Update app.py

app.py

@@ -1,7 +1,31 @@
 import gradio as gr
+import torch
+import transformers
+from transformers import BertTokenizer, BertForMaskedLM
 
-def greet(name):
-    return "Hello " + name + "!"
+device = torch.device('cpu')
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()  
+NUM_CLASSES=5
+
+model=BertForMaskedLM.from_pretrained("./")
+tokenizer=BertTokenizer.from_pretrained("./")
+
+
+def predict(text=None) -> dict:  
+    model.eval()
+    inputs = tokenizer(text, return_tensors="pt")
+    input_ids = inputs["input_ids"].to(device)
+    attention_mask = inputs["attention_mask"].to(device)
+    model.to(device)
+    token_logits = model(input_ids, attention_mask=attention_mask).logits
+    mask_token_index = torch.where(inputs_ex["input_ids"] == tokenizer.mask_token_id)[1]
+    mask_token_logits = token_logits[0, mask_token_index, :]
+    top_5_tokens = torch.topk(mask_token_logits, NUM_CLASSES, dim=1).indices[0].tolist()
+    score = torch.nn.functional.softmax(mask_token_logits)[0]
+    top_5_score = torch.topk(score, NUM_CLASSES).values.tolist()
+    return {tokenizer.decode([tok]): float(score) for tok, score in zip(top_5_tokens, top_5_score)}
+    
+gr.Interface(fn=predict, 
+             inputs=gr.inputs.Textbox(lines=2, placeholder="Your Text… "),
+             title="Mask Language Modeling - Demo",
+             outputs=gr.outputs.Label(num_top_classes=NUM_CLASSES)).launch()