Use BERT Zero-Shot Classifier

app.py

@@ -1,10 +1,36 @@
 import gradio
 
+from transformers import pipeline
+classifier = pipeline("zero-shot-classification",
+                      model="facebook/bart-large-mnli")
+
+# sequence_to_classify = "one day I will see the world"
+# candidate_labels = ['travel', 'cooking', 'dancing']
+# CATEGORIES = ['doc_type.jur', 'doc_type.Spec', 'doc_type.ZDF', 'doc_type.Publ',
+#        'doc_type.Scheme', 'content_type.Alt', 'content_type.Krypto',
+#        'content_type.Karte', 'content_type.Banking', 'content_type.Reg',
+#        'content_type.Konto']
+categories = [
+    "Legal", "Specification", "Facts and Figures",
+    "Publication", "Payment Scheme",
+    "Alternative Payment Systems", "Crypto Payments",
+    "Card Payments", "Banking", "Regulations", "Account Payments"
+]
+
+def clf_text(txt: str):
+    return classifier(txt, categories)
+# classifier(sequence_to_classify, candidate_labels)
+#{'labels': ['travel', 'dancing', 'cooking'],
+# 'scores': [0.9938651323318481, 0.0032737774308770895, 0.002861034357920289],
+# 'sequence': 'one day I will see the world'}
+
+
 def my_inference_function(name):
   return "Hello " + name + "!"
 
 gradio_interface = gradio.Interface(
-  fn = my_inference_function,
+  # fn = my_inference_function,
+  fn = clf_text,
   inputs = "text",
   outputs = "text"
 )