Update app.py

app.py

@@ -3,7 +3,7 @@ import pandas as pd
 from transformers import pipeline
 import torch
 from transformers import AutoModel, AutoTokenizer
-from torch.nn import Softmax
+import torch.nn.functional as F
 import torch
 import torch.nn as nn
 from huggingface_hub import PyTorchModelHubMixin
@@ -16,7 +16,7 @@ class MyModel(
     nn.Module,
     PyTorchModelHubMixin,
     # optionally, you can add metadata which gets pushed to the model card
-    repo_url="your-repo-url",
+    # repo_url="your-repo-url",
     pipeline_tag="text-classification",
     license="mit",
 ):
@@ -66,9 +66,6 @@ def preprocessing(input_text, tokenizer):
 def convert_excel_to_csv(file):
     return pd.read_excel(file)
 
-# initialising the Softmax function
-soft = Softmax()
-
 # Function to load models from Hugging Face Hub
 @st.cache_resource
 def get_model_score(sentence, mft):
@@ -83,7 +80,7 @@ def get_model_score(sentence, mft):
 
     # predicting the mft score
     output = model(**encodeds)
-    score = soft(output)
+    score = F.softmax(output, dim=1)
 
     # extracting and return the second value from the tensor
     #mft_value = score[0, 1].item()