# app.py
import gradio as gr
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForSequenceClassification

#Defining the classify function which takes text as input and returns the label of the sentiment
def classify(text):
  # Initializing the pipeline for sentiment analysis
  cls = pipeline('text-classification', model='RJuro/dk_emotion_bert_in_class')
  # Predicting the sentiment label for the input text
  return cls(text)[0]['label']

#Creating the Gradio interface with input textbox and output text
gr.Interface(fn=classify, inputs=["textbox"], outputs="text").launch()