linting

app.py

@@ -2,17 +2,21 @@ import streamlit as st
 import transformers
 import matplotlib.pyplot as plt
 
+
 @st.cache(allow_output_mutation=True)
 def get_pipe():
     model_name = "joeddav/distilbert-base-uncased-go-emotions-student"
     model = transformers.AutoModelForSequenceClassification.from_pretrained(model_name)
     tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
-    pipe = transformers.pipeline('text-classification', model=model, tokenizer=tokenizer, return_all_scores=True, truncation=True)
+    pipe = transformers.pipeline('text-classification', model=model, tokenizer=tokenizer,
+                                 return_all_scores=True, truncation=True)
     return pipe
 
+
 def sort_predictions(predictions):
     return sorted(predictions, key=lambda x: x['score'], reverse=True)
 
+
 st.set_page_config(page_title="Emotion Prediction")
 st.title("Emotion Prediction")
 st.write("Type text into the text box and then press 'Predict' to get the predicted emotion.")
@@ -24,7 +28,7 @@ text = st.text_area('Enter text here:')
 submit = st.button('Predict')
 
 if submit:
-    
+
     prediction = pipe(text)[0]
     prediction = sort_predictions(prediction)
 
@@ -34,7 +38,7 @@ if submit:
            tick_label=[p['label'] for p in prediction])
     ax.tick_params(rotation=90)
     ax.set_ylim(0, 1)
-    
+
     st.header('Prediction:')
     st.pyplot(fig)