Update app.py with transformer embeddings and prediction pipeline

app.py

@@ -62,12 +62,13 @@ def get_embedding(sequence, esm_model_name, layer):
 
     # Convert to DataFrame with named columns
     feature_columns = {f"D{i+1}": embedding[0, i] for i in range(embedding.shape[1])}
-    embedding_df = pd.DataFrame([feature_columns])
-    
+    embedding_df = pd.DataFrame([feature_columns]) 
+    print (embedding_df)
     return embedding_df.values, embedding_df
 
 
 def predict_with_gpflow(model, X):
+    print(model.signatures)
     # Convert input to TensorFlow tensor
     X_tensor = tf.convert_to_tensor(X, dtype=tf.float64)