Update app.py

app.py

@@ -54,6 +54,10 @@ def tester(text):
  
 emo = st.text_input("Enter a text/phrase/sentence. A corresponding song will be recommended based on its emotion.")
 
+
+st.sidebar.subheader("Description")
+st.sidebar.write("This application detects the emotion behind your text input and recommends a song that matches it.")
+
 st.sidebar.subheader("Disclaimer/Limitations")
 st.sidebar.write("The model only outputs sadness, joy, love, anger, fear, and surprise. With that said, it does not completely encompass the emotions that a human being feels, and the application only suggests a playlist based on the aforementioned emotions.")
 
@@ -61,9 +65,6 @@ st.sidebar.subheader("Model Description")
 st.sidebar.write("This application uses the DistilBERT model, a distilled version of BERT. The BERT framework uses a bidirectional transformer that allows it to learn the context of a word based on the left and right of the word. According to a paper by V. Sanh, et al., DistilBERT can \"reduce the size of a BERT model by 40%, while retaining 97% of its language understanding capabilities, and being 60% faster.\" This is why the DistilBERT model was used. For more information about the paper, please check out this [link](https://share.streamlit.io/mesmith027/streamlit_webapps/main/MC_pi/streamlit_app.py).")
 st.sidebar.write("The specific DistilBERT model used for this is Bhadresh Savani's [distilbert-base-uncased-emotion] (https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion). It is fine-tuned on the Emotion Dataset from Twitter, which can be found [here](https://huggingface.co/datasets/viewer/?dataset=emotion).")
 
-
-
-
 st.sidebar.subheader("Performance Benchmarks")
 st.sidebar.write("[Distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion)")
 st.sidebar.write("Accuracy = 93.8")