DEPLOYMENT_GUIDE.md

Hugging Face Spaces Deployment Guide

Steps to Deploy on Hugging Face Spaces

1. Create a New Space

1. Go to Hugging Face Spaces
2. Choose a name for your space (e.g., content-classifier)
3. Select Docker as the SDK
4. Set the space to Public or Private as needed
5. Click Create Space

2. Upload Files to Your Space

You need to upload these files to your Space repository:

contextClassifier.onnx    # Your ONNX model
app.py                   # FastAPI application
requirements.txt         # Python dependencies
Dockerfile              # Docker configuration
README.md               # This will become the Space's README

3. Required Files Content

For the Space's README.md header, add this at the top: ```yaml

title: Content Classifier emoji: 🔍 colorFrom: blue colorTo: purple sdk: docker pinned: false license: mit app_port: 7860

### 4. Deployment Process

1. **Via Git (Recommended):**
   ```bash
   git clone https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME
   cd YOUR_SPACE_NAME
   
   # Copy your files
   copy contextClassifier.onnx .
   copy app.py .
   copy requirements.txt .
   copy Dockerfile .
   
   # Commit and push
   git add .
   git commit -m "Add content classifier API"
   git push

2. Via Web Interface:
   • Use the Files tab in your Space
   • Upload each file individually
   • Or drag and drop all files at once

5. Monitor Deployment

1. Go to your Space URL: https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME
2. Check the Logs tab to monitor the build process
3. The Space will show "Building" status during deployment
4. Once ready, you'll see the API documentation interface

6. Access Your API

Once deployed, your API will be available at:

• Swagger UI: https://YOUR_USERNAME-YOUR_SPACE_NAME.hf.space/docs
• API Endpoints:
  • POST /predict - Main prediction endpoint
  • GET /health - Health check
  • GET /model-info - Model information

7. Example Usage

import requests

# Replace with your actual Space URL
api_url = "https://YOUR_USERNAME-YOUR_SPACE_NAME.hf.space"

# Make a prediction
response = requests.post(
    f"{api_url}/predict",
    json={"text": "This is a test message"}
)

print(response.json())

8. Important Notes

• Model Size: Make sure your contextClassifier.onnx file is under the Space's size limit
• Cold Start: The first request might take longer as the Space wakes up
• Logs: Monitor the logs for any runtime errors
• Updates: Any push to the repository will trigger a rebuild

9. Troubleshooting

Common Issues:

• Build Fails: Check logs for dependency issues
• Model Not Found: Ensure contextClassifier.onnx is in the root directory
• Port Issues: Make sure the app uses port 7860
• Memory Issues: Large models might exceed memory limits

Solutions:

• Review requirements.txt for compatible versions
• Check model file path in app.py
• Verify Dockerfile exposes port 7860
• Consider model optimization for deployment