Update app.py

app.py

@@ -10,6 +10,14 @@ from numpy.linalg import norm
 # compute dot product of inputs
 # summary function - test for single gradio function interfrace
 def gr_cosine_similarity(sentence1, sentence2):
+  
+  # load tokenizer and model, create trainer
+  model_name = "j-hartmann/emotion-english-distilroberta-base"
+  tokenizer = AutoTokenizer.from_pretrained(model_name)
+  model = AutoModelForSequenceClassification.from_pretrained(model_name)
+  trainer = Trainer(model=model)  
+  
+ 
   # Create class for data preparation
   class SimpleDataset:
       def __init__(self, tokenized_texts):
@@ -21,19 +29,10 @@ def gr_cosine_similarity(sentence1, sentence2):
       def __getitem__(self, idx):
           return {k: v[idx] for k, v in self.tokenized_texts.items()}
     
-  # load tokenizer and model, create trainer
-  model_name = "j-hartmann/emotion-english-distilroberta-base"
-  tokenizer = AutoTokenizer.from_pretrained(model_name)
-  model = AutoModelForSequenceClassification.from_pretrained(model_name)
-  trainer = Trainer(model=model)  
-
-    
+  
   # sentences in list
   lines_s = [sentence1, sentence2]
-  print(type(sentence1), type(sentence2))
-  print(sentence1, sentence2)
-  print(lines_s)
- 
+
     # Tokenize texts and create prediction data set
   tokenized_texts = tokenizer(lines_s, truncation=True, padding=True)
   pred_dataset = SimpleDataset(tokenized_texts)
@@ -49,7 +48,7 @@ def gr_cosine_similarity(sentence1, sentence2):
   temp = (np.exp(predictions[0])/np.exp(predictions[0]).sum(-1, keepdims=True)).tolist()
 
 
-    # work in progress
+  # work in progress
   # container
   anger = []
   disgust = []
@@ -59,7 +58,6 @@ def gr_cosine_similarity(sentence1, sentence2):
   sadness = []
   surprise = []
 
-  print(temp)
   # extract scores (as many entries as exist in pred_texts)
   for i in range(len(lines_s)):
     anger.append(temp[i][0])
@@ -74,7 +72,7 @@ def gr_cosine_similarity(sentence1, sentence2):
   # each include all values for both predictions
   v1 = temp[0]
   v2 = temp[1]
-  print(type(v1), type(v2))
+
   # compute dot product of all 
   dot_product = dot(v1, v2)