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import gradio as gr | |
import onnxruntime as rt | |
from transformers import AutoTokenizer | |
import torch, json | |
tokenizer = AutoTokenizer.from_pretrained("distilroberta-base") | |
with open("tag_types_encoded.json", "r") as fp: | |
encode_tag_types = json.load(fp) | |
tags = list(encode_tag_types.keys()) | |
inf_session = rt.InferenceSession('tag_classifier_quantized.onnx') | |
input_name = inf_session.get_inputs()[0].name | |
output_name = inf_session.get_outputs()[0].name | |
def classify_blog_tags(description): | |
input_ids = tokenizer(description)['input_ids'][:512] | |
logits = inf_session.run([output_name], {input_name: [input_ids]})[0] | |
logits = torch.FloatTensor(logits) | |
probs = torch.sigmoid(logits)[0] | |
return dict(zip(tags, map(float, probs))) | |
label = gr.outputs.Label(num_top_classes=5) | |
iface = gr.Interface(fn=classify_blog_tags, inputs="text", outputs=label) | |
iface.launch(inline=False) |