formatting

app.py

@@ -13,22 +13,25 @@ models = {
 selected_model = st.selectbox("Select a pretrained model", list(models.keys()))
 
 # roBERTa specific label map
-roberta_label_map = {"LABEL_0": "negative", "LABEL_1": "neutral", "LABEL_2": "positive"}
+roberta_label_map = {"LABEL_0": "negative",
+                     "LABEL_1": "neutral", "LABEL_2": "positive"}
 
 # Load the selected model and tokenizer
 model_name = models[selected_model]
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-sentiment_pipeline = pipeline("sentiment-analysis", model=model_name, tokenizer=tokenizer)
+sentiment_pipeline = pipeline(
+    "sentiment-analysis", model=model_name, tokenizer=tokenizer)
 
 # Get user input and perform sentiment analysis
-text_input = st.text_input("Enter text for sentiment analysis", "Anish is a very awesome and dedicated TA! 🤗")
+text_input = st.text_input("Enter text for sentiment analysis",
+                           "Anish is a very awesome and dedicated TA! 🤗")
 submit_btn = st.button("Submit")
 
 if submit_btn and text_input:
     result = sentiment_pipeline(text_input)
     if selected_model == "roBERTa":
-      st.write("Sentiment:", roberta_label_map[result[0]["label"]])
-      st.write("Score:", result[0]["score"])
+        st.write("Sentiment:", roberta_label_map[result[0]["label"]])
+        st.write("Score:", result[0]["score"])
     else:
-      st.write("Sentiment:", result[0]["label"])
-      st.write("Score:", result[0]["score"])
+        st.write("Sentiment:", result[0]["label"])
+        st.write("Score:", result[0]["score"])