Create app.py

app.py

@@ -0,0 +1,31 @@
+import os
+
+import gradio as gr
+import torch
+from transformers.models.bert import BertTokenizer, BertForSequenceClassification
+path='./roberta-base-cold'
+vocab='vocab.txt'
+tokenizer = BertTokenizer.from_pretrained(os.path.join(path,vocab))
+model = BertForSequenceClassification.from_pretrained('roberta-base-cold')
+model.eval()
+
+
+
+def get_output(text):
+    output=[]
+    model_input = tokenizer(text, return_tensors="pt", padding=True)
+    model_output = model(**model_input, return_dict=False)
+    prediction = torch.argmax(model_output[0].cpu(), dim=-1)
+    prediction = [p.item() for p in prediction]
+    for i in range(len(prediction)):
+        if prediction[i]==0:
+            output.append("消极")
+        else:
+            output.append('积极')
+
+
+
+    return output
+
+demo=gr.Interface(fn=get_output,inputs='text',outputs='text')
+demo.launch()