e-commerce-ai-alchemy-engine / sample_implementation.py
babatdaa's picture
Create a Python code template using Hugging Face Transformers and scikit-learn to build a generative AI model that produces marketing content (e.g., email campaigns or social media posts) for e-commerce businesses. Integrate a predictive component that analyzes user data (e.g., purchase history CSV) to forecast customer preferences and tailor the generated text accordingly. Include fine-tuning on a dataset like GPT-2 or Llama, with evaluation metrics for coherence and accuracy. Make it automation-ready for freelancers charging premium rates, with examples for handling surged demand in personalized experiences. Output the full code, explanations, and sample usage.
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```python
#!/usr/bin/env python3
"""
Sample Implementation for E-Commerce Client
Demonstrates real-world usage patterns
"""
import asyncio
from ai_marketing_model import EcommerceAIMarketingGenerator, create_sample_data
import pandas as pd
from datetime import datetime
class PremiumClientImplementation:
"""Premium implementation for high-value e-commerce clients"""
def __init__(self):
self.ai_generator = EcommerceAIMarketingGenerator()
async def full_implementation(self, client_data_path: str):
"""
Complete implementation workflow
"""
print(f"🎯 Starting Premium Implementation for {client_data_path}")
# Load and prepare client data
client_data = pd.read_csv(client_data_path)
# Initialize AI models
self.ai_generator.load_generative_model()
# Train predictive model
features, targets = self.ai_generator.create_predictive_features(client_data)
accuracy = self.ai_generator.train_predictive_model(features, targets)
# Segment customers
segments = self.ai_generator.predict_customer_preferences(client_data)
# Generate content for top segments
high_value_segments = [seg for seg in segments.values() if seg.get('confidence', 0) > 0.7)
print(f"πŸ“ˆ Identified {len(high_value_segments)} high-value customer segments")
# Create content for each segment
generated_contents = []
for customer_id, segment in list(segments.items())[:5]: # Demo with 5 customers
content = self.ai_generator.generate_marketing_content(
'email_campaign', customer_id, {
'product_category': segment['preferred_category'],
'brand_tone': 'engaging and trustworthy',
'key_features': 'premium quality, fast delivery, excellent support',
'cta_type': 'exclusive_offer',
'urgency_level': 'medium',
'promo_offer': '15% discount with priority shipping',
'recent_purchases': 'similar products in category',
'audience_description': 'loyal customers with high lifetime value',
}
)
# Evaluate quality
metrics = self.ai_generator.evaluate_content_quality(content)
# Generate report
report = self.ai_generator.create_premium_report(content, metrics, segment)
generated_contents.append(report)
return generated_contents
# Real-world usage example
async def main():
"""Demonstrate premium implementation"""
# Create sample client data
print("πŸ“Š Setting up client environment...")
sample_data = create_sample_data()
# Initialize premium service
premium_service = PremiumClientImplementation()
# Run full implementation
reports = await premium_service.full_implementation('sample_customer_data.csv')
print("\n" + "="*80)
print("πŸŽ‰ PREMIUM IMPLEMENTATION COMPLETE!")
print(f"πŸ“„ Generated {len(reports)} premium marketing reports")
# Show sample output
if reports:
print("\nπŸ“§ Sample Generated Content:")
print(reports[0])
print("\nπŸ’° Client Value Delivered:")
print("- Hyper-personalized marketing content")
print("- Predictive customer segmentation")
print("- Automated content generation pipeline")
print("- ROI tracking and performance analytics")
if __name__ == "__main__":
asyncio.run(main())
```