| |
| """ |
| Lightweight BeastBullet + TinyLlama Demo |
| Works without torch - demonstrates integration concept |
| """ |
|
|
| import subprocess |
| import json |
| import os |
| from pathlib import Path |
|
|
| class SimpleBeastBulletDemo: |
| """Simplified demo showing BeastBullet + TinyLlama integration""" |
| |
| def __init__(self, experts_dir="experts/"): |
| self.experts_dir = Path(experts_dir) |
| self.available_experts = self._discover_experts() |
| |
| def _discover_experts(self): |
| """Discover available expert models""" |
| expert_files = list(self.experts_dir.glob("*_expert_v1.0.pt")) |
| experts = {} |
| for f in expert_files: |
| name = f.stem.replace("_v1.0", "") |
| experts[name] = { |
| "path": str(f), |
| "size_mb": f.stat().st_size / (1024 * 1024) |
| } |
| return experts |
| |
| def route_query(self, query): |
| """Simple routing based on query keywords""" |
| query_lower = query.lower() |
| selected = [] |
| |
| |
| if any(w in query_lower for w in ["calculate", "math", "%", "percent", "+"]): |
| selected.append("math_expert") |
| |
| |
| if any(w in query_lower for w in ["if", "then", "therefore", "logic"]): |
| selected.append("logic_expert") |
| |
| |
| if any(w in query_lower for w in ["code", "function", "python", "javascript"]): |
| selected.append("code_generation_expert") |
| |
| |
| if not selected: |
| selected.append("question_answering_expert") |
| |
| |
| selected.append("validator_expert") |
| |
| return selected |
| |
| def query_tinyllama(self, prompt): |
| """Query TinyLlama via Ollama CLI""" |
| try: |
| result = subprocess.run( |
| ["ollama", "run", "tinyllama", prompt], |
| capture_output=True, |
| text=True, |
| timeout=30 |
| ) |
| return result.stdout.strip() |
| except Exception as e: |
| return f"Error querying TinyLlama: {e}" |
| |
| def process_query(self, user_query): |
| """Process query through BeastBullet + TinyLlama pipeline""" |
| |
| print("=" * 70) |
| print(f"Query: {user_query}") |
| print("=" * 70) |
| |
| |
| selected_experts = self.route_query(user_query) |
| print(f"\n🔀 Selected experts: {', '.join(selected_experts)}") |
| |
| |
| print(f"\n📊 Expert status:") |
| for expert in selected_experts: |
| if expert in self.available_experts: |
| info = self.available_experts[expert] |
| print(f" ✅ {expert}: {info['size_mb']:.1f} MB") |
| else: |
| print(f" ⚠️ {expert}: Not found") |
| |
| |
| prompt = f"""You are a helpful AI assistant integrated with BeastBullet expert system. |
| |
| Question: {user_query} |
| |
| Expert Analysis: |
| """ |
| for expert in selected_experts: |
| if expert in self.available_experts: |
| prompt += f"\n- {expert}: Available for analysis" |
| |
| prompt += "\n\nProvide a clear, accurate answer:" |
| |
| |
| print(f"\n🤖 Querying TinyLlama via Ollama...") |
| answer = self.query_tinyllama(prompt) |
| |
| |
| print(f"\n💡 Answer:") |
| print(f" {answer}") |
| print("\n" + "=" * 70) |
| |
| return { |
| "query": user_query, |
| "answer": answer, |
| "experts_selected": selected_experts, |
| "experts_available": len(self.available_experts) |
| } |
|
|
|
|
| def main(): |
| print("=" * 70) |
| print("🧠 BeastBullet + TinyLlama Integration Demo") |
| print("=" * 70) |
| |
| |
| demo = SimpleBeastBulletDemo("experts/") |
| |
| print(f"\n📦 Discovered {len(demo.available_experts)} expert models:") |
| for name, info in sorted(demo.available_experts.items()): |
| print(f" • {name}: {info['size_mb']:.1f} MB") |
| |
| print(f"\n✅ TinyLlama available in Ollama") |
| |
| |
| print("\n" + "=" * 70) |
| print("Running Test Queries") |
| print("=" * 70) |
| |
| test_queries = [ |
| "What is 15% of 240?", |
| "If all cats are mammals, and Fluffy is a cat, what is Fluffy?", |
| ] |
| |
| for query in test_queries: |
| print() |
| demo.process_query(query) |
| |
| print("\n" + "=" * 70) |
| print("✅ Demo Complete!") |
| print("=" * 70) |
| print("\nNote: This is a lightweight demo. For full functionality,") |
| print("install torch to load the actual expert models:") |
| print(" pip install torch") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
