| import gradio as gr |
| from transformers import AutoTokenizer |
|
|
| from nfqa_model import RobertaNFQAClassification |
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|
|
| index_to_label = {0: 'NOT-A-QUESTION', |
| 1: 'FACTOID', |
| 2: 'DEBATE', |
| 3: 'EVIDENCE-BASED', |
| 4: 'INSTRUCTION', |
| 5: 'REASON', |
| 6: 'EXPERIENCE', |
| 7: 'COMPARISON'} |
|
|
|
|
| model = RobertaNFQAClassification.from_pretrained("Lurunchik/nf-cats") |
| nfqa_tokenizer = AutoTokenizer.from_pretrained("deepset/roberta-base-squad2") |
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|
|
| def get_nfqa_prediction(text): |
| output = model(**nfqa_tokenizer(text, return_tensors="pt")) |
| index = output.logits.argmax() |
| return index_to_label[int(index)] |
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|
|
|
| iface = gr.Interface(fn=get_nfqa_prediction, inputs="text", outputs="text") |
| iface.launch() |
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|
