Update app.py

app.py

@@ -1,3 +1,54 @@
 import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
 
-gr.Interface.load("huggingface/allenai/longformer-scico").launch()
+tokenizer = AutoTokenizer.from_pretrained('allenai/longformer-scico')
+model = AutoModelForSequenceClassification.from_pretrained('allenai/longformer-scico')
+
+start_token = tokenizer.convert_tokens_to_ids("<m>")
+end_token = tokenizer.convert_tokens_to_ids("</m>")
+
+def get_global_attention(input_ids):
+    global_attention_mask = torch.zeros(input_ids.shape)
+    global_attention_mask[:, 0] = 1  # global attention to the CLS token
+    start = torch.nonzero(input_ids == start_token) # global attention to the <m> token
+    end = torch.nonzero(input_ids == end_token) # global attention to the </m> token
+    globs = torch.cat((start, end))
+    value = torch.ones(globs.shape[0])
+    global_attention_mask.index_put_(tuple(globs.t()), value)
+    return global_attention_mask
+  
+def inference(m1,m2):
+  b = {}
+  m1 = m1
+  m2 = m2
+  
+  inputs = m1 + " </s></s> " + m2  
+  
+  tokens = tokenizer(inputs, return_tensors='pt')
+  global_attention_mask = get_global_attention(tokens['input_ids'])
+  
+  with torch.no_grad():
+      output = model(tokens['input_ids'], tokens['attention_mask'], global_attention_mask)
+      
+  scores = torch.softmax(output.logits, dim=-1)
+  listscore = scores.tolist()
+  b['not related'] = listscore[0]
+  b['coref'] = listscore[1]
+  b['parent'] = listscore[2]
+  b['child'] = listscore[3]
+  return b
+
+title = "Longformer-scico"
+description = "demo for Anime2Sketch. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2104.05703'>Adversarial Open Domain Adaption for Sketch-to-Photo Synthesis</a> | <a href='https://github.com/Mukosame/Anime2Sketch'>Github Repo</a></p>"
+
+gr.Interface(
+    inference, 
+    gr.inputs.Textbox(label="Input")], 
+    gr.outputs.Label(label="Output"),
+    title=title,
+    description=description,
+    article=article,
+    enable_queue=True
+    ).launch(debug=True)