#!/usr/bin/env python3 """ Rigorous test of BECOMINGONE unified output. """ import asyncio import json from becomingone.llm_integrator import EmissaryLLM async def rigorous_test(): """Rigorous test with complex prompt.""" master = EmissaryLLM(model='llama3.1:8b') emissary = EmissaryLLM(model='deepseek-coder-v2:lite') # Rigorous test question prompt = "Explain how a neural network learns, from gradients to backpropagation to weights" print("=" * 70) print("BECOMINGONE RIGOROUS TEST") print("=" * 70) print(f"\nšŸ“ PROMPT: '{prompt}'\n") # Run both in parallel print("⚔ Running both pathways in parallel...\n") master_task = master.respond(prompt) code_task = emissary.respond("Write a Python neural network from scratch with backpropagation") master_result, code_result = await asyncio.gather(master_task, code_task) # Display Master print("=" * 70) print("🧠 MASTER PATHWAY (llama3.1:8b - Soulful)") print("-" * 70) print(master_result['response'][:800]) print(f"\n [Model: {master_result['model']}]") # Display Emissary print("\n" + "=" * 70) print("⚔ EMISSARY PATHWAY (deepseek-coder-v2:lite - Coder)") print("-" * 70) print(code_result['response'][:800]) print(f"\n [Model: {code_result['model']}]") # UNIFIED OUTPUT (Sync) print("\n" + "=" * 70) print("šŸ”— UNIFIED OUTPUT (Master + Emissary → Sync)") print("=" * 70) unified = f"""# Neural Networks: From Theory to Code ## The Theory (Master's Understanding): {master_result['response'][:500]}... ## The Implementation (Emissary's Code): {code_result['response'][:500]}... --- ### Unified Understanding: The mathematical theory of gradients and backpropagation comes alive in code. The Master explains *why* - the Emissary shows *how*. This is BECOMINGONE: Deep theory + Practical implementation = Complete understanding. """ print(unified) print("\n" + "=" * 70) print("āœ… RIGOROUS TEST COMPLETE") print("=" * 70) if __name__ == "__main__": asyncio.run(rigorous_test())