deploy to Cloud Run:

gcloud run deploy --image gcr.io/your-project/bert-sentiment-api --platform managed

Azure (Container Instances)

1. Build and push to ACR:

az acr build --registry your-registry --image bert-sentiment-api .

2. Deploy to ACI:

az container create --resource-group your-rg --name bert-sentiment-api --image your-registry.azurecr.io/bert-sentiment-api --dns-name-label bert-sentiment-api --ports 8000

Requirements

• Python 3.11+
• Fine-tuned BERT model in ./model/ directory
• Sufficient RAM for model loading (4GB+ recommended)

Model

The API expects a fine-tuned BERT model for sentiment analysis saved in the ./model/ directory with the standard transformers format (config.json, pytorch_model.bin or model.safetensors, tokenizer files).# FastAPI