Update app.py with transformer embeddings and prediction pipeline

app.py

@@ -80,7 +80,8 @@ def predict_with_gpflow(model, X):
     return mean.numpy().flatten(), variance.numpy().flatten()
 
 
-def process_target(target):
+
+def process_target(target, selected_models, sequence, prediction_type):
     """
     Process a single target for prediction using transformer embeddings and the specified model.
     """
@@ -101,6 +102,7 @@ def process_target(target):
         y_pred, y_uncertainty = predict_with_gpflow(model, embedding)
         return target, round(y_pred[0], 2), round(y_uncertainty[0], 2)
 
+
 def predict(sequence, prediction_type):
     """
     Predicts Specificity, kcatC, and KC for the given sequence and prediction type.
@@ -110,7 +112,12 @@ def predict(sequence, prediction_type):
 
     # Predict for all targets in parallel
     with ThreadPoolExecutor() as executor:
-        results = list(executor.map(process_target, selected_models.keys()))
+        results = list(
+            executor.map(
+                lambda target: process_target(target, selected_models, sequence, prediction_type),
+                selected_models.keys()
+            )
+        )
 
     # Format results
     if prediction_type == "Plant-Specific":