Create app.py

app.py

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+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()