app.py

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()