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app.py

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+import gradio as gr
+import transformers
+from transformers import pipeline, BertForSequenceClassification, BertTokenizer
+
+def classify(input_text):
+    tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')
+    model = BertForSequenceClassification.from_pretrained('./bert-cls')
+    classifier = pipeline("text-classification", model=model, tokenizer=tokenizer, top_k=3)
+    class_dict = {0:'story',
+                1:'culture',
+                2:'entertainment',
+                3:'sports',
+                4:'finance',
+                6:'house',
+                7:'car',
+                8:'edu',
+                9:'tech',
+                10:'military',
+                12:'travel',
+                13:'world',
+                14:'stock',
+                15:'argriculture',
+                16:'game'}
+    output = classifier([input_text])
+    idx_list = [output[0][i]['label'].split('_')[1] for i in range(len(output[0]))]
+    label_list = [class_dict[int(idx)] for idx in idx_list]
+    score_list = [output[0][i]['score'] for i in range(len(output[0]))]
+    return dict(zip(label_list, score_list))
+label = gr.Label()
+iface = gr.Interface(fn = classify, 
+                     inputs = "text", 
+                     outputs = label,
+                     title = 'chinese news classification')
+                     
+iface.launch(inline = False)