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app.py
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@@ -1,9 +1,11 @@
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import gradio as gr
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import os
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os.environ["
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from transformers import AutoTokenizer, TFAutoModelForSequenceClassification, TextClassificationPipeline
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tokenizer = AutoTokenizer.from_pretrained("dipesh/Intent-Classification-Bert-Base-Cased")
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model = TFAutoModelForSequenceClassification.from_pretrained("dipesh/Intent-Classification-Bert-Base-Cased")
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@@ -13,8 +15,17 @@ intent_classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer,
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def predict(input_text):
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ans = intent_classifier(input_text)
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iface = gr.Interface(fn=predict, inputs="text", outputs="text", title="Intent Classifier",
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import gradio as gr
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import os
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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from transformers import AutoTokenizer, TFAutoModelForSequenceClassification, TextClassificationPipeline
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tokenizer = AutoTokenizer.from_pretrained("dipesh/Intent-Classification-Bert-Base-Cased")
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model = TFAutoModelForSequenceClassification.from_pretrained("dipesh/Intent-Classification-Bert-Base-Cased")
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def predict(input_text):
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ans = "intent_classifier(input_text)"
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# list of questions words
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question_words = ['will', 'is', 'when', 'may', 'should', 'would', 'which', 'shall', 'does', 'why', 'can', 'whose',
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'do', 'was', 'where', 'who', 'might', 'how', 'must', 'whom', 'are', 'did', 'were', 'what',
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'could']
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question_words = set(question_words)
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if ans.split()[0] in question_words:
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ans += "?"
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ans = intent_classifier(input_text)
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return ans[0]['label'], ans[0]['score']
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iface = gr.Interface(fn=predict, inputs="text", outputs="text", title="Intent Classifier",
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