Hi @Neel-Gupta,
The steps are usually as follows
You load your trained model similar to this
loaded_model = TFDistilBertForSequenceClassification.from_pretrained("./sentiment")

Then you usually have to run your text through the preprocessing in the same way you did for your training. Usually some kind of tokenizing involved
predict_input = tokenizer.encode(test_sentence,
                                 truncation=True,
                                 padding=True,
                                 return_tensors="tf")

And then you finally can predict based on your loaded model and the preprocessed text
tf_output = loaded_model.predict(predict_input)[0]

Disclaimer, I am the author of this article, but It covers the process from data to deployment.
https://blog.doit-intl.com/performing-surprisingly-easy-sentiment-analysis-on-google-cloud-platform-fc26b2e2b4b 50