Delete pages/Recommender.py

pages/Recommender.py

@@ -1,175 +0,0 @@
-import streamlit as st
-
-# Configure Streamlit page
-st.set_page_config(
-    page_title="Find the Song that You Like🎸", page_icon="🎸", layout="wide"
-)
-
-import pandas as pd
-import plotly.express as px
-import streamlit.components.v1 as components
-from sklearn.neighbors import NearestNeighbors
-
-
-@st.cache(allow_output_mutation=True)
-def data_import():
-    """Function for loading in cleaned data csv file."""
-    df = pd.read_csv("data/clean_data.csv")
-    df["genres"] = df.genres.apply(
-        lambda x: [i[1:-1] for i in str(x)[1:-1].split(", ")]
-    )
-    df_explode = df.explode("genres")
-    return df_explode
-
-
-genre_names = [
-    "Dance Pop",
-    "Electronic",
-    "Electropop",
-    "Hip Hop",
-    "Jazz",
-    "K-pop",
-    "Latin",
-    "Pop",
-    "Pop Rap",
-    "R&B",
-    "Rock",
-]
-audio_params = [
-    "acousticness",
-    "danceability",
-    "energy",
-    "instrumentalness",
-    "valence",
-    "tempo",
-]
-
-df_explode = data_import()
-
-
-def match_song(genre, yr_start, yr_end, test_feat):
-    """Function for finding similar songs with KNN algorithm."""
-    genre = genre.lower()
-    genre_data = df_explode[
-        (df_explode["genres"] == genre)
-        & (df_explode["release_year"] >= yr_start)
-        & (df_explode["release_year"] <= yr_end)
-    ]
-    genre_data = genre_data.sort_values(by="popularity", ascending=False)[:500]
-
-    # Load KNN from SkLearn
-    neigh = NearestNeighbors()
-    neigh.fit(genre_data[audio_params].to_numpy())
-
-    n_neighbors = neigh.kneighbors(
-        [test_feat], n_neighbors=len(genre_data), return_distance=False
-    )[0]
-
-    uris = genre_data.iloc[n_neighbors]["uri"].tolist()
-    audios = genre_data.iloc[n_neighbors][audio_params].to_numpy()
-
-    return uris, audios
-
-
-# Setup page order
-def page():
-    title = "Find Your Song🎸"
-    st.title(title)
-
-    st.write(
-        "Get recommended songs on Spotify based on genre and key audio parameters."
-    )
-    st.markdown("##")
-
-    # Streamlit column layout
-    with st.container():
-        col1, col2, col3, col4 = st.columns((2, 0.5, 0.5, 0.5))
-
-        with col3:
-            st.markdown("***Select genre:***")
-            genre = st.radio("", genre_names, index=genre_names.index("Rock"))
-
-        with col1:
-            st.markdown("***Select audio parameters to customize:***")
-            yr_start, yr_end = st.slider(
-                "Select the year range", 1908, 2022, (1980, 2022)
-            )
-            acousticness = st.slider("Acousticness", 0.0, 1.0, 0.5)
-            danceability = st.slider("Danceability", 0.0, 1.0, 0.5)
-            energy = st.slider("Energy", 0.0, 1.0, 0.5)
-            instrumentalness = st.slider("Instrumentalness", 0.0, 1.0, 0.5)
-            valence = st.slider("Valence", 0.0, 1.0, 0.45)
-            tempo = st.slider("Tempo", 0.0, 244.0, 125.01)
-
-    pr_page_tracks = 6
-    test_feat = [acousticness, danceability, energy, instrumentalness, valence, tempo]
-    uris, audios = match_song(genre, yr_start, yr_end, test_feat)
-
-    tracks = []
-    for uri in uris:
-        track = """<iframe src="https://open.spotify.com/embed/track/{}" width="280" height="400" frameborder="0" allowtransparency="true" allow="encrypted-media"></iframe>""".format(
-            uri
-        )
-        tracks.append(track)
-
-    if "previous_inputs" not in st.session_state:
-        st.session_state["previous_inputs"] = [genre, yr_start, yr_end] + test_feat
-
-    current_inputs = [genre, yr_start, yr_end] + test_feat
-    if current_inputs != st.session_state["previous_inputs"]:
-        if "start_track_i" in st.session_state:
-            st.session_state["start_track_i"] = 0
-
-        st.session_state["previous_inputs"] = current_inputs
-
-    if "start_track_i" not in st.session_state:
-        st.session_state["start_track_i"] = 0
-
-    with st.container():
-        col1, col2, col3 = st.columns([2, 1, 2])
-        if st.button("More Songs"):
-            if st.session_state["start_track_i"] < len(tracks):
-                st.session_state["start_track_i"] += pr_page_tracks
-
-        current_tracks = tracks[
-            st.session_state["start_track_i"] : st.session_state["start_track_i"]
-            + pr_page_tracks
-        ]
-        current_audios = audios[
-            st.session_state["start_track_i"] : st.session_state["start_track_i"]
-            + pr_page_tracks
-        ]
-        if st.session_state["start_track_i"] < len(tracks):
-            for i, (track, audio) in enumerate(zip(current_tracks, current_audios)):
-                if i % 2 == 0:
-                    with col1:
-                        components.html(
-                            track,
-                            height=400,
-                        )
-                        with st.expander("Display Chart"):
-                            df = pd.DataFrame(dict(r=audio[:5], theta=audio_params[:5]))
-                            fig = px.line_polar(
-                                df, r="r", theta="theta", line_close=True
-                            )
-                            fig.update_layout(height=400, width=340)
-                            st.plotly_chart(fig)
-
-                else:
-                    with col3:
-                        components.html(
-                            track,
-                            height=400,
-                        )
-                        with st.expander("Display Chart"):
-                            df = pd.DataFrame(dict(r=audio[:5], theta=audio_params[:5]))
-                            fig = px.line_polar(
-                                df, r="r", theta="theta", line_close=True
-                            )
-                            fig.update_layout(height=400, width=340)
-                            st.plotly_chart(fig)
-        else:
-            st.write("No more songs")
-
-
-page()