Update README.md

README.md

@@ -63,8 +63,35 @@ This UNet-based segmentation model is designed for **automated segmentation of C
 ### **1️⃣ Load the Model**
 #### **TensorFlow/Keras**
 ```python
+import os
 from huggingface_hub import hf_hub_download
 from tensorflow.keras.models import load_model
+from keras.saving import register_keras_serializable
+import tensorflow.keras.backend as K
 
+# ✅ Set Keras backend (optional)
+os.environ["KERAS_BACKEND"] = "jax"
+
+# ✅ Register and define missing functions
+@register_keras_serializable()
+def dice_coef(y_true, y_pred, smooth=1e-6):
+    y_true_f = K.flatten(y_true)
+    y_pred_f = K.flatten(y_pred)
+    intersection = K.sum(y_true_f * y_pred_f)
+    return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)
+
+@register_keras_serializable()
+def gl_sl(*args, **kwargs):
+    pass  # Placeholder function (update if needed)
+
+# ✅ Download the model from Hugging Face
 model_path = hf_hub_download(repo_id="amal90888/unet-segmentation-model", filename="unet_model.keras")
-unet = load_model(model_path)
+
+# ✅ Load the model with registered custom objects
+unet = load_model(model_path, custom_objects={"dice_coef": dice_coef, "gl_sl": gl_sl}, compile=False)
+
+# ✅ Recompile with fresh optimizer and correct loss function
+from tensorflow.keras.optimizers import Adam
+unet.compile(optimizer=Adam(learning_rate=1e-4), loss="binary_crossentropy", metrics=["accuracy", dice_coef])
+
+print("✅ Model loaded and recompiled successfully!")