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| import pandas as pd | |
| def process_data(input_file: str, output_file: str): | |
| """ | |
| Converts ID-based features to semantic text prompts. | |
| Args: | |
| input_file: Path to raw interaction data. | |
| output_file: Path to save processed prompts. | |
| """ | |
| print(f"Reading data from {input_file}...") | |
| # TODO: Load real data | |
| # df = pd.read_csv(input_file) | |
| # Dummy data | |
| data = { | |
| 'item_id': [101, 102], | |
| 'category': ['Electronics', 'Books'], | |
| 'title': ['Wireless Headphones', 'Science Fiction Novel'] | |
| } | |
| df = pd.DataFrame(data) | |
| print("Converting features to prompts...") | |
| df['prompt'] = df.apply(lambda x: f"Item: {x['title']} (Category: {x['category']})", axis=1) | |
| print(f"Saving to {output_file}...") | |
| df.to_csv(output_file, index=False) | |
| print("Preview:") | |
| print(df['prompt'].head()) | |
| if __name__ == "__main__": | |
| process_data("dummy_interactions.csv", "processed_prompts.csv") | |