Spaces:
Running
Running
File size: 1,515 Bytes
b87d52c 0c97a7e 9d20c0e 5fe3cd7 5e3ae64 0c97a7e b87d52c 0c97a7e b87d52c 5e3ae64 b87d52c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 |
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):
res = classifier(txt, categories, multi_label=True)
items = sorted(zip(res["labels"], res["scores"]), key=lambda tpl: tpl[1])
# d = dict(zip(res["labels"], res["scores"]))
# output = [f"{lbl}:\t{score}" for lbl, score in items]
# return "\n".join(output)
return dict(items)
# 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 = clf_text,
inputs = "text",
outputs = "json"
)
gradio_interface.launch() |