Delete Introduction.py

Introduction.py

@@ -1,33 +0,0 @@
-import streamlit as st
-from streamlit import session_state as session
-
-# Configure Streamlit page
-st.set_page_config(page_title="Song Recommender🎶", page_icon="🎶")
-
-st.title("Song Recommender🎶")
-st.markdown("Click on '**Recommender**' from the side panel to get started.")
-st.markdown("**How does it work?**")
-st.markdown(
-    "The songs come from the [Spotify and Genius Track Dataset](https://www.kaggle.com/datasets/saurabhshahane/spotgen-music-dataset) on Kaggle. The [k-Nearest Neighbor algorithm](https://scikit-learn.org/stable/modules/neighbors.html) is used to obtain recommendations, i.e., the top songs which are closest in distance to the set of parameter inputs specified by you."
-)
-
-st.markdown("This app will recommend you songs based on the characteristics below.")
-st.markdown(
-    """  
-            **Acousticness**: A metric describing the 'acousticness' of a song. 1.0 represents high confidence the song is acoustic.<br>
-            
-            **Danceability**: Describes a song's suitability for dancing based on combination of elements including tempo, rhythm stability, beat strength, and overall regularity. 
-                        0.0 is least danceable and 1.0 is most danceable.<br>
-
-            **Energy**: Measure of intensity and activity. Often, energetic songs feel fast, loud, and noisy.<br>
-
-            **Liveness**: A metric describing the likelihood that a track is a recording of a live performance.<br>
-
-            **Speechiness**: How much lyrics the track contains.<br>
-
-            **Valence**: A metric ranging from 0.0 to 1.0 describing the positivity conveyed by a track.<br>
-            
-            Source: [Spotify Web API](https://developer.spotify.com/documentation/web-api/reference)
-            """,
-    unsafe_allow_html=True,
-)