Update app.py

app.py

@@ -1,21 +1,14 @@
 # app.py
-import streamlit as st
+import gradio as gr
 from transformers import pipeline
-from textblob import TextBlob
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-pipe = pipeline('sentiment-analysis')
-st.title("Hugging Face Sentiment Analysis Spaces Example")
-st.subheader("What framework would you like to use for Sentiment Analysis")
-#Picking what NLP task you want to do
-option = st.selectbox('Framework',('Transformers', 'TextBlob')) #option is stored in this variable
-#Textbox for text user is entering
-st.subheader("Enter the text you'd like to analyze.")
-text = st.text_input('Enter text') #text is stored in this variable
+#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']
 
-if option == 'Transformers':
-    out = pipe(text)
-else:
-    out = TextBlob(text)
-    out = out.sentiment
-st.write("Sentiment of Text: ")
-st.write(out)
+#Creating the Gradio interface with input textbox and output text
+gr.Interface(fn=classify, inputs=["textbox"], outputs="text").launch()