Update test_ai_integration.py

test_ai_integration.py

@@ -370,12 +370,106 @@ def test_ai_integration():
     
     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(app, host="0.0.0.0", port=8000)
+    uvicorn.run("test_ai_integration:create_app()", 
+                host="0.0.0.0", 
+                port=8000, 
+                factory=True,
+                reload=True)