Blood_Cell_Object_Detection / huggingface_deployment.md
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# Hugging Face Spaces Deployment Guide
This document provides instructions on how to deploy the Blood Cell Detection application on Hugging Face Spaces.
## Prerequisites
1. A Hugging Face account
2. The fine-tuned YOLOv10 model file
3. This repository
## Steps to Deploy
### 1. Create a New Space
1. Log in to your Hugging Face account at [huggingface.co](https://huggingface.co)
2. Go to your profile and click on "Spaces"
3. Click "Create new Space"
4. Configure your Space:
- Select "Streamlit" as the SDK
- Set a name for your Space (e.g., "blood-cell-detection")
- Choose visibility (Public or Private)
- Click "Create Space"
### 2. Upload Files
You can upload files using the Hugging Face web interface or using Git:
#### Option 1: Web Interface
1. In your new Space, click on "Files" tab
2. Upload the following files:
- `app.py`
- `model.py`
- `utils.py`
- `inference.py`
- The model weights file (e.g., `yolov10_bccd.pt`)
- Additional Python files as needed
- `huggingface_requirements.txt` (rename to `requirements.txt` when uploading)
#### Option 2: Using Git
1. Clone the Space repository
```bash
git clone https://huggingface.co/spaces/YOUR_USERNAME/blood-cell-detection
```
2. Copy all project files to the cloned directory
3. Rename `huggingface_requirements.txt` to `requirements.txt`
4. Push to Hugging Face
```bash
git add .
git commit -m "Initial deployment"
git push
```
### 3. Configure the Space
1. In your Space settings, make sure:
- The SDK is set to Streamlit
- The Python version is at least 3.9
- The hardware is appropriate (at least CPU)
### 4. Verify the Deployment
1. Once deployed, your Space will build and start the Streamlit app
2. Access your Space through the provided URL to verify it works correctly
3. Test the application by uploading a blood cell image
## Troubleshooting
If you encounter issues during deployment:
1. Check the build logs in your Space
2. Ensure the `requirements.txt` file includes all necessary dependencies
3. Verify the model file path in your code matches the actual file location in the Space
## Additional Resources
- [Hugging Face Spaces Documentation](https://huggingface.co/docs/hub/spaces)
- [Streamlit Documentation](https://docs.streamlit.io/)