# Hugging Face Dataset Upload Instructions

## Files to Upload

### Core Dataset Files
1. **README.md** - Complete dataset card with metadata, description, and usage examples
2. **data.csv** - Clean CSV file with 516 scenarios and their misery scores
3. **load_dataset.py** - Python script for easy dataset loading and exploration
4. **requirements.txt** - Dependencies needed to use the dataset

### Supporting Files (Optional)
- **misery_index.py** - Advanced datasets library loading script
- **UPLOAD_INSTRUCTIONS.md** - This file (for reference)

## Upload Steps

### Method 1: Using Hugging Face Hub (Recommended)

1. **Install Hugging Face Hub**:
   ```bash
   pip install huggingface_hub
   ```

2. **Login to Hugging Face**:
   ```bash
   huggingface-cli login
   ```

3. **Create and upload the dataset**:
   ```python
   from huggingface_hub import HfApi, create_repo
   
   # Create repository
   repo_id = "your-username/misery-index"
   create_repo(repo_id, repo_type="dataset")
   
   # Upload files
   api = HfApi()
   api.upload_file(
       path_or_fileobj="README.md",
       path_in_repo="README.md",
       repo_id=repo_id,
       repo_type="dataset"
   )
   
   api.upload_file(
       path_or_fileobj="data.csv",
       path_in_repo="data.csv",
       repo_id=repo_id,
       repo_type="dataset"
   )
   
   api.upload_file(
       path_or_fileobj="load_dataset.py",
       path_in_repo="load_dataset.py",
       repo_id=repo_id,
       repo_type="dataset"
   )
   
   api.upload_file(
       path_or_fileobj="requirements.txt",
       path_in_repo="requirements.txt",
       repo_id=repo_id,
       repo_type="dataset"
   )
   ```

### Method 2: Using Git

1. **Clone the dataset repository**:
   ```bash
   git clone https://huggingface.co/datasets/your-username/misery-index
   cd misery-index
   ```

2. **Copy files**:
   ```bash
   cp /path/to/your/files/* .
   ```

3. **Push to Hugging Face**:
   ```bash
   git add .
   git commit -m "Add Misery Index Dataset"
   git push
   ```

### Method 3: Web Interface

1. Go to [Hugging Face Datasets](https://huggingface.co/new-dataset)
2. Create a new dataset repository
3. Upload files using the web interface
4. Edit README.md directly in the browser if needed

## Usage After Upload

Once uploaded, users can load the dataset in several ways:

### Using Datasets Library
```python
from datasets import load_dataset

# Load from Hugging Face Hub
dataset = load_dataset("your-username/misery-index")
print(dataset["train"][0])
```

### Using Pandas (Direct CSV)
```python
import pandas as pd
from huggingface_hub import hf_hub_download

# Download and load CSV
file_path = hf_hub_download(
    repo_id="your-username/misery-index", 
    filename="data.csv",
    repo_type="dataset"
)
df = pd.read_csv(file_path)
```

### Using the Provided Script
```python
# Download the load_dataset.py script and use it
from huggingface_hub import hf_hub_download
import importlib.util

# Download the script
script_path = hf_hub_download(
    repo_id="your-username/misery-index",
    filename="load_dataset.py", 
    repo_type="dataset"
)

# Load and use
spec = importlib.util.spec_from_file_location("load_dataset", script_path)
load_module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(load_module)

df = load_module.load_misery_dataset("data.csv")
stats = load_module.get_dataset_statistics(df)
```

## Dataset Configuration

The dataset uses these configurations:
- **License**: CC-BY-4.0 (Creative Commons Attribution)
- **Language**: English (en)
- **Task**: Text regression, sentiment analysis, emotion prediction
- **Size**: 516 samples (100<n<1K category)

## Validation Checklist

Before uploading, ensure:
- [ ] README.md contains comprehensive dataset card
- [ ] data.csv has all 516 rows with proper formatting
- [ ] No missing values in critical columns (scenario, misery_score)
- [ ] load_dataset.py script runs without errors
- [ ] requirements.txt includes all necessary dependencies
- [ ] License is properly specified (CC-BY-4.0)
- [ ] Dataset tags are appropriate for discoverability

## Post-Upload Tasks

1. **Test the uploaded dataset**:
   ```python
   from datasets import load_dataset
   ds = load_dataset("your-username/misery-index")
   print(f"Dataset loaded successfully with {len(ds['train'])} samples")
   ```

2. **Update dataset card** if needed using the web interface

3. **Share the dataset** with relevant research communities

4. **Consider creating a model** trained on this dataset

## Support

If you encounter issues:
1. Check the [Hugging Face documentation](https://huggingface.co/docs/datasets/)
2. Visit the [Hugging Face Discord](https://discord.gg/huggingface)
3. Create an issue in the dataset repository