example.py

"""Example usage of PyCompat model from Hugging Face Hub."""

from pycompat_model import PyCompatModel

# Load from local directory (after downloading from Hub)
model = PyCompatModel.load(".")

# Single prediction
result = model.predict("boto3", "1.42.49", "3.12", "darwin_x86_64")
print(f"Compatible: {result['is_compatible']} (confidence: {result['confidence']:.0%})")

# Recommendations
print("\nTop 5 recommendations for alembic on Python 3.9:")
for r in model.recommend("alembic", "3.9", top_n=5):
    s = "✅" if r["is_compatible"] else "❌"
    print(f"  v{r['version']} {s} ({r['confidence']:.0%})")

# Batch prediction
results = model.predict_batch([
    {"package": "boto3", "version": "1.42.49", "python_version": "3.12"},
    {"package": "alembic", "version": "1.18.4", "python_version": "3.9"},
    {"package": "azure-core", "version": "1.38.0", "python_version": "3.11"},
])
print("\nBatch results:")
for r in results:
    s = "✅" if r["is_compatible"] else "❌"
    print(f"  {r['package']} v{r['version']} on Py{r['python_version']}: {s}")