Update app.py

app.py

@@ -44,16 +44,16 @@ def softmax(x):
 	
 # Load pre-trained model 
 
-model = GPT2LMHeadModel.from_pretrained('distilgpt2', output_hidden_states = True, output_attentions = True)
+# model = GPT2LMHeadModel.from_pretrained('distilgpt2', output_hidden_states = True, output_attentions = True)
 
-#model  =  gr.Interface.load('huggingface/distilgpt2', output_hidden_states = True, output_attentions = True)
+# #model  =  gr.Interface.load('huggingface/distilgpt2', output_hidden_states = True, output_attentions = True)
 
-#model.eval()
-#tokenizer =  gr.Interface.load('huggingface/distilgpt2')
+# #model.eval()
+# #tokenizer =  gr.Interface.load('huggingface/distilgpt2')
 
-#tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
-tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
-#tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
+# #tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
+# tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+# #tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
 
 tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
 model = GPT2LMHeadModel.from_pretrained('gpt2')
@@ -120,6 +120,7 @@ def sentence_prob_mean(text):
     # Compute the mean probability across the tokens
     mean_prob = torch.mean(gathered_probs).item()
 
+    return mean_prob