Update app.py

app.py

@@ -4,7 +4,6 @@ import shap
 import numpy as np
 import scipy as sp
 import torch
-import tensorflow as tf
 import transformers
 from transformers import pipeline
 from transformers import RobertaTokenizer, RobertaModel
@@ -51,8 +50,8 @@ ner_pipe = pipeline("ner", model=ner_model, tokenizer=ner_tokenizer, aggregation
 def adr_predict(x):
     encoded_input = tokenizer(x, return_tensors='pt')
     output = model(**encoded_input)
-    scores = output[0][0].detach().numpy()
-    scores = tf.nn.softmax(scores)
+    scores = output[0][0].detach()
+    scores = torch.nn.functional.softmax(scores)
    
     shap_values = explainer([str(x).lower()])
     # # Find the index of the class you want as the default reference (e.g., 'label_1')