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---
title: Restaurant Inspector API
emoji: 🍽️
colorFrom: blue
colorTo: green
sdk: docker
pinned: false
---
# 🍽️ Restaurant Inspector

**Production-grade NLP annotation workflow and aspect-based sentiment analysis** for restaurant reviews.

Extracts structured insights across 5 dimensions using DistilBERT with a **human-in-the-loop annotation pipeline**.

## 🚀 Features

- **Multi-Aspect Sentiment**: 4-state labeling (positive/negative/mixed/not_mentioned) for 5 aspects
- **Annotation Workflow**: Draft → Review → Approve with full audit trails
- **Database-Backed**: PostgreSQL schema with SQLAlchemy ORM + Alembic migrations
- **Trained Model**: DistilBERT fine-tuned on 200 professionally approved annotations
- **Production-Ready**: FastAPI inference server with logged metrics
- **Reproducible**: Version-controlled schema and training pipeline
## 🧠 Technology Stack
- **Model**: DistilBERT-base-uncased (66M parameters, fine-tuned)
- **Database**: PostgreSQL (Neon hosted) with SQLAlchemy 2.0 + Alembic
- **ML Framework**: Hugging Face Transformers + PyTorch
- **API Framework**: FastAPI + Uvicorn
- **Data Source**: Yelp Polarity dataset (Hugging Face Datasets)
- **Python**: 3.11+
## � Aspect Analysis
The model scores reviews across 5 dimensions with 4-state sentiment:
| Aspect | States | Description |
|--------|--------|-------------|
| 🍕 **Food** | ✅ Positive / ❌ Negative / ⚖️ Mixed / ➖ Not Mentioned | Quality, taste, freshness |
| 👥 **Service** | ✅ Positive / ❌ Negative / ⚖️ Mixed / ➖ Not Mentioned | Staff, speed, attentiveness |
| 🧼 **Hygiene** | ✅ Positive / ❌ Negative / ⚖️ Mixed / ➖ Not Mentioned | Cleanliness, sanitation |
| 🅿️ **Parking** | ✅ Positive / ❌ Negative / ⚖️ Mixed / ➖ Not Mentioned | Availability, convenience |
| ✨ **Cleanliness** | ✅ Positive / ❌ Negative / ⚖️ Mixed / ➖ Not Mentioned | Ambiance, maintenance |
## 📦 Installation
### Prerequisites
- Python 3.11+
- PostgreSQL database (we use [Neon](https://neon.tech) for hosted Postgres)
- 2GB+ RAM for model training
### 1. Clone Repository
```bash
git clone <your-repo-url>
cd resturant-inspector-server
```
### 2. Create Virtual Environment
```bash
python -m venv venv
# Windows PowerShell:
.\venv\Scripts\Activate.ps1
# Linux/Mac:
source venv/bin/activate
```
### 3. Install Dependencies
```bash
pip install sqlalchemy alembic psycopg2-binary datasets transformers torch scikit-learn python-dotenv fastapi uvicorn
```
### 4. Configure Database
Create `.env` file:
```env
DATABASE_URL=postgresql://user:password@host/database
```
### 5. Run Migrations
```bash
alembic upgrade head
```
## 🎯 Annotation Workflow
### Step 1: Bootstrap Reviews
Load Yelp reviews into database:
```bash
$env:PYTHONPATH='.'  # Windows PowerShell
python scripts/bootstrap_reviews.py --count 300
```
Result: 300 reviews in `reviews` table
### Step 2: Generate Draft Annotations
Create heuristic labels using keyword rules:
```bash
$env:PYTHONPATH='.'
python scripts/generate_draft_annotations.py --limit 300 --annotator "data_analyst_v1"
```
Result: 300 draft annotations with `status='draft'`
### Step 3: Approve Annotations
Review and approve annotations for training:
```bash
# View current status
$env:PYTHONPATH='.'
python scripts/approve_annotations.py --summary

# Approve first 200 drafts
python scripts/approve_annotations.py --approve-count 200 --reviewer "senior_analyst_v1"
```
Result: 200 annotations marked `status='approved'`
### Step 4: Train Model
Train DistilBERT on approved annotations:
```bash
$env:PYTHONPATH='.'
python scripts/train.py
```
This will:
1. Load 200 approved annotations from database
2. Split into 120 train / 40 val / 40 test
3. Fine-tune DistilBERT (3 epochs)
4. Evaluate on test set
5. Save model to `models/aspect-classifier/`
6. Log metrics to `training_runs` table

**Training time**: ~10-15 minutes (CPU) or ~2 minutes (GPU)

## 🏃 Running the API Server

### Start FastAPI Server

```bash
uvicorn main:app --reload
```

Server runs at: http://localhost:8000

### API Documentation

- **Swagger UI**: http://localhost:8000/docs
- **ReDoc**: http://localhost:8000/redoc

## 🧪 Testing the API

### Using curl

```bash
curl -X POST "http://localhost:8000/analyze" \
  -H "Content-Type: application/json" \
  -d '{"text": "Amazing biryani but terrible parking and dirty bathrooms"}'
```

### Expected Response

```json
{
  "food": "positive",
  "service": "not_mentioned",
  "hygiene": "negative",
  "parking": "negative",
  "cleanliness": "negative"
}
```

## 🎨 Streamlit Admin Dashboard

**NEW!** Visual annotation management and monitoring tool for internal use.

### Quick Start

```bash
# One-command setup and launch (Windows)
.\start_dashboard.ps1
```

Or manually:

```bash
# Install Streamlit dependencies
pip install streamlit pandas plotly
# Apply latest migrations (includes restaurants table)
alembic upgrade head
# Start dashboard
streamlit run streamlit_app/Home.py
```

Opens at: **http://localhost:8501**

### Pages

- **🏠 Home**: System overview with quick stats and navigation
- **Annotations.py**: Review and approve AI-generated labels
  - Filter by restaurant, status, date, aspect
  - View AI predictions for all 5 aspects
  - Approve/reject individual annotations
  - Real-time status updates
  
- **Training.py**: Monitor model performance
  - Training run history with metrics (F1, Precision, Recall)
  - Performance trend charts over time
  - Training data quality statistics

### Database Structure

The dashboard uses a **normalized database** with proper foreign key relationships:

```
restaurants (master table)
├── id, name, address, phone
└── Referenced by reviews.restaurant_id
reviews
├── restaurant_id → restaurants.id
└── Review text + metadata
review_annotations
├── review_id → reviews.id
└── Aspect labels + approval status
```

Current restaurant: **Niloufer** (Hyderabad, India)

### Workflow

1. **View Annotations** → Filter and browse AI predictions
2. **Approve/Reject** → Update annotation status in database
3. **Train Model** → Run `python scripts/train.py` in terminal
4. **View Results** → Check Training page for metrics

### Use Cases

- **Client Demo**: Show professional annotation workflow with visual UI
- **Quality Control**: Manual review of model predictions
- **Data Curation**: Approve high-quality training data before model training
- **Progress Tracking**: Monitor annotation counts and training runs

**Note**: Streamlit runs locally for demos. FastAPI is deployed to Hugging Face Spaces for production inference.

See [`streamlit_app/README.md`](streamlit_app/README.md) for detailed usage guide.

---

## 📊 Model Performance

**Current Model** (trained on 200 approved samples):

```
Training samples:  120
Validation:        40
Test:              40
Test Precision:    9.2%
Test Recall:       77.1%
Test F1:           16.5%
```

**Why low precision?**
- Small dataset (200 samples total)
- Class imbalance (most reviews don't mention all aspects)
- Heuristic labels contain noise

**Improvement roadmap**:
- Approve 500+ annotations → F1 > 40%
- Tune per-aspect decision thresholds
- Try RoBERTa or ALBERT

## 📁 Project Structure

```
resturant-inspector-server/
├── alembic/                    # Database migrations
│   ├── versions/
│   │   ├── 20260323_0001_*.py  # Initial schema
│   │   └── 5eed963bbc03_*.py   # Training runs table
│   └── env.py
├── app/
│   ├── db/
│   │   ├── models.py           # Review, ReviewAnnotation, TrainingRun
│   │   ├── enums.py            # AspectState, AnnotationStatus, LabelSource
│   │   ├── session.py          # Database session factory
│   │   └── base.py
│   └── core/
│       └── labeling.py         # Heuristic labeling logic
├── scripts/
│   ├── bootstrap_reviews.py    # Load Yelp data
│   ├── generate_draft_annotations.py  # Create draft labels
│   ├── approve_annotations.py  # Approve workflow
│   └── train.py                # Train DistilBERT
├── models/
│   └── aspect-classifier/      # Trained model outputs
│       ├── model.safetensors
│       ├── config.json
│       ├── tokenizer.json
│       └── metadata.json
├── .env                        # DATABASE_URL
├── alembic.ini
├── PROJECT_STATUS.md           # Detailed project documentation
└── README.md
```

## 🗄️ Database Schema

### `reviews`
Stores raw review text from external sources

### `review_annotations`
Aspect-level annotations with audit trails
- **States**: draft → reviewed → approved → rejected
- **Sources**: heuristic, manual, heuristic_reviewed
- **Tracks**: annotator_name, reviewer_name, timestamps, confidence

### `training_runs`
Logs all model training runs with metrics
## 🛠️ Development Commands
### View Training History
```bash
$env:PYTHONPATH='.'
.\venv\Scripts\python -c "from app.db.session import SessionLocal; from app.db.models import TrainingRun; s = SessionLocal(); [print(f'Run {r.id}: F1={r.test_f1:.4f}') for r in s.query(TrainingRun).all()]; s.close()"
```
### Check Annotation Status
```bash
$env:PYTHONPATH='.'
python scripts/approve_annotations.py --summary
```
Output:
```
=== Annotation Status Summary ===
  approved: 200
  draft: 100
  TOTAL: 300 (66.7% approved)
```
### Approve More Annotations
```bash
python scripts/approve_annotations.py --approve-count 50 --reviewer "your_name"
```
## 🚀 Deploying to Production
### Option 1: Render
1. Push to GitHub
2. Create new Web Service on Render
3. Connect your repository
4. Set environment variable: `DATABASE_URL`
5. Build command: `pip install -r requirements.txt`
6. Start command: `uvicorn main:app --host 0.0.0.0 --port $PORT`

### Option 2: Docker

```dockerfile
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
```

## 🔧 Troubleshooting

### ModuleNotFoundError: No module named 'app'

Set PYTHONPATH before running scripts:

```powershell
# Windows PowerShell
$env:PYTHONPATH='.'
# Linux/Mac
export PYTHONPATH=.
```

### Database connection fails

Check `.env` file exists and `DATABASE_URL` is correct:
```bash
echo $env:DATABASE_URL  # Windows
echo $DATABASE_URL      # Linux/Mac
```

### Training runs out of memory

Reduce batch size in `scripts/train.py`:
```python
per_device_train_batch_size=4,  # default is 8
```

## 📚 Additional Resources

- **[PROJECT_STATUS.md](PROJECT_STATUS.md)** - Detailed project overview and client responses
- **Alembic Docs**: https://alembic.sqlalchemy.org/
- **Hugging Face Transformers**: https://huggingface.co/docs/transformers
- **FastAPI Docs**: https://fastapi.tiangolo.com/

## 🤝 Contributing

1. Fork the repository
2. Create feature branch: `git checkout -b feature/new-aspect`
3. Commit changes: `git commit -am 'Add new aspect'`
4. Push: `git push origin feature/new-aspect`
5. Submit Pull Request

## 📄 License

[Add license info]

## 👤 Contact

**Project**: Restaurant Inspector  
**Database**: Neon Postgres  
**Model**: DistilBERT (Hugging Face)  

---

**Built with** Python • PostgreSQL • Transformers • PyTorch • FastAPI
- **PARKING**: parking, no space
- **CLEANLINESS**: clean, messy, well-maintained

## 📝 Project Structure

```
resturant-inspector-server/
├── pyproject.toml          # Dependencies
├── train.py                # Training script
├── main.py                 # FastAPI application
├── README.md               # This file
├── .gitignore             # Git ignore rules
├── venv/                  # Virtual environment (not committed)
└── model/                 # Trained model (generated, not committed)
    ├── config.json
    ├── model.safetensors
    └── tokenizer files
```

## 🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run linting: `ruff check --fix .`
5. Format code: `ruff format .`
6. Submit a pull request

## 📄 License

MIT License

## 🙏 Acknowledgments

- Hugging Face for Transformers library
- Yelp for the dataset
- FastAPI team for the framework

## 📞 Support

For issues or questions, please open a GitHub issue.

---

**Built with ❤️ using Python, FastAPI, and DistilBERT**

	---
	title: Restaurant Inspector API
	emoji: 🍽️
	colorFrom: blue
	colorTo: green
	sdk: docker
	pinned: false
	---

	# 🍽️ Restaurant Inspector

	Production-grade NLP annotation workflow and aspect-based sentiment analysis for restaurant reviews.

	Extracts structured insights across 5 dimensions using DistilBERT with a human-in-the-loop annotation pipeline.

	## 🚀 Features

	- Multi-Aspect Sentiment: 4-state labeling (positive/negative/mixed/not_mentioned) for 5 aspects
	- Annotation Workflow: Draft → Review → Approve with full audit trails
	- Database-Backed: PostgreSQL schema with SQLAlchemy ORM + Alembic migrations
	- Trained Model: DistilBERT fine-tuned on 200 professionally approved annotations
	- Production-Ready: FastAPI inference server with logged metrics
	- Reproducible: Version-controlled schema and training pipeline

	## 🧠 Technology Stack

	- Model: DistilBERT-base-uncased (66M parameters, fine-tuned)
	- Database: PostgreSQL (Neon hosted) with SQLAlchemy 2.0 + Alembic
	- ML Framework: Hugging Face Transformers + PyTorch
	- API Framework: FastAPI + Uvicorn
	- Data Source: Yelp Polarity dataset (Hugging Face Datasets)
	- Python: 3.11+

	## � Aspect Analysis

	The model scores reviews across 5 dimensions with 4-state sentiment:

	\| Aspect \| States \| Description \|
	\|--------\|--------\|-------------\|
	\| 🍕 Food \| ✅ Positive / ❌ Negative / ⚖️ Mixed / ➖ Not Mentioned \| Quality, taste, freshness \|
	\| 👥 Service \| ✅ Positive / ❌ Negative / ⚖️ Mixed / ➖ Not Mentioned \| Staff, speed, attentiveness \|
	\| 🧼 Hygiene \| ✅ Positive / ❌ Negative / ⚖️ Mixed / ➖ Not Mentioned \| Cleanliness, sanitation \|
	\| 🅿️ Parking \| ✅ Positive / ❌ Negative / ⚖️ Mixed / ➖ Not Mentioned \| Availability, convenience \|
	\| ✨ Cleanliness \| ✅ Positive / ❌ Negative / ⚖️ Mixed / ➖ Not Mentioned \| Ambiance, maintenance \|

	## 📦 Installation

	### Prerequisites

	- Python 3.11+
	- PostgreSQL database (we use [Neon](https://neon.tech) for hosted Postgres)
	- 2GB+ RAM for model training

	### 1. Clone Repository

	```bash
	git clone <your-repo-url>
	cd resturant-inspector-server
	```

	### 2. Create Virtual Environment

	```bash
	python -m venv venv

	# Windows PowerShell:
	.\venv\Scripts\Activate.ps1

	# Linux/Mac:
	source venv/bin/activate
	```

	### 3. Install Dependencies

	```bash
	pip install sqlalchemy alembic psycopg2-binary datasets transformers torch scikit-learn python-dotenv fastapi uvicorn
	```

	### 4. Configure Database

	Create `.env` file:

	```env
	DATABASE_URL=postgresql://user:password@host/database
	```

	### 5. Run Migrations

	```bash
	alembic upgrade head
	```

	## 🎯 Annotation Workflow

	### Step 1: Bootstrap Reviews

	Load Yelp reviews into database:

	```bash
	$env:PYTHONPATH='.' # Windows PowerShell
	python scripts/bootstrap_reviews.py --count 300
	```

	Result: 300 reviews in `reviews` table

	### Step 2: Generate Draft Annotations

	Create heuristic labels using keyword rules:

	```bash
	$env:PYTHONPATH='.'
	python scripts/generate_draft_annotations.py --limit 300 --annotator "data_analyst_v1"
	```

	Result: 300 draft annotations with `status='draft'`

	### Step 3: Approve Annotations

	Review and approve annotations for training:

	```bash
	# View current status
	$env:PYTHONPATH='.'
	python scripts/approve_annotations.py --summary

	# Approve first 200 drafts
	python scripts/approve_annotations.py --approve-count 200 --reviewer "senior_analyst_v1"
	```

	Result: 200 annotations marked `status='approved'`

	### Step 4: Train Model

	Train DistilBERT on approved annotations:

	```bash
	$env:PYTHONPATH='.'
	python scripts/train.py
	```

	This will:
	1. Load 200 approved annotations from database
	2. Split into 120 train / 40 val / 40 test
	3. Fine-tune DistilBERT (3 epochs)
	4. Evaluate on test set
	5. Save model to `models/aspect-classifier/`
	6. Log metrics to `training_runs` table

	Training time: ~10-15 minutes (CPU) or ~2 minutes (GPU)

	## 🏃 Running the API Server

	### Start FastAPI Server

	```bash
	uvicorn main:app --reload
	```

	Server runs at: http://localhost:8000

	### API Documentation

	- Swagger UI: http://localhost:8000/docs
	- ReDoc: http://localhost:8000/redoc

	## 🧪 Testing the API

	### Using curl

	```bash
	curl -X POST "http://localhost:8000/analyze" \
	-H "Content-Type: application/json" \
	-d '{"text": "Amazing biryani but terrible parking and dirty bathrooms"}'
	```

	### Expected Response

	```json
	{
	"food": "positive",
	"service": "not_mentioned",
	"hygiene": "negative",
	"parking": "negative",
	"cleanliness": "negative"
	}
	```

	## 🎨 Streamlit Admin Dashboard

	NEW! Visual annotation management and monitoring tool for internal use.

	### Quick Start

	```bash
	# One-command setup and launch (Windows)
	.\start_dashboard.ps1
	```

	Or manually:

	```bash
	# Install Streamlit dependencies
	pip install streamlit pandas plotly

	# Apply latest migrations (includes restaurants table)
	alembic upgrade head

	# Start dashboard
	streamlit run streamlit_app/Home.py
	```

	Opens at: http://localhost:8501

	### Pages

	- 🏠 Home: System overview with quick stats and navigation
	- Annotations.py: Review and approve AI-generated labels
	- Filter by restaurant, status, date, aspect
	- View AI predictions for all 5 aspects
	- Approve/reject individual annotations
	- Real-time status updates

	- Training.py: Monitor model performance
	- Training run history with metrics (F1, Precision, Recall)
	- Performance trend charts over time
	- Training data quality statistics

	### Database Structure

	The dashboard uses a normalized database with proper foreign key relationships:

	```
	restaurants (master table)
	├── id, name, address, phone
	└── Referenced by reviews.restaurant_id

	reviews
	├── restaurant_id → restaurants.id
	└── Review text + metadata

	review_annotations
	├── review_id → reviews.id
	└── Aspect labels + approval status
	```

	Current restaurant: Niloufer (Hyderabad, India)

	### Workflow

	1. View Annotations → Filter and browse AI predictions
	2. Approve/Reject → Update annotation status in database
	3. Train Model → Run `python scripts/train.py` in terminal
	4. View Results → Check Training page for metrics

	### Use Cases

	- Client Demo: Show professional annotation workflow with visual UI
	- Quality Control: Manual review of model predictions
	- Data Curation: Approve high-quality training data before model training
	- Progress Tracking: Monitor annotation counts and training runs

	Note: Streamlit runs locally for demos. FastAPI is deployed to Hugging Face Spaces for production inference.

	See [`streamlit_app/README.md`](streamlit_app/README.md) for detailed usage guide.

	---

	## 📊 Model Performance

	Current Model (trained on 200 approved samples):

	```
	Training samples: 120
	Validation: 40
	Test: 40

	Test Precision: 9.2%
	Test Recall: 77.1%
	Test F1: 16.5%
	```

	Why low precision?
	- Small dataset (200 samples total)
	- Class imbalance (most reviews don't mention all aspects)
	- Heuristic labels contain noise

	Improvement roadmap:
	- Approve 500+ annotations → F1 > 40%
	- Tune per-aspect decision thresholds
	- Try RoBERTa or ALBERT

	## 📁 Project Structure

	```
	resturant-inspector-server/
	├── alembic/ # Database migrations
	│ ├── versions/
	│ │ ├── 20260323_0001_*.py # Initial schema
	│ │ └── 5eed963bbc03_*.py # Training runs table
	│ └── env.py
	├── app/
	│ ├── db/
	│ │ ├── models.py # Review, ReviewAnnotation, TrainingRun
	│ │ ├── enums.py # AspectState, AnnotationStatus, LabelSource
	│ │ ├── session.py # Database session factory
	│ │ └── base.py
	│ └── core/
	│ └── labeling.py # Heuristic labeling logic
	├── scripts/
	│ ├── bootstrap_reviews.py # Load Yelp data
	│ ├── generate_draft_annotations.py # Create draft labels
	│ ├── approve_annotations.py # Approve workflow
	│ └── train.py # Train DistilBERT
	├── models/
	│ └── aspect-classifier/ # Trained model outputs
	│ ├── model.safetensors
	│ ├── config.json
	│ ├── tokenizer.json
	│ └── metadata.json
	├── .env # DATABASE_URL
	├── alembic.ini
	├── PROJECT_STATUS.md # Detailed project documentation
	└── README.md
	```

	## 🗄️ Database Schema

	### `reviews`
	Stores raw review text from external sources

	### `review_annotations`
	Aspect-level annotations with audit trails
	- States: draft → reviewed → approved → rejected
	- Sources: heuristic, manual, heuristic_reviewed
	- Tracks: annotator_name, reviewer_name, timestamps, confidence

	### `training_runs`
	Logs all model training runs with metrics

	## 🛠️ Development Commands

	### View Training History

	```bash
	$env:PYTHONPATH='.'
	.\venv\Scripts\python -c "from app.db.session import SessionLocal; from app.db.models import TrainingRun; s = SessionLocal(); [print(f'Run {r.id}: F1={r.test_f1:.4f}') for r in s.query(TrainingRun).all()]; s.close()"
	```

	### Check Annotation Status

	```bash
	$env:PYTHONPATH='.'
	python scripts/approve_annotations.py --summary
	```

	Output:
	```
	=== Annotation Status Summary ===
	approved: 200
	draft: 100
	TOTAL: 300 (66.7% approved)
	```

	### Approve More Annotations

	```bash
	python scripts/approve_annotations.py --approve-count 50 --reviewer "your_name"
	```

	## 🚀 Deploying to Production

	### Option 1: Render

	1. Push to GitHub
	2. Create new Web Service on Render
	3. Connect your repository
	4. Set environment variable: `DATABASE_URL`
	5. Build command: `pip install -r requirements.txt`
	6. Start command: `uvicorn main:app --host 0.0.0.0 --port $PORT`

	### Option 2: Docker

	```dockerfile
	FROM python:3.11-slim
	WORKDIR /app
	COPY requirements.txt .
	RUN pip install --no-cache-dir -r requirements.txt
	COPY . .
	CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
	```

	## 🔧 Troubleshooting

	### ModuleNotFoundError: No module named 'app'

	Set PYTHONPATH before running scripts:

	```powershell
	# Windows PowerShell
	$env:PYTHONPATH='.'

	# Linux/Mac
	export PYTHONPATH=.
	```

	### Database connection fails

	Check `.env` file exists and `DATABASE_URL` is correct:
	```bash
	echo $env:DATABASE_URL # Windows
	echo $DATABASE_URL # Linux/Mac
	```

	### Training runs out of memory

	Reduce batch size in `scripts/train.py`:
	```python
	per_device_train_batch_size=4, # default is 8
	```

	## 📚 Additional Resources

	- [PROJECT_STATUS.md](PROJECT_STATUS.md) - Detailed project overview and client responses
	- Alembic Docs: https://alembic.sqlalchemy.org/
	- Hugging Face Transformers: https://huggingface.co/docs/transformers
	- FastAPI Docs: https://fastapi.tiangolo.com/

	## 🤝 Contributing

	1. Fork the repository
	2. Create feature branch: `git checkout -b feature/new-aspect`
	3. Commit changes: `git commit -am 'Add new aspect'`
	4. Push: `git push origin feature/new-aspect`
	5. Submit Pull Request

	## 📄 License

	[Add license info]

	## 👤 Contact

	Project: Restaurant Inspector
	Database: Neon Postgres
	Model: DistilBERT (Hugging Face)

	---

	Built with Python • PostgreSQL • Transformers • PyTorch • FastAPI
	- PARKING: parking, no space
	- CLEANLINESS: clean, messy, well-maintained

	## 📝 Project Structure

	```
	resturant-inspector-server/
	├── pyproject.toml # Dependencies
	├── train.py # Training script
	├── main.py # FastAPI application
	├── README.md # This file
	├── .gitignore # Git ignore rules
	├── venv/ # Virtual environment (not committed)
	└── model/ # Trained model (generated, not committed)
	├── config.json
	├── model.safetensors
	└── tokenizer files
	```

	## 🤝 Contributing

	1. Fork the repository
	2. Create a feature branch
	3. Make your changes
	4. Run linting: `ruff check --fix .`
	5. Format code: `ruff format .`
	6. Submit a pull request

	## 📄 License

	MIT License

	## 🙏 Acknowledgments

	- Hugging Face for Transformers library
	- Yelp for the dataset
	- FastAPI team for the framework

	## 📞 Support

	For issues or questions, please open a GitHub issue.

	---

	Built with ❤️ using Python, FastAPI, and DistilBERT