Fix WebSocket CORS and TensorFlow event loop freeze

frontend/3d-visualizer/src/app/page.js

@@ -299,9 +299,6 @@ export default function NetworkVisualizer() {
                 <div className="p-6 flex-1 overflow-y-auto relative custom-scrollbar">
                     {activeTab.type === 'layer' && activeLayerPanelData && (
                         <div className="animate-fadeIn">
-                            <h2 className="text-xl md:text-2xl font-bold font-mono text-[#00ffcc] mb-4 border-b border-white/10 pb-2">
-                                Layer {activeLayerPanelData.layer_index}
-                            </h2>
                             <div className="mb-6">
                                 <p className="text-[10px] md:text-xs text-gray-500 uppercase tracking-wider mb-1">Tensor Shape</p>
                                 <p className="font-mono text-xs md:text-sm bg-black/50 px-3 py-2 rounded-lg border border-white/10 tracking-widest inline-block text-gray-200">

src/api/api.py

@@ -22,7 +22,7 @@ app = FastAPI()
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
-    allow_credentials=True,
+    allow_credentials=False,
     allow_methods=["*"],
     allow_headers=["*"],
 )
@@ -56,6 +56,7 @@ feature_extractor = tf.keras.Model(inputs=model.inputs, outputs=[layer.output fo
 CIFAR10_CLASSES = ['Airplane', 'Automobile', 'Bird', 'Cat', 'Deer', 
                    'Dog', 'Frog', 'Horse', 'Ship', 'Truck']
 
+# YOUR ORIGINAL PNG LOGIC RESTORED
 def generate_feature_grid(feature_map, max_features=64):
     if len(feature_map.shape) == 4:
         feature_map = feature_map[0]
@@ -85,7 +86,7 @@ def generate_feature_grid(feature_map, max_features=64):
     colored_grid = cv2.applyColorMap(grid_image, cv2.COLORMAP_VIRIDIS)
     
     b_channel, g_channel, r_channel = cv2.split(colored_grid)
-    alpha_channel = grid_image # Use the raw grayscale intensity as the alpha map
+    alpha_channel = grid_image 
     
     transparent_grid = cv2.merge((b_channel, g_channel, r_channel, alpha_channel))
     
@@ -102,19 +103,19 @@ async def predict_image(file: UploadFile = File(...)):
     img_normalized = img_resized.astype(np.float32) / 255.0
     img_batch = np.expand_dims(img_normalized, axis=0)
     
-    activations = feature_extractor.predict(img_batch)
-    
-    predictions = model.predict(img_batch)
-    class_idx = np.argmax(predictions[0])
+    # DIRECT CALLS FOR IMAGE AS WELL
+    activations = feature_extractor(img_batch, training=False)
+    predictions = model(img_batch, training=False)
+    class_idx = np.argmax(predictions[0].numpy())
     
     layer_data = []
     for i, activation in enumerate(activations):
-        b64_image = generate_feature_grid(activation)
+        b64_image = generate_feature_grid(activation.numpy())
         
         layer_data.append({
             "layer_index": i + 1,
             "shape": activation.shape[1:], 
-            "texture_b64": f"data:image/jpeg;base64,{b64_image}"
+            "texture_b64": f"data:image/png;base64,{b64_image}"
         })
         
     return {
@@ -142,13 +143,13 @@ async def predict_video_stream(websocket: WebSocket):
             img_normalized = img_resized.astype(np.float32) / 255.0
             img_batch = np.expand_dims(img_normalized, axis=0)
             
-            activations = feature_extractor.predict(img_batch, verbose=0)
-            predictions = model.predict(img_batch, verbose=0)
-            class_idx = np.argmax(predictions[0])
+            activations = feature_extractor(img_batch, training=False)
+            predictions = model(img_batch, training=False)
+            class_idx = np.argmax(predictions[0].numpy())
             
             layer_data = []
             for i, activation in enumerate(activations):
-                b64_image = generate_feature_grid(activation)
+                b64_image = generate_feature_grid(activation.numpy())
                 layer_data.append({
                     "layer_index": i + 1,
                     "shape": activation.shape[1:],