DEPLOYMENT.md

Hugging Face Space Deployment Guide

🚀 How to Deploy Your Sentiment Analysis API to Hugging Face Spaces

Step 1: Create a Hugging Face Account

1. Go to https://huggingface.co
2. Sign up for a free account if you don't have one

Step 2: Create a New Space

1. Go to https://huggingface.co/new-space
2. Fill in the details:
   • Space name: sentiment-analysis-api (or your preferred name)
   • License: MIT
   • SDK: Docker
   • Hardware: CPU Basic (free tier)

Step 3: Upload Your Files

Upload all the files from this directory to your new Space:

• app.py - FastAPI application
• sentiment.pkl - Your trained model
• requirements.txt - Python dependencies
• Dockerfile - Docker configuration
• README.md - Documentation
• index.html - Simple web interface (optional)

Step 4: Your Space Will Auto-Deploy

• Hugging Face will automatically build and deploy your Docker container
• The build process takes 5-10 minutes
• You can monitor the build logs in the Space dashboard

Step 5: Access Your API

Once deployed, your API will be available at:

• API Base URL: https://YOUR_USERNAME-sentiment-analysis-api.hf.space
• Interactive Docs: https://YOUR_USERNAME-sentiment-analysis-api.hf.space/docs
• Simple Interface: https://YOUR_USERNAME-sentiment-analysis-api.hf.space (if you uploaded index.html)

🧪 Testing Locally First

Before deploying, test your API locally:

1. Install dependencies:

   pip install -r requirements.txt
   

2. Run the API:

   python app.py
   

3. Test the endpoints:

   • Visit http://localhost:7860/docs for interactive API docs
   • Run python test_api.py to test all endpoints
   • Visit http://localhost:7860 for the simple web interface

📋 API Endpoints

POST /predict

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

Response:

{
  "prediction": 1,
  "confidence": 0.95,
  "sentiment": "positive"
}

POST /predict_proba

{
  "text": "This is terrible"
}

Response:

{
  "probabilities": [0.85, 0.15],
  "prediction": 0,
  "sentiment": "negative"
}

POST /batch_predict

["I love it!", "This is bad", "It's okay"]

🔧 Customization

If your model requires text preprocessing:

Edit the app.py file in the prediction functions to add your preprocessing steps:

# Add preprocessing before model.predict()
def preprocess_text(text):
    # Add your preprocessing logic here
    # e.g., tokenization, vectorization, etc.
    return processed_text

# In the prediction functions:
processed_text = preprocess_text(input_data.text)
prediction = model.predict([processed_text])[0]

To change the port or host:

Modify the last line in app.py:

uvicorn.run(app, host="0.0.0.0", port=8000)  # Change port as needed

🆘 Troubleshooting

1. Model loading errors: Ensure sentiment.pkl is in the same directory
2. Dependency issues: Check that all required packages are in requirements.txt
3. Memory issues: Your model might be too large for the free tier (upgrade to paid tier)
4. Preprocessing errors: Make sure your text preprocessing matches your training pipeline

💡 Next Steps

• Add authentication for production use
• Implement rate limiting
• Add logging and monitoring
• Create more sophisticated web interface
• Add model versioning