Update app.py with transformer embeddings and prediction pipeline

app.py

@@ -97,7 +97,8 @@ def process_target(target, selected_models, sequence, prediction_type):
 
     # Generate embeddings in the required format
     embedding, _ = get_embedding(sequence, esm_model_name, layer)
-
+    embedding = embedding.astype(np.float64) 
+    np.save(f"hf_embedding_{target}.npy", embedding)
     if prediction_type == "Plant-Specific":
         # Random Forest prediction
         y_pred = model.predict(embedding)[0]