$ pip install jeffy-classify
$ python3

>>> from jeffy.engine import Engine
>>> engine = Engine()
>>> engine.load()

>>> # 13 pretrained classifiers ready

>>> engine.predict("banking77", "I was charged twice for the same transaction")
{"label": "transaction_charged_twice", "confidence": 0.999}

>>> engine.predict("sms_spam", "WINNER! Call now to claim your 1000 dollar prize!")
{"label": "spam", "confidence": 0.983}

>>> engine.predict("ag_news", "Apple stock surges 5% after record iPhone sales")
{"label": "Business", "confidence": 0.933}

# ── Train a custom classifier ──

>>> from jeffy.train import train_classifier
>>> clf = train_classifier(texts=[...], labels=[...], task_id="inbox_router")
Training 'inbox_router': 24 examples, 4 classes
  Encoded 24 texts in 2.5s
  3-fold CV: 70.8% ± 5.9%

>>> # Predictions on NEW messages (not in training data)

>>> clf.predict("Can you send me the Q4 projections?")
{"label": "work", "confidence": 0.59}

>>> clf.predict("Aunt Clara is hosting Thanksgiving this year")
{"label": "family", "confidence": 0.78}

>>> clf.predict("Buy 2 get 1 free — today only!")
{"label": "promo", "confidence": 0.76}

>>> clf.predict("Your credit card statement is available")
{"label": "notification", "confidence": 0.75}

# 24 examples → working classifier. All on CPU, no API keys.
# Custom training confidence varies with dataset size (this is a small smoke test).
# See data/eval_results/benchmark.json for formal evaluation on full datasets.
