Update UnifiedPredictor to handle feature dimensions consistently

models/analysis/multimodal_integrator.py

@@ -142,10 +142,11 @@ def __init__(self, hidden_size: int):
         )
 
         self.integration_network = nn.Sequential(
-            nn.Linear(2304, 1536),  # 3 * 768-dim features
+            nn.Linear(768 * 3, 1536),  # Concatenated features from all three modalities
+            nn.LayerNorm(1536),        # Normalize combined features
             nn.ReLU(),
             nn.Dropout(0.1),
-            nn.Linear(1536, 768)  # Match ESM2 dimensions
+            nn.Linear(1536, 768)       # Final output dimension
         )
 
         self.confidence_estimator = nn.Sequential(
@@ -164,6 +165,13 @@ def forward(
         # Transform function features to match dimensions
         function_features = self.function_encoder(function_results['go_terms'])
 
+        # Ensure all features have the same dimensions before combining
+        batch_size = sequence_features.size(0)
+        seq_len = sequence_features.size(1)
+
+        # Reshape function features if needed
+        function_features = function_features.view(batch_size, seq_len, -1)
+
         # Combine all features
         combined_features = torch.cat([
             sequence_features,