import gradio as gr import onnxruntime from transformers import AutoTokenizer import torch, json token = AutoTokenizer.from_pretrained('distilroberta-base') types = ['Toxic','Severe_toxic','Obscene','Threat','Insult','Identity_hate'] inf_session = onnxruntime.InferenceSession('classifier-quantized.onnx') input_name = inf_session.get_inputs()[0].name output_name = inf_session.get_outputs()[0].name def classify(review): input_ids = token(review)['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(types, map(float, probs))) label = gr.outputs.Label(num_top_classes=5) iface = gr.Interface(fn=classify, inputs="text", outputs=label) iface.launch(inline=False)