Update trjgru.py

trjgru.py

@@ -285,16 +285,25 @@ import h5py
 
 model = build_combined_model()  # Your original model building function
 # Rebuild the model architecture
-model = build_tgru_model(input_shape=(8, 95, 95, 2))
+# Step 1: Build the full combined model (with 6 inputs)
+# model = build_combined_model()
 
-# Build the model by calling it once (to initialize all weights)
-dummy_input = tf.random.normal((1, 8, 95, 95, 2))  # batch_size=1
-_ = model(dummy_input)  # Forward pass to build all layers
+# Step 2: Call the model once with dummy data to build the weights
+# import tensorflow as tf
 
-# Now load the saved weights
-# model.load_weights("Trj_GRU.weights.h5")
+dummy_input = [
+    tf.random.normal((1, 8, 95, 95, 2)),   # reduced_images_test
+    tf.random.normal((1, 95, 95, 8)),      # hov_m_test
+    tf.random.normal((1, 8, 8, 1)),        # test_vmax_3d
+    tf.random.normal((1, 8)),              # lat_test
+    tf.random.normal((1, 8)),              # lon_test
+    tf.random.normal((1, 9)),              # other_scalar_inputs
+]
+_ = model(dummy_input)  # Build model by doing one forward pass
+
+# Step 3: Load weights
+model.load_weights("Trj_GRU.weights.h5")  # Make sure this matches the architecture
 
-model.load_weights(r"Trj_GRU.weights.h5")
 
 
 def predict_trajgru(reduced_images_test,hov_m_test,test_vmax_3d,lat_test,lon_test,int_diff_test):