Delete app.py

app.py

@@ -1,177 +0,0 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
-from sklearn.decomposition import TruncatedSVD
-
-# Add this at the very top of the file, before any other Streamlit commands
-st.set_page_config(layout="wide")
-
-# Add custom CSS after the st.set_page_config
-st.markdown("""
-<style>
-.small-select {
-    max-width: 200px !important;
-}
-.stButton > button {
-    background-color: pink;
-    color: black !important;
-    border: none;
-}
-.stButton > button:hover {
-    background-color: pink !important;
-    color: white !important;
-    border: 2px solid white !important;
-}
-/* Style for selected items in multiselect */
-.stMultiSelect [data-baseweb="tag"] {
-    background-color: pink !important;
-    color: black !important;
-}
-/* Add focus/click style for multiselect and select */
-.stMultiSelect [data-baseweb="select"] > div:first-child,
-.stSelectbox [data-baseweb="select"] > div:first-child {
-    border-color: white !important;
-    box-shadow: 0 0 0 1px white !important;
-}
-</style>
-""", unsafe_allow_html=True)
-
-# Cache data loading
-@st.cache_data
-def load_data():
-    df = pd.read_csv('song_dataset.csv')
-    return df
-
-# Cache matrix computations
-@st.cache_data
-def compute_matrices(df_songsDB):
-    user_item_matrix = df_songsDB.pivot_table(index='user', columns='song', values='play_count', fill_value=0)
-    svd = TruncatedSVD(n_components=20, random_state=20)
-    svd_matrix = svd.fit_transform(user_item_matrix)
-    item_factors = svd.components_
-    return user_item_matrix, svd_matrix, item_factors
-
-# Load data and compute matrices once
-df_songsDB = load_data()
-user_item_matrix, svd_matrix, item_factors = compute_matrices(df_songsDB)
-
-# Cache the TF-IDF computation
-@st.cache_data
-def compute_tfidf(df_songsDB):
-    df_songsDB['combined_features'] = (
-        df_songsDB['artist_name'] + " " +
-        df_songsDB['release'] + " " +
-        df_songsDB['title']
-    )
-    tfidf = TfidfVectorizer()
-    tfidf_matrix = tfidf.fit_transform(df_songsDB['combined_features'])
-    return tfidf, tfidf_matrix
-
-# Helper functions
-def content_score_calculator(selected_songs, unlistened_songs):
-    df_songsDB['combined_features'] = (
-        df_songsDB['artist_name'] + " " +
-        df_songsDB['release'] + " " +
-        df_songsDB['title']
-    )
-
-    selected_song_features = df_songsDB[df_songsDB['title'].isin(selected_songs)]['combined_features']
-    unlistened_song_features = df_songsDB[df_songsDB['song'].isin(unlistened_songs)]['combined_features']
-
-    tfidf = TfidfVectorizer()
-    tfidf_matrix = tfidf.fit_transform(df_songsDB['combined_features'])
-
-    selected_matrix = tfidf.transform(selected_song_features)
-    unlistened_matrix = tfidf.transform(unlistened_song_features)
-    similarity_scores = cosine_similarity(selected_matrix, unlistened_matrix)
-
-    avg_similarity = similarity_scores.mean(axis=0)
-
-    return dict(zip(unlistened_songs, avg_similarity))
-
-def collaborative_score_calculator(user_id, unlistened_songs):
-    user_idx = user_item_matrix.index.get_loc(user_id)
-    user_vector = svd_matrix[user_idx]
-    cf_scores = {}
-
-    for song_id in unlistened_songs:
-        if (song_id in user_item_matrix.columns):
-            song_idx = user_item_matrix.columns.get_loc(song_id)
-            song_vector = item_factors[:, song_idx]
-            cf_scores[song_id] = np.dot(user_vector, song_vector)
-        else:
-            cf_scores[song_id] = 0
-    return cf_scores
-
-def hybridRecommendationEngine(user_id, selected_songs):
-    alpha = 0.5
-
-    listened_songs = df_songsDB[df_songsDB['user'] == user_id]['song'].unique()
-    all_songs = df_songsDB['song'].unique()
-    unlistened_songs = set(all_songs) - set(listened_songs)
-
-    cf_scores = collaborative_score_calculator(user_id, unlistened_songs)
-    content_scores = content_score_calculator(selected_songs, unlistened_songs)
-
-    final_scores = {}
-    for song_id in unlistened_songs:
-        cf_score = cf_scores.get(song_id, 0)
-        content_score = content_scores.get(song_id, 0)
-        final_scores[song_id] = alpha * cf_score + (1 - alpha) * content_score
-
-    scores = list(final_scores.values())
-    min_score = min(scores) if scores else 0
-    max_score = max(scores) if scores else 1
-
-    if max_score > min_score:
-        normalized_scores = {
-            song_id: (score - min_score) / (max_score - min_score)
-            for song_id, score in final_scores.items()
-        }
-    else:
-        normalized_scores = {song_id: 0.5 for song_id in final_scores}
-
-    sorted_songs = sorted(normalized_scores.items(), key=lambda x: x[1], reverse=True)
-    recommended_song_ids = [song_id for song_id, _ in sorted_songs[:10]]
-
-    recommended_songs = (
-        pd.DataFrame(recommended_song_ids, columns=['song'])
-        .merge(df_songsDB[['song', 'title', 'release', 'artist_name']].drop_duplicates(), on='song', how='left')
-        .assign(recommendation=lambda x: x['title'] + ' by ' + x['artist_name'])
-    )
-    return recommended_songs['recommendation'].tolist()
-
-# Streamlit app
-st.title("Delta Melody Match 🎶")
-
-# Make columns take more width
-col1, col2 = st.columns([2, 4])
-
-with col1:
-    with st.container():
-        user_id = st.selectbox(
-            "👤 Select User ID",
-            options=df_songsDB['user'].unique().tolist(),
-            key="small_select"
-        )
-        st.markdown('<style>div[data-testid="stSelectbox"] > div:first-child {max-width: 200px;}</style>', unsafe_allow_html=True)
-
-songs_selectable = df_songsDB[df_songsDB['user'] == user_id]['title'].unique()
-
-with col2:
-    song_titles = st.multiselect(
-        "🎵 Select Songs You Like",
-        options=songs_selectable,
-        default=songs_selectable[:1]
-    )
-
-# Make the recommendations table wider
-if st.button("Get Recommendations"):
-    st.subheader("Recommended Songs")
-    recommendations = hybridRecommendationEngine(user_id, song_titles)
-    for i, rec in enumerate(recommendations, 1):
-        # Split the recommendation into title and artist
-        title, artist = rec.split(' by ')
-        st.write(f"{i}. ***{title}*** by {artist}")