Update app.py

app.py

@@ -18,8 +18,8 @@ import requests
 #from sentence_transformers import SentenceTransformer, util
 #from sklearn.metrics.pairwise import cosine_similarity
 
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-model = gr.interface.huggingface.load('sentence-transformers/stsb-distilbert-base') 
+#device = "cuda:0" if torch.cuda.is_available() else "cpu"
+model_1 = gr.interface.huggingface.load('sentence-transformers/stsb-distilbert-base') 
 
 #SentenceTransformer('stsb-distilbert-base', device=device)
 
@@ -43,9 +43,16 @@ def softmax(x):
 	return np.divide(exps, np.sum(exps))
 	
 # 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.huggingface.load('distilgpt2', output_hidden_states = True, output_attentions = True)
+
 model.eval()
-tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
+tokenizer = gr.interface.huggingface.load('distilgpt2')
+
+#tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
+#tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
 
 
 def cloze_prob(text):
@@ -102,7 +109,7 @@ def Visual_re_ranker(caption, visual_context_label, visual_context_prob):
     visual_context_label= visual_context_label
     visual_context_prob = visual_context_prob
     caption_emb = model.encode(caption, convert_to_tensor=True)
-    visual_context_label_emb = model.encode(visual_context_label, convert_to_tensor=True)
+    visual_context_label_emb = model_1.encode(visual_context_label, convert_to_tensor=True)
 
 
     sim =  cosine_scores = util.pytorch_cos_sim(caption_emb, visual_context_label_emb)