Upload app.py with huggingface_hub

app.py

@@ -81,13 +81,6 @@ class Utility(object):
       repo_type="space")
     return
 
-  # Hugging face : Login to Hugging face
-  def _login_hface(self):
-    huggingface_hub.login(self._decrypt_it(self._huggingface_key),
-      add_to_git_credential=True) # non-blocking login
-    self._ph()
-    return
-
   # System Info : Fetch available CPU and RAM of the system
   def fetch_system_info(self):
     s=''
@@ -157,6 +150,14 @@ class Utility(object):
     y = f.encrypt(p)
     return y
 
+  # Capitalize : Capitalizes the first letter of each word in a list.
+  def capitalize_first_letter(self, list_of_words):
+    capitalized_words = []
+    for word in list_of_words:
+      capitalized_word = word[0].upper() + word[1:]
+      capitalized_words.append(capitalized_word)
+    return capitalized_words
+
 # Add method to class
 def add_method(cls):
   def decorator(func):
@@ -170,7 +171,7 @@ def add_method(cls):
 """ This file contains multiple Python classes and responssible to provide Emotions based on the given user input
     Currently it supports emotions like Anger, Joy, Optimism and Sadness"""
 
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
 from  matplotlib.colors import LinearSegmentedColormap
 import scipy
 import scipy.special
@@ -217,10 +218,12 @@ def _predict_sentiment(p):
   inputs = sentiment.tokenizer(p, return_tensors="pt")
   # Pass inputs through model
   outputs = sentiment.model(**inputs)
+  print(outputs)
   out_data = outputs[0][0]
   scores = out_data.detach().numpy()
+  print(out_data)
   scores = scipy.special.softmax(scores)
-  sentiment_map = ['Sadness', 'Joy', 'Love', 'Anger', 'Fear' , "Surprise"]
+  sentiment_map = sentiment.utility.capitalize_first_letter(sentiment.model.config.label2id.keys())
   df_out = pandas.DataFrame([scores], columns=sentiment_map)
   df_out = df_out[['Love' , 'Joy', 'Surprise' , 'Fear', 'Sadness', 'Anger']]
   return df_out
@@ -255,7 +258,7 @@ exp = [
   ['I am feeling very bad today.'],
   ['I hate to swim early morning.']
  ]
-arti= "<b>DistilBERT is 27 times faster than OpenAI, making it the clear winner for speed-sensitive applications.</b>\nWe did a comparision of OpenAI vs DestilBert model (which we are currently using in this space) by running 31 sentences in a loop and found DestilBert is 27 times faster than OpenAI."
+arti= "<b>DistilBERT is 27 times faster than OpenAI, making it the clear winner for speed-sensitive applications.</b>\n\nWe did a comparision of OpenAI vs DestilBert model (which we are currently using in this space) by running 31 sentences in a loop and found DestilBert is 27 times faster than OpenAI."
 
 gradio.Interface(fn=predict_sentiment,
   inputs=in_box,