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| import gradio as gr | |
| import os | |
| os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" | |
| os.environ["CUDA_VISIBLE_DEVICES"] = "0" | |
| from transformers import AutoTokenizer, TFAutoModelForSequenceClassification, TextClassificationPipeline | |
| tokenizer = AutoTokenizer.from_pretrained("dipesh/Intent-Classification-Bert-Base-Cased") | |
| model = TFAutoModelForSequenceClassification.from_pretrained("dipesh/Intent-Classification-Bert-Base-Cased") | |
| intent_classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=False, | |
| framework='tf') | |
| def predict(input_text): | |
| ans = "intent_classifier(input_text)" | |
| # list of questions words | |
| question_words = ['will', 'is', 'when', 'may', 'should', 'would', 'which', 'shall', 'does', 'why', 'can', 'whose', | |
| 'do', 'was', 'where', 'who', 'might', 'how', 'must', 'whom', 'are', 'did', 'were', 'what', | |
| 'could'] | |
| question_words = set(question_words) | |
| if ans.split()[0] in question_words: | |
| ans += "?" | |
| ans = intent_classifier(input_text) | |
| return {"class": ans[0]['label'], | |
| "accuracy": ans[0]['score']} | |
| iface = gr.Interface(fn=predict, inputs="text", outputs="json", title="Intent Classifier", | |
| description="Classifier") | |
| iface.launch() | |