Update app.py

app.py

@@ -18,9 +18,9 @@ from sentence_transformers import SentenceTransformer, util
 #from sklearn.metrics.pairwise import cosine_similarity
 
 #device = "cuda:0" if torch.cuda.is_available() else "cpu"
-model_sts = gr.Interface.load('huggingface/sentence-transformers/stsb-distilbert-base') 
+#model_sts = gr.Interface.load('huggingface/sentence-transformers/stsb-distilbert-base') 
 
-#SentenceTransformer('stsb-distilbert-base', device=device)
+model_sts = SentenceTransformer('stsb-distilbert-base')
 
 #batch_size = 1
 #scorer = LMScorer.from_pretrained('gpt2' , device=device, batch_size=batch_size)
@@ -43,14 +43,14 @@ 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')
+#tokenizer =  gr.Interface.load('huggingface/distilgpt2')
 
-#tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
+tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
 #tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
 
 
@@ -130,8 +130,8 @@ def Visual_re_ranker(caption, visual_context_label, visual_context_prob):
 demo = gr.Interface(
     fn=Visual_re_ranker,
     description="Demo for Belief Revision based Caption Re-ranker with Visual Semantic Information",
-    #inputs=[gr.Textbox(value="a city street filled with traffic at night") , gr.Textbox(value="traffic"),  gr.Textbox(value="0.7458009")],
-    outputs=[gr.Textbox(value="Language Model Score") , gr.Textbox(value="Semantic Similarity Score"),  gr.Textbox(value="Belief revision score via visual context")],
-    #outputs="label",
+    inputs=[gr.Textbox(value="a city street filled with traffic at night") , gr.Textbox(value="traffic"),  gr.Textbox(value="0.7458009")],
+    #outputs=[gr.Textbox(value="Language Model Score") , gr.Textbox(value="Semantic Similarity Score"),  gr.Textbox(value="Belief revision score via visual context")],
+    outputs="label",
 )
 demo.launch()