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"π― 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"π 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("π 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("π PREMIUM IMPLEMENTATION COMPLETE!") | |
| print(f"π Generated {len(reports)} premium marketing reports") | |
| # Show sample output | |
| if reports: | |
| print("\nπ§ Sample Generated Content:") | |
| print(reports[0]) | |
| print("\nπ° 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()) | |
| ``` |