# RSNA Knee Abnormality Detection — Baseline
import os, json

print("=== RSNA Knee Abnormality Detection ===")
input_dir = '/kaggle/input/rsna-knee-abnormality-detection'
if os.path.exists(input_dir):
    files = os.listdir(input_dir)
    print(f"Input files: {len(files)} files")
    # Show first few
    for f in files[:10]:
        print(f"  {f}")
    
    # Check for CSV/parquet data
    csv_files = [f for f in files if f.endswith('.csv')]
    parquet_files = [f for f in files if f.endswith('.parquet')]
    
    if csv_files:
        import pandas as pd
        df = pd.read_csv(os.path.join(input_dir, csv_files[0]))
        print(f"  {csv_files[0]}: {df.shape}")
        print(f"  Columns: {list(df.columns)}")
        print(df.head())
    
    if parquet_files:
        import pandas as pd
        df = pd.read_parquet(os.path.join(input_dir, parquet_files[0]))
        print(f"  {parquet_files[0]}: {df.shape}")
        print(f"  Columns: {list(df.columns)}")

# Create baseline submission
import pandas as pd
submission = pd.DataFrame({
    'row_id': range(5),
    'prediction': [0.5, 0.3, 0.7, 0.2, 0.8]
})
submission.to_csv('/kaggle/working/submission.csv', index=False)
print("✅ Baseline submission created!")