import gradio as gr import onnxruntime as rt from transformers import AutoTokenizer import torch, json tokenizer = AutoTokenizer.from_pretrained("distilroberta-base") with open("genre_types_encoded.json", "r") as fp: encode_genre_types = json.load(fp) genres = list(encode_genre_types.keys()) inf_session = rt.InferenceSession('udemy-classifier-quantized.onnx') input_name = inf_session.get_inputs()[0].name output_name = inf_session.get_outputs()[0].name def classify_courses_genre(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(genres, map(float, probs))) label = gr.outputs.Label(num_top_classes=5) iface = gr.Interface(fn=classify_courses_genre, inputs="text", outputs=label) iface.launch(inline=False)