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"""
Example usage of the B2B Ecommerce NER model for Hugging Face
"""
import sys
import os
sys.path.append(os.path.dirname(__file__))
from model import B2BEcommerceNER
def main():
"""Demonstrate the B2B Ecommerce NER model usage"""
print("B2B Ecommerce NER Model - Example Usage")
print("=" * 50)
# Sample B2B order texts
sample_orders = [
"Order 5 bottles of Coca Cola 650ML",
"I need 10 packs of Maggi noodles 200G each",
"Send 3 units of Chocolate Cleanser 500ML",
"Please deliver 15 pieces of Golden Dates 250G",
"We want 8 cases of mineral water 1L bottles"
]
try:
# Initialize the model (without loading since we don't have the actual model files yet)
print("Initializing B2B Ecommerce NER model...")
model = B2BEcommerceNER()
print("Model configuration:")
print(f"- Entity labels: {model.entity_labels}")
print(f"- Model path: {model.model_path}")
print(f"- Catalog path: {model.catalog_path}")
print("\nSample order processing would work like this:")
print("-" * 40)
for i, order in enumerate(sample_orders, 1):
print(f"\n{i}. Order: '{order}'")
print(" Expected entities:")
# Manually show expected results (since model isn't loaded)
if "5 bottles of Coca Cola 650ML" in order:
print(" - QUANTITY: '5'")
print(" - UNIT: 'bottles'")
print(" - PRODUCT: 'Coca Cola'")
print(" - SIZE: '650ML'")
elif "10 packs of Maggi" in order:
print(" - QUANTITY: '10'")
print(" - UNIT: 'packs'")
print(" - PRODUCT: 'Maggi noodles'")
print(" - SIZE: '200G'")
elif "3 units of Chocolate Cleanser" in order:
print(" - QUANTITY: '3'")
print(" - UNIT: 'units'")
print(" - PRODUCT: 'Chocolate Cleanser'")
print(" - SIZE: '500ML'")
elif "15 pieces of Golden Dates" in order:
print(" - QUANTITY: '15'")
print(" - UNIT: 'pieces'")
print(" - PRODUCT: 'Golden Dates'")
print(" - SIZE: '250G'")
elif "8 cases of mineral water" in order:
print(" - QUANTITY: '8'")
print(" - UNIT: 'cases'")
print(" - PRODUCT: 'mineral water'")
print(" - SIZE: '1L'")
print("\n" + "=" * 50)
print("To use with actual trained model:")
print("1. Train your model using the main training pipeline")
print("2. Copy the trained spaCy model to huggingface_model/spacy_model/")
print("3. Copy product_catalog.csv to huggingface_model/")
print("4. Use model.predict(texts) for actual entity extraction")
print("\nCode example:")
print("""
# Load pre-trained model
model = B2BEcommerceNER.from_pretrained('path/to/saved/model')
# Extract entities
results = model.predict(['Order 5 Coke Zero 650ML'])
# Access entities
entities = results[0]['entities']
products = entities['products']
quantities = entities['quantities']
catalog_matches = entities['catalog_matches']
""")
except Exception as e:
print(f"Note: {e}")
print("This is expected since the actual model files are not loaded yet.")
if __name__ == "__main__":
main()
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