Update app.py

app.py

@@ -81,40 +81,6 @@ def sentence_prob_mean(text):
     return mean_prob
 
 
-# def cloze_prob(text):
-
-# 	whole_text_encoding = tokenizer.encode(text)
-# 	text_list = text.split()
-# 	stem = ' '.join(text_list[:-1])
-# 	stem_encoding = tokenizer.encode(stem)
-# 	cw_encoding = whole_text_encoding[len(stem_encoding):]
-# 	tokens_tensor = torch.tensor([whole_text_encoding])
-	
-# 	with torch.no_grad():
-# 		outputs = model(tokens_tensor)
-# 		predictions = outputs[0]   
-
-# 	logprobs = []
-# 	start = -1-len(cw_encoding)
-# 	for j in range(start,-1,1):
-# 			raw_output = []
-# 			for i in predictions[-1][j]:
-# 					raw_output.append(i.item())
-	
-# 			logprobs.append(np.log(softmax(raw_output)))
-			
-	
-# 	conditional_probs = []
-# 	for cw,prob in zip(cw_encoding,logprobs):
-# 			conditional_probs.append(prob[cw])
-
-
-# 	return np.exp(np.sum(conditional_probs))
-
-
-
-
-
 def cos_sim(a, b):
     return np.inner(a, b) / (np.linalg.norm(a) * (np.linalg.norm(b)))
 
@@ -142,8 +108,7 @@ def Visual_re_ranker(caption_man, caption_woman, context_label, context_prob):
     LM_man =  sentence_prob_mean(caption_man)
     LM_woman = sentence_prob_mean(caption_woman)
 
-    #LM_man = cloze_prob(caption_man)
-    #LM_woman = cloze_prob(caption_woman)
+