| 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) |
