Update test_ai_integration.py

test_ai_integration.py

@@ -290,186 +290,4 @@ def test_ai_integration():
 
     print("\n--- All AI Integration Tests Completed ---")
 
-from fastapi import FastAPI, UploadFile, File
-from fastapi.responses import JSONResponse
-import uvicorn
-import io
-
-# Initialize FastAPI app
-app = FastAPI()
-
-# Store initialized components
-gpu_components = None
-
-@app.on_event("startup")
-async def startup_event():
-    """Initialize GPU components on server startup"""
-    global gpu_components
-    gpu_components = test_ai_integration()
-
-@app.post("/process_image")
-async def process_image(image: UploadFile = File(...)):
-    """Process an image using the initialized GPU components"""
-    try:
-        # Read the image
-        contents = await image.read()
-        img = Image.open(io.BytesIO(contents)).convert('RGB')
-        
-        # Process using existing components
-        with websocket_manager() as storage:
-            # Convert image to numpy array
-            image_array = np.array(img)
-            
-            # Store in WebSocket storage
-            storage.store_tensor("input_image", image_array)
-            
-            # Process using first AI accelerator
-            result = gpu_components['ai_accelerators'][0].inference(
-                gpu_components['model_id'], 
-                "input_image"
-            )
-            
-            return JSONResponse({
-                "result": result.tolist() if isinstance(result, np.ndarray) else result,
-                "status": "success"
-            })
-            
-    except Exception as e:
-        return JSONResponse({
-            "error": str(e),
-            "status": "error"
-        }, status_code=500)
-
-@app.get("/status")
-async def get_status():
-    """Get the status of the GPU components"""
-    if not gpu_components:
-        return {"status": "not_initialized"}
-    
-    return {
-        "status": "running",
-        "num_chips": len(gpu_components['chips']),
-        "num_accelerators": len(gpu_components['ai_accelerators']),
-        "model_id": gpu_components['model_id']
-    }
-
-def test_ai_integration():
-    """Original test function modified to return components"""
-    print("\n--- Testing WebSocket-Based AI Integration with Zero CPU Usage ---")
-    from electron_speed import TARGET_SWITCHES_PER_SEC, TRANSISTORS_ON_CHIP, drift_velocity, speed_of_light_silicon
-    
-    components = {
-        'chips': [],
-        'ai_accelerators': [],
-        'model_id': None
-    }
-    
-    # Rest of your original test_ai_integration code here...
-    # Store important components in the components dict
-    # Replace print statements with logging if needed
-    
-    return components
-
-def create_app():
-    """Create and configure the FastAPI application"""
-    from fastapi import FastAPI, File, UploadFile
-    from fastapi.responses import JSONResponse
-    import io
-    from PIL import Image
-    
-    app = FastAPI(
-        title="AI Integration Test API",
-        description="WebSocket-based GPU storage with zero CPU memory usage",
-        version="1.0.0"
-    )
-
-    @app.on_event("startup")
-    async def startup_event():
-        """Initialize GPU components on server startup"""
-        global gpu_components
-        gpu_components = test_ai_integration()
-
-    @app.get("/")
-    async def root():
-        """Root endpoint showing system status"""
-        from electron_speed import drift_velocity
-        return {
-            "status": "running",
-            "speed": f"{drift_velocity:.2e} m/s",
-            "flip_flop_delay": "1.02e-15 s",
-            "max_frequency": "9.80e+14 Hz"
-        }
-        
-    @app.post("/process")
-    async def process_image(image: UploadFile = File(...)):
-        """Process an image using WebSocket-based GPU acceleration"""
-        try:
-            contents = await image.read()
-            img = Image.open(io.BytesIO(contents)).convert('RGB')
-            
-            # Process using initialized GPU components
-            with websocket_manager() as storage:
-                # Convert and store image
-                image_array = np.array(img)
-                storage.store_tensor("input_image", image_array)
-                
-                # Run inference
-                if gpu_components and gpu_components.get('ai_accelerators'):
-                    accelerator = gpu_components['ai_accelerators'][0]
-                    result = accelerator.inference(
-                        gpu_components['model_id'],
-                        "input_image"
-                    )
-                    
-                    return JSONResponse({
-                        "status": "success",
-                        "result": result.tolist() if isinstance(result, np.ndarray) else result,
-                        "processing_stats": {
-                            "flip_flop_delay": "1.02e-15 s",
-                            "max_frequency": "9.80e+14 Hz"
-                        }
-                    })
-                else:
-                    raise ValueError("GPU components not initialized")
-                    
-        except Exception as e:
-            return JSONResponse(
-                status_code=500,
-                content={
-                    "status": "error",
-                    "message": str(e)
-                }
-            )
-
-    @app.get("/status")
-    async def get_status():
-        if not gpu_components:
-            return {"status": "not_initialized"}
-        return {
-            "status": "running",
-            "flip_flop_delay": "1.02e-15 s",
-            "max_frequency": "9.80e+14 Hz",
-            "model_loaded": gpu_components.get('model_id') is not None
-        }
-
-    return app
-
-if __name__ == "__main__":
-    import logging
-    import uvicorn
-    from fastapi import FastAPI
-    
-    logging.basicConfig(level=logging.INFO)
-    logger = logging.getLogger(__name__)
-    
-    # Create the FastAPI app
-    app = create_app()
-    
-    # Run as FastAPI server
-    logger.info("Starting AI Integration Test Server...")
-    uvicorn.run("test_ai_integration:create_app()", 
-                host="0.0.0.0", 
-                port=8000, 
-                factory=True,
-                reload=True)