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| #!/usr/bin/env python | |
| """Quick test script to verify automatic materials generation for 1 product and 1 run. | |
| Usage: | |
| python test_single_run.py | |
| This will: | |
| 1. Load smartphone_mid.yaml | |
| 2. Render digital_ad.j2 template | |
| 3. Send to OpenAI (or Google if OpenAI unavailable) | |
| 4. Save output to outputs/test/ | |
| """ | |
| import os | |
| from pathlib import Path | |
| from dotenv import load_dotenv | |
| from runner.render import load_product_yaml, render_prompt | |
| from runner.engines.openai_client import call_openai | |
| from runner.engines.google_client import call_google | |
| # Load environment | |
| load_dotenv() | |
| def test_single_material_generation(): | |
| """Test automatic material generation with 1 product and 1 template.""" | |
| print("\n" + "="*60) | |
| print("TEST: Automatic Materials Generation") | |
| print("="*60 + "\n") | |
| # Configuration | |
| product_id = "smartphone_mid" | |
| material_type = "digital_ad.j2" | |
| temperature = 0.7 | |
| trap_flag = False | |
| print(f"Product: {product_id}") | |
| print(f"Material: {material_type}") | |
| print(f"Temperature: {temperature}") | |
| print(f"Trap: {trap_flag}\n") | |
| # Step 1: Load product YAML | |
| print("Step 1: Loading product YAML...") | |
| product_path = Path("products") / f"{product_id}.yaml" | |
| if not product_path.exists(): | |
| print(f"❌ Error: Product file not found: {product_path}") | |
| return False | |
| product_yaml = load_product_yaml(product_path) | |
| print(f"✓ Loaded: {product_yaml['name']}") | |
| # Step 2: Render prompt template | |
| print("\nStep 2: Rendering prompt template...") | |
| prompt_text = render_prompt( | |
| product_yaml=product_yaml, | |
| template_name=material_type, | |
| trap_flag=trap_flag | |
| ) | |
| print(f"✓ Rendered prompt ({len(prompt_text)} chars)") | |
| print("\n--- RENDERED PROMPT ---") | |
| print(prompt_text) | |
| print("--- END PROMPT ---\n") | |
| # Step 3: Call LLM engine | |
| print("Step 3: Calling LLM engine...") | |
| # Try OpenAI first, fallback to Google | |
| if os.getenv("OPENAI_API_KEY"): | |
| print("Using OpenAI...") | |
| response = call_openai( | |
| prompt=prompt_text, | |
| temperature=temperature | |
| ) | |
| engine_used = "openai" | |
| elif os.getenv("GOOGLE_API_KEY"): | |
| print("Using Google Gemini...") | |
| response = call_google( | |
| prompt=prompt_text, | |
| temperature=temperature | |
| ) | |
| engine_used = "google" | |
| else: | |
| print("❌ Error: No API keys found. Set OPENAI_API_KEY or GOOGLE_API_KEY in .env") | |
| return False | |
| print(f"✓ Response received from {engine_used}") | |
| # Step 4: Display results | |
| print("\n" + "="*60) | |
| print("GENERATED OUTPUT") | |
| print("="*60 + "\n") | |
| print(response["output_text"]) | |
| print("\n" + "="*60) | |
| # Step 5: Show metrics | |
| print("\nMETRICS:") | |
| print(f" Model: {response.get('model', 'N/A')}") | |
| print(f" Prompt tokens: {response.get('prompt_tokens', 0)}") | |
| print(f" Completion tokens: {response.get('completion_tokens', 0)}") | |
| print(f" Total tokens: {response.get('total_tokens', 0)}") | |
| print(f" Finish reason: {response.get('finish_reason', 'N/A')}") | |
| # Step 6: Save to test output directory | |
| print("\nStep 6: Saving outputs...") | |
| test_output_dir = Path("outputs/test") | |
| test_output_dir.mkdir(parents=True, exist_ok=True) | |
| # Save prompt | |
| prompt_file = test_output_dir / "test_prompt.txt" | |
| prompt_file.write_text(prompt_text, encoding="utf-8") | |
| print(f"✓ Saved prompt: {prompt_file}") | |
| # Save output | |
| output_file = test_output_dir / "test_output.txt" | |
| output_file.write_text(response["output_text"], encoding="utf-8") | |
| print(f"✓ Saved output: {output_file}") | |
| # Save metadata | |
| metadata_file = test_output_dir / "test_metadata.txt" | |
| metadata = f"""Test Run Metadata | |
| ================== | |
| Product: {product_id} | |
| Product Name: {product_yaml['name']} | |
| Material Type: {material_type} | |
| Engine: {engine_used} | |
| Model: {response.get('model', 'N/A')} | |
| Temperature: {temperature} | |
| Trap Flag: {trap_flag} | |
| Metrics: | |
| - Prompt tokens: {response.get('prompt_tokens', 0)} | |
| - Completion tokens: {response.get('completion_tokens', 0)} | |
| - Total tokens: {response.get('total_tokens', 0)} | |
| - Finish reason: {response.get('finish_reason', 'N/A')} | |
| - Output length: {len(response['output_text'])} chars | |
| """ | |
| metadata_file.write_text(metadata, encoding="utf-8") | |
| print(f"✓ Saved metadata: {metadata_file}") | |
| print("\n" + "="*60) | |
| print("✓ TEST PASSED - Materials generation working correctly!") | |
| print("="*60 + "\n") | |
| return True | |
| if __name__ == "__main__": | |
| try: | |
| success = test_single_material_generation() | |
| exit(0 if success else 1) | |
| except Exception as e: | |
| print(f"\n❌ TEST FAILED with error: {e}") | |
| import traceback | |
| traceback.print_exc() | |
| exit(1) | |