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PDF-Redaction-API / COMPLETE_GUIDE.md

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# 🚀 Complete FastAPI Deployment Package

## 📦 What You've Got

A production-ready FastAPI application for PDF redaction with Named Entity Recognition, ready to deploy on HuggingFace Spaces or any cloud platform.

---

## 📁 Directory Structure

```
pdf-redaction-api/
│
├── 📄 main.py                     # FastAPI application
├── 🐳 Dockerfile                  # Production container
├── 🐳 docker-compose.yml          # Local development
├── 📋 requirements.txt            # Python dependencies
│
├── 📱 app/
│   ├── __init__.py
│   └── redaction.py              # Core redaction engine
│
├── 📂 uploads/                    # Temporary uploads
│   └── .gitkeep
│
├── 📂 outputs/                    # Redacted PDFs
│   └── .gitkeep
│
├── 🧪 tests/
│   └── test_api.py               # API tests
│
├── 📚 Documentation/
│   ├── README.md                 # Main docs (for HF Spaces)
│   ├── DEPLOYMENT.md             # Deployment guide
│   ├── QUICKSTART.md             # Quick start guide
│   └── STRUCTURE.md              # Project structure
│
├── 🔧 Configuration/
│   ├── .env.example              # Environment variables
│   ├── .gitignore                # Git ignore
│   └── .dockerignore             # Docker ignore
│
├── 🤖 .github/
│   └── workflows/
│       └── ci-cd.yml             # GitHub Actions CI/CD
│
├── 📝 client_example.py           # Example API client
└── 📜 LICENSE                     # MIT License
```

---

## ✨ Features

### Core Functionality
✅ PDF upload and processing
✅ OCR with pytesseract (configurable DPI)
✅ Named Entity Recognition (NER)
✅ Accurate coordinate-based redaction
✅ Multiple entity type support
✅ Downloadable redacted PDFs

### API Features
✅ RESTful API with FastAPI
✅ Automatic OpenAPI documentation
✅ File upload handling
✅ Background task cleanup
✅ Health checks
✅ Statistics endpoint
✅ CORS support

### DevOps
✅ Docker containerization
✅ Docker Compose for local dev
✅ GitHub Actions CI/CD
✅ HuggingFace Spaces ready
✅ Comprehensive testing
✅ Logging and monitoring

---

## 🎯 Quick Deployment Paths

### Option 1: HuggingFace Spaces (Recommended for Demo)

**Time: 10 minutes**

```bash
# 1. Create Space on HuggingFace (select Docker SDK)
# 2. Clone your space
git clone https://huggingface.co/spaces/YOUR_USERNAME/pdf-redaction-api
cd pdf-redaction-api
# 3. Copy all files
cp -r /path/to/pdf-redaction-api/* .
# 4. Deploy
git add .
git commit -m "Initial deployment"
git push
```

**Your API will be at:** `https://YOUR_USERNAME-pdf-redaction-api.hf.space`

**Cost:** FREE (with CPU Basic tier)

---

### Option 2: Docker Locally

**Time: 5 minutes**

```bash
# Build
docker build -t pdf-redaction-api .
# Run
docker run -p 7860:7860 pdf-redaction-api
# Test
curl http://localhost:7860/health
```

---

### Option 3: Direct Python

**Time: 3 minutes**

```bash
# Install dependencies
sudo apt-get install tesseract-ocr poppler-utils
pip install -r requirements.txt
# Run
python main.py
# Access at http://localhost:7860
```

---

## 🔌 API Endpoints

### Core Endpoints

| Method | Endpoint | Description |
|--------|----------|-------------|
| POST | `/redact` | Upload and redact PDF |
| GET | `/download/{job_id}` | Download redacted PDF |
| GET | `/health` | Health check |
| GET | `/stats` | API statistics |
| DELETE | `/cleanup/{job_id}` | Manual cleanup |
| GET | `/docs` | Interactive API docs |

### Example Usage

**cURL:**
```bash
curl -X POST "http://localhost:7860/redact" \
  -F "file=@document.pdf" \
  -F "dpi=300"
```

**Python:**
```python
import requests
response = requests.post(
    "http://localhost:7860/redact",
    files={"file": open("document.pdf", "rb")},
    params={"dpi": 300}
)
job_id = response.json()["job_id"]
redacted = requests.get(f"http://localhost:7860/download/{job_id}")
```

---

## 🎨 Architecture

```
┌─────────────────────────────────────────────────────────┐
│                    CLIENT REQUEST                       │
│              (Upload PDF via POST /redact)              │
└─────────────────────────────────────────────────────────┘
                          ↓
┌─────────────────────────────────────────────────────────┐
│                   FASTAPI (main.py)                     │
│  • Validate file                                        │
│  • Generate job_id                                      │
│  • Save to uploads/                                     │
└─────────────────────────────────────────────────────────┘
                          ↓
┌─────────────────────────────────────────────────────────┐
│              PDFRedactor (app/redaction.py)             │
│                                                         │
│  ┌─────────────────────────────────────────┐           │
│  │ 1. OCR (pytesseract)                    │           │
│  │    • Convert PDF → Images (pdf2image)   │           │
│  │    • Extract text + bounding boxes      │           │
│  │    • Store image dimensions             │           │
│  └─────────────────────────────────────────┘           │
│                     ↓                                   │
│  ┌─────────────────────────────────────────┐           │
│  │ 2. NER (HuggingFace Transformers)       │           │
│  │    • Load model                         │           │
│  │    • Identify entities in text          │           │
│  │    • Return entity types + positions    │           │
│  └─────────────────────────────────────────┘           │
│                     ↓                                   │
│  ┌─────────────────────────────────────────┐           │
│  │ 3. Mapping                              │           │
│  │    • Create character span index        │           │
│  │    • Match NER entities to OCR boxes    │           │
│  └─────────────────────────────────────────┘           │
│                     ↓                                   │
│  ┌─────────────────────────────────────────┐           │
│  │ 4. Redaction (pypdf)                    │           │
│  │    • Scale image coords → PDF coords    │           │
│  │    • Create black rectangle annotations │           │
│  │    • Write redacted PDF                 │           │
│  └─────────────────────────────────────────┘           │
└─────────────────────────────────────────────────────────┘
                          ↓
┌─────────────────────────────────────────────────────────┐
│                   RESPONSE                              │
│  • job_id                                               │
│  • List of entities                                     │
│  • Download URL                                         │
└─────────────────────────────────────────────────────────┘
```

---

## 🔐 Security Considerations

### Current Implementation
- ✅ File validation (PDF only)
- ✅ Temporary file cleanup
- ✅ CORS middleware
- ✅ Error handling

### For Production (TODO)
- ⚠️ Add API key authentication
- ⚠️ Implement rate limiting
- ⚠️ Add file size limits
- ⚠️ Use HTTPS only
- ⚠️ Implement user quotas
- ⚠️ Add input sanitization

**Example API Key Auth:**
```python
# Add to main.py
from fastapi import Security, HTTPException
from fastapi.security import APIKeyHeader
API_KEY = "your-secret-key"
api_key_header = APIKeyHeader(name="X-API-Key")
def verify_api_key(key: str = Security(api_key_header)):
    if key != API_KEY:
        raise HTTPException(401, "Invalid API Key")
```

---

## 📊 Performance Tuning

### DPI Settings

| DPI | Quality | Speed | Use Case |
|-----|---------|-------|----------|
| 150 | Low | Fast | Quick previews |
| 200 | Medium | Medium | General use |
| 300 | High | Slow | **Recommended** |
| 600 | Very High | Very Slow | Critical documents |

### Hardware Requirements

**Minimum (Free Tier):**
- CPU: 2 cores
- RAM: 2GB
- Storage: 1GB

**Recommended (Production):**
- CPU: 4+ cores
- RAM: 8GB
- Storage: 10GB
- GPU: Optional (speeds up NER)

---

## 🧪 Testing

```bash
# Install test dependencies
pip install pytest pytest-cov httpx
# Run tests
pytest tests/ -v
# With coverage
pytest tests/ --cov=app --cov-report=html
# View coverage report
open htmlcov/index.html
```

---

## 📈 Monitoring

### Built-in Endpoints

**Health Check:**
```bash
curl http://localhost:7860/health
```

**Statistics:**
```bash
curl http://localhost:7860/stats
```

### Logs

**Development:**
```bash
python main.py
# Logs appear in console
```

**Docker:**
```bash
docker logs -f container_name
```

**HuggingFace Spaces:**
- View in Space dashboard → Logs tab

---

## 💰 Cost Estimation

### HuggingFace Spaces

| Tier | CPU | RAM | Price | Use Case |
|------|-----|-----|-------|----------|
| Basic | 2 | 16GB | **FREE** | Demo, testing |
| CPU Upgrade | 4 | 32GB | $0.50/hr | Production |
| GPU T4 | - | - | $0.60/hr | Heavy load |
| GPU A10G | - | - | $1.50/hr | Enterprise |

**Monthly Costs (if always on):**
- Free: $0
- CPU Upgrade: ~$360/month
- GPU T4: ~$432/month

**Recommendation:** Start free, upgrade based on usage

### Alternatives

**AWS ECS Fargate:** ~$30-100/month  
**Google Cloud Run:** Pay per request (~$10-50/month)  
**DigitalOcean App:** $12-24/month  
**Self-hosted VPS:** $5-20/month

---

## 🔄 CI/CD Pipeline

### Automated with GitHub Actions

```
Push to GitHub
      ↓
   [Run Tests]
      ↓
  [Build Docker]
      ↓
   [Test Container]
      ↓
[Deploy to HuggingFace]
```

**Setup:**
1. Add secrets in GitHub repo settings:
   - `HF_TOKEN`: HuggingFace access token
   - `HF_SPACE`: Your space name (username/space-name)

2. Push to main branch → Auto-deploy! ✨

---

## 📚 Documentation Access

| Document | Purpose |
|----------|---------|
| `README.md` | Overview, API docs, usage examples |
| `QUICKSTART.md` | 5-minute setup guide |
| `DEPLOYMENT.md` | Production deployment |
| `STRUCTURE.md` | Code organization |
| `/docs` endpoint | Interactive API documentation |

---

## 🎓 Learning Resources

### FastAPI
- Docs: https://fastapi.tiangolo.com
- Tutorial: https://fastapi.tiangolo.com/tutorial

### HuggingFace
- Spaces: https://huggingface.co/docs/hub/spaces
- Transformers: https://huggingface.co/docs/transformers

### Docker
- Getting Started: https://docs.docker.com/get-started

---

## 🐛 Troubleshooting

### Common Issues

**Problem:** "Tesseract not found"  
**Solution:** `apt-get install tesseract-ocr`

**Problem:** "Poppler not found"  
**Solution:** `apt-get install poppler-utils`

**Problem:** Slow processing  
**Solution:** Lower DPI to 150-200

**Problem:** Out of memory  
**Solution:** Upgrade hardware or reduce DPI

**Problem:** Model not loading  
**Solution:** Check internet, wait for download

### Debug Mode

```python
# In main.py, add debug mode
if __name__ == "__main__":
    uvicorn.run("main:app", host="0.0.0.0", port=7860, reload=True, log_level="debug")
```

---

## ✅ Checklist for Production

- [ ] Test all endpoints thoroughly
- [ ] Add API key authentication
- [ ] Implement rate limiting
- [ ] Set up monitoring (Sentry, DataDog, etc.)
- [ ] Configure auto-scaling
- [ ] Set up backups
- [ ] Add usage analytics
- [ ] Create user documentation
- [ ] Set up SSL/TLS (HF provides by default)
- [ ] Test with large files
- [ ] Load testing
- [ ] Security audit
- [ ] Legal compliance (GDPR, etc.)

---

## 🎉 You're Ready!

Your FastAPI PDF Redaction application is complete and ready to deploy!

### Next Steps:
1. ✨ Deploy to HuggingFace Spaces (easiest)
2. 🧪 Test with real PDFs
3. 📊 Monitor usage
4. 🔒 Add security for production
5. 🚀 Scale as needed

### Support:
- 📖 Read the documentation
- 🐛 Check troubleshooting guide
- 💬 HuggingFace community forums
- 📧 Create issues on your repo

**Happy Deploying! 🚀**

	# 🚀 Complete FastAPI Deployment Package

	## 📦 What You've Got

	A production-ready FastAPI application for PDF redaction with Named Entity Recognition, ready to deploy on HuggingFace Spaces or any cloud platform.

	---

	## 📁 Directory Structure

	```
	pdf-redaction-api/
	│
	├── 📄 main.py # FastAPI application
	├── 🐳 Dockerfile # Production container
	├── 🐳 docker-compose.yml # Local development
	├── 📋 requirements.txt # Python dependencies
	│
	├── 📱 app/
	│ ├── __init__.py
	│ └── redaction.py # Core redaction engine
	│
	├── 📂 uploads/ # Temporary uploads
	│ └── .gitkeep
	│
	├── 📂 outputs/ # Redacted PDFs
	│ └── .gitkeep
	│
	├── 🧪 tests/
	│ └── test_api.py # API tests
	│
	├── 📚 Documentation/
	│ ├── README.md # Main docs (for HF Spaces)
	│ ├── DEPLOYMENT.md # Deployment guide
	│ ├── QUICKSTART.md # Quick start guide
	│ └── STRUCTURE.md # Project structure
	│
	├── 🔧 Configuration/
	│ ├── .env.example # Environment variables
	│ ├── .gitignore # Git ignore
	│ └── .dockerignore # Docker ignore
	│
	├── 🤖 .github/
	│ └── workflows/
	│ └── ci-cd.yml # GitHub Actions CI/CD
	│
	├── 📝 client_example.py # Example API client
	└── 📜 LICENSE # MIT License
	```

	---

	## ✨ Features

	### Core Functionality
	✅ PDF upload and processing
	✅ OCR with pytesseract (configurable DPI)
	✅ Named Entity Recognition (NER)
	✅ Accurate coordinate-based redaction
	✅ Multiple entity type support
	✅ Downloadable redacted PDFs

	### API Features
	✅ RESTful API with FastAPI
	✅ Automatic OpenAPI documentation
	✅ File upload handling
	✅ Background task cleanup
	✅ Health checks
	✅ Statistics endpoint
	✅ CORS support

	### DevOps
	✅ Docker containerization
	✅ Docker Compose for local dev
	✅ GitHub Actions CI/CD
	✅ HuggingFace Spaces ready
	✅ Comprehensive testing
	✅ Logging and monitoring

	---

	## 🎯 Quick Deployment Paths

	### Option 1: HuggingFace Spaces (Recommended for Demo)

	Time: 10 minutes

	```bash
	# 1. Create Space on HuggingFace (select Docker SDK)
	# 2. Clone your space
	git clone https://huggingface.co/spaces/YOUR_USERNAME/pdf-redaction-api
	cd pdf-redaction-api

	# 3. Copy all files
	cp -r /path/to/pdf-redaction-api/* .

	# 4. Deploy
	git add .
	git commit -m "Initial deployment"
	git push
	```

	Your API will be at: `https://YOUR_USERNAME-pdf-redaction-api.hf.space`

	Cost: FREE (with CPU Basic tier)

	---

	### Option 2: Docker Locally

	Time: 5 minutes

	```bash
	# Build
	docker build -t pdf-redaction-api .

	# Run
	docker run -p 7860:7860 pdf-redaction-api

	# Test
	curl http://localhost:7860/health
	```

	---

	### Option 3: Direct Python

	Time: 3 minutes

	```bash
	# Install dependencies
	sudo apt-get install tesseract-ocr poppler-utils
	pip install -r requirements.txt

	# Run
	python main.py

	# Access at http://localhost:7860
	```

	---

	## 🔌 API Endpoints

	### Core Endpoints

	\| Method \| Endpoint \| Description \|
	\|--------\|----------\|-------------\|
	\| POST \| `/redact` \| Upload and redact PDF \|
	\| GET \| `/download/{job_id}` \| Download redacted PDF \|
	\| GET \| `/health` \| Health check \|
	\| GET \| `/stats` \| API statistics \|
	\| DELETE \| `/cleanup/{job_id}` \| Manual cleanup \|
	\| GET \| `/docs` \| Interactive API docs \|

	### Example Usage

	cURL:
	```bash
	curl -X POST "http://localhost:7860/redact" \
	-F "file=@document.pdf" \
	-F "dpi=300"
	```

	Python:
	```python
	import requests

	response = requests.post(
	"http://localhost:7860/redact",
	files={"file": open("document.pdf", "rb")},
	params={"dpi": 300}
	)

	job_id = response.json()["job_id"]
	redacted = requests.get(f"http://localhost:7860/download/{job_id}")
	```

	---

	## 🎨 Architecture

	```
	┌─────────────────────────────────────────────────────────┐
	│ CLIENT REQUEST │
	│ (Upload PDF via POST /redact) │
	└─────────────────────────────────────────────────────────┘
	↓
	┌─────────────────────────────────────────────────────────┐
	│ FASTAPI (main.py) │
	│ • Validate file │
	│ • Generate job_id │
	│ • Save to uploads/ │
	└─────────────────────────────────────────────────────────┘
	↓
	┌─────────────────────────────────────────────────────────┐
	│ PDFRedactor (app/redaction.py) │
	│ │
	│ ┌─────────────────────────────────────────┐ │
	│ │ 1. OCR (pytesseract) │ │
	│ │ • Convert PDF → Images (pdf2image) │ │
	│ │ • Extract text + bounding boxes │ │
	│ │ • Store image dimensions │ │
	│ └─────────────────────────────────────────┘ │
	│ ↓ │
	│ ┌─────────────────────────────────────────┐ │
	│ │ 2. NER (HuggingFace Transformers) │ │
	│ │ • Load model │ │
	│ │ • Identify entities in text │ │
	│ │ • Return entity types + positions │ │
	│ └─────────────────────────────────────────┘ │
	│ ↓ │
	│ ┌─────────────────────────────────────────┐ │
	│ │ 3. Mapping │ │
	│ │ • Create character span index │ │
	│ │ • Match NER entities to OCR boxes │ │
	│ └─────────────────────────────────────────┘ │
	│ ↓ │
	│ ┌─────────────────────────────────────────┐ │
	│ │ 4. Redaction (pypdf) │ │
	│ │ • Scale image coords → PDF coords │ │
	│ │ • Create black rectangle annotations │ │
	│ │ • Write redacted PDF │ │
	│ └─────────────────────────────────────────┘ │
	└─────────────────────────────────────────────────────────┘
	↓
	┌─────────────────────────────────────────────────────────┐
	│ RESPONSE │
	│ • job_id │
	│ • List of entities │
	│ • Download URL │
	└─────────────────────────────────────────────────────────┘
	```

	---

	## 🔐 Security Considerations

	### Current Implementation
	- ✅ File validation (PDF only)
	- ✅ Temporary file cleanup
	- ✅ CORS middleware
	- ✅ Error handling

	### For Production (TODO)
	- ⚠️ Add API key authentication
	- ⚠️ Implement rate limiting
	- ⚠️ Add file size limits
	- ⚠️ Use HTTPS only
	- ⚠️ Implement user quotas
	- ⚠️ Add input sanitization

	Example API Key Auth:
	```python
	# Add to main.py
	from fastapi import Security, HTTPException
	from fastapi.security import APIKeyHeader

	API_KEY = "your-secret-key"
	api_key_header = APIKeyHeader(name="X-API-Key")

	def verify_api_key(key: str = Security(api_key_header)):
	if key != API_KEY:
	raise HTTPException(401, "Invalid API Key")
	```

	---

	## 📊 Performance Tuning

	### DPI Settings

	\| DPI \| Quality \| Speed \| Use Case \|
	\|-----\|---------\|-------\|----------\|
	\| 150 \| Low \| Fast \| Quick previews \|
	\| 200 \| Medium \| Medium \| General use \|
	\| 300 \| High \| Slow \| Recommended \|
	\| 600 \| Very High \| Very Slow \| Critical documents \|

	### Hardware Requirements

	Minimum (Free Tier):
	- CPU: 2 cores
	- RAM: 2GB
	- Storage: 1GB

	Recommended (Production):
	- CPU: 4+ cores
	- RAM: 8GB
	- Storage: 10GB
	- GPU: Optional (speeds up NER)

	---

	## 🧪 Testing

	```bash
	# Install test dependencies
	pip install pytest pytest-cov httpx

	# Run tests
	pytest tests/ -v

	# With coverage
	pytest tests/ --cov=app --cov-report=html

	# View coverage report
	open htmlcov/index.html
	```

	---

	## 📈 Monitoring

	### Built-in Endpoints

	Health Check:
	```bash
	curl http://localhost:7860/health
	```

	Statistics:
	```bash
	curl http://localhost:7860/stats
	```

	### Logs

	Development:
	```bash
	python main.py
	# Logs appear in console
	```

	Docker:
	```bash
	docker logs -f container_name
	```

	HuggingFace Spaces:
	- View in Space dashboard → Logs tab

	---

	## 💰 Cost Estimation

	### HuggingFace Spaces

	\| Tier \| CPU \| RAM \| Price \| Use Case \|
	\|------\|-----\|-----\|-------\|----------\|
	\| Basic \| 2 \| 16GB \| FREE \| Demo, testing \|
	\| CPU Upgrade \| 4 \| 32GB \| $0.50/hr \| Production \|
	\| GPU T4 \| - \| - \| $0.60/hr \| Heavy load \|
	\| GPU A10G \| - \| - \| $1.50/hr \| Enterprise \|

	Monthly Costs (if always on):
	- Free: $0
	- CPU Upgrade: ~$360/month
	- GPU T4: ~$432/month

	Recommendation: Start free, upgrade based on usage

	### Alternatives

	AWS ECS Fargate: ~$30-100/month
	Google Cloud Run: Pay per request (~$10-50/month)
	DigitalOcean App: $12-24/month
	Self-hosted VPS: $5-20/month

	---

	## 🔄 CI/CD Pipeline

	### Automated with GitHub Actions

	```
	Push to GitHub
	↓
	[Run Tests]
	↓
	[Build Docker]
	↓
	[Test Container]
	↓
	[Deploy to HuggingFace]
	```

	Setup:
	1. Add secrets in GitHub repo settings:
	- `HF_TOKEN`: HuggingFace access token
	- `HF_SPACE`: Your space name (username/space-name)

	2. Push to main branch → Auto-deploy! ✨

	---

	## 📚 Documentation Access

	\| Document \| Purpose \|
	\|----------\|---------\|
	\| `README.md` \| Overview, API docs, usage examples \|
	\| `QUICKSTART.md` \| 5-minute setup guide \|
	\| `DEPLOYMENT.md` \| Production deployment \|
	\| `STRUCTURE.md` \| Code organization \|
	\| `/docs` endpoint \| Interactive API documentation \|

	---

	## 🎓 Learning Resources

	### FastAPI
	- Docs: https://fastapi.tiangolo.com
	- Tutorial: https://fastapi.tiangolo.com/tutorial

	### HuggingFace
	- Spaces: https://huggingface.co/docs/hub/spaces
	- Transformers: https://huggingface.co/docs/transformers

	### Docker
	- Getting Started: https://docs.docker.com/get-started

	---

	## 🐛 Troubleshooting

	### Common Issues

	Problem: "Tesseract not found"
	Solution: `apt-get install tesseract-ocr`

	Problem: "Poppler not found"
	Solution: `apt-get install poppler-utils`

	Problem: Slow processing
	Solution: Lower DPI to 150-200

	Problem: Out of memory
	Solution: Upgrade hardware or reduce DPI

	Problem: Model not loading
	Solution: Check internet, wait for download

	### Debug Mode

	```python
	# In main.py, add debug mode
	if __name__ == "__main__":
	uvicorn.run("main:app", host="0.0.0.0", port=7860, reload=True, log_level="debug")
	```

	---

	## ✅ Checklist for Production

	- [ ] Test all endpoints thoroughly
	- [ ] Add API key authentication
	- [ ] Implement rate limiting
	- [ ] Set up monitoring (Sentry, DataDog, etc.)
	- [ ] Configure auto-scaling
	- [ ] Set up backups
	- [ ] Add usage analytics
	- [ ] Create user documentation
	- [ ] Set up SSL/TLS (HF provides by default)
	- [ ] Test with large files
	- [ ] Load testing
	- [ ] Security audit
	- [ ] Legal compliance (GDPR, etc.)

	---

	## 🎉 You're Ready!

	Your FastAPI PDF Redaction application is complete and ready to deploy!

	### Next Steps:
	1. ✨ Deploy to HuggingFace Spaces (easiest)
	2. 🧪 Test with real PDFs
	3. 📊 Monitor usage
	4. 🔒 Add security for production
	5. 🚀 Scale as needed

	### Support:
	- 📖 Read the documentation
	- 🐛 Check troubleshooting guide
	- 💬 HuggingFace community forums
	- 📧 Create issues on your repo

	Happy Deploying! 🚀