Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.neighbors import NearestNeighbors
+from sklearn.decomposition import TruncatedSVD
+import numpy as np
+
+# Page config
+st.set_page_config(
+    page_title="MusicMind - Smart Music Recommendations",
+    page_icon="🎵",
+    layout="wide"
+)
+
+# Custom CSS
+st.markdown("""
+<style>
+    .main {
+        background-color: #f9f9f9;
+    }
+    .stButton button {
+        background-color: #76818e;
+        color: white;
+        border-radius: 20px;
+        padding: 10px 25px;
+        border: none;
+        transition: background-color 0.3s;
+    }
+    .stButton button:hover {
+        background-color: #5348d4;
+    }
+    .recommendation-card {
+        background-color: #76818e;
+        padding: 20px;
+        border-radius: 10px;
+        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+        margin: 10px 0;
+        transition: transform 0.3s;
+    }
+    .recommendation-card:hover {
+        transform: translateY(-5px);
+    }
+    .recommendation-card h3 {
+        font-family: 'Poppins', sans-serif;
+        color: #e7d7c1;
+    }
+    .recommendation-card p {
+        font-family: 'Roboto', sans-serif;
+        color: #262730;
+    }
+    .recommendation-card a {
+        text-decoration: none;
+        color: #e7d7c1;
+        font-weight: 600;
+        display: inline-block;
+        margin-top: 10px;
+        transition: color 0.3s;
+    }
+    .youtube-link {
+        background-color: #ff4b4b;
+        color: white !important;
+        padding: 8px 16px;
+        border-radius: 20px;
+        text-decoration: none;
+        display: inline-flex;
+        align-items: center;
+        gap: 8px;
+        font-family: 'Roboto', sans-serif;
+        font-weight: 500;
+        transition: all 0.3s ease;
+    }
+    .youtube-link:before {
+        content: "▶";
+        font-size: 0.8em;
+    }
+    .youtube-link:hover {
+        background-color: #cc0000;
+        transform: scale(1.05);
+        box-shadow: 0 4px 12px rgba(255, 0, 0, 0.2);
+    }
+</style>
+<link href="https://fonts.googleapis.com/css2?family=Poppins:wght@400;600&family=Roboto:wght@400;500&display=swap" rel="stylesheet">
+""", unsafe_allow_html=True)
+
+# Load and prepare data
+@st.cache_data
+def load_data():
+    df = pd.read_csv("song_dataset.csv")
+    return df
+
+df = load_data()
+
+@st.cache_resource
+def run_imps(df):
+    required_columns = ['user', 'song', 'play_count', 'title', 'artist_name', 'release']
+    if not all(col in df.columns for col in required_columns):
+        raise ValueError(f"Dataset must contain the following columns: {required_columns}")
+
+    df = df.drop_duplicates(subset=['song', 'title', 'artist_name', 'release'])
+    df['combined_features'] = (df['title'] + " " + df['artist_name'] + " " + df['release']).fillna("")
+
+    # Content-Based Filtering
+    tfidf = TfidfVectorizer(max_features=5000, stop_words='english')
+    tfidf_matrix = tfidf.fit_transform(df['combined_features'])
+
+    nn = NearestNeighbors(n_neighbors=10, metric='cosine', algorithm='auto')
+    nn.fit(tfidf_matrix)
+
+    # Collaborative Filtering
+    user_song_matrix = df.pivot_table(index='user', columns='song', values='play_count', fill_value=0)
+    svd = TruncatedSVD(n_components=20)
+    user_factors = svd.fit_transform(user_song_matrix)
+    song_factors = svd.components_.T
+
+    return df, tfidf, tfidf_matrix, nn, user_song_matrix, user_factors, song_factors
+
+df, tfidf, tfidf_matrix, nn, user_song_matrix, user_factors, song_factors = run_imps(df)
+
+# Content-based recommendation function
+def content_based_recommend(song_title, top_n=5):
+    try:
+        idx = df[df['title'] == song_title].index[0]
+        distances, indices = nn.kneighbors(tfidf_matrix[idx], n_neighbors=top_n + 1)
+        song_indices = indices.flatten()[1:]
+        return df.iloc[song_indices][['title', 'artist_name', 'release']].drop_duplicates()
+    except IndexError:
+        return pd.DataFrame(columns=['title', 'artist_name', 'release'])
+
+def collaborative_recommend(user_id, top_n=5):
+    if user_id not in user_song_matrix.index:
+        return pd.DataFrame(columns=['title', 'artist_name', 'release'])
+
+    user_vector = user_factors[user_song_matrix.index.get_loc(user_id)]
+    scores = np.dot(song_factors, user_vector)
+
+    listened_songs = user_song_matrix.loc[user_id][user_song_matrix.loc[user_id] > 0].index
+    scores = {song: score for song, score in zip(user_song_matrix.columns, scores) if song not in listened_songs}
+
+    recommended_songs = sorted(scores.items(), key=lambda x: x[1], reverse=True)[:top_n]
+    recommended_song_ids = [song for song, _ in recommended_songs]
+    return df[df['song'].isin(recommended_song_ids)][['title', 'artist_name', 'release']].drop_duplicates()
+
+# Hybrid Recommendation
+def hybrid_recommendv2(user_id, song_titles, top_n=5):
+    collab_recs = collaborative_recommend(user_id, top_n)
+    content_recs = pd.DataFrame()
+    for song_title in song_titles:
+        content_recs = pd.concat([content_recs, content_based_recommend(song_title, top_n)], ignore_index=True)
+    hybrid_recs = pd.concat([collab_recs, content_recs]).drop_duplicates().sample(frac=1).reset_index(drop=True)
+    return hybrid_recs.head(top_n)
+
+# Sidebar and Main UI
+with st.sidebar:
+    st.header("🎯 Customize Your Recommendations")
+    user_id = st.selectbox(
+        "Select User ID",
+        options=df['user'].unique(),
+        index=0
+    )
+    user_songs = df[df['user'] == user_id]['title'].unique()
+    song_title = st.multiselect(
+        "Select Songs You Like",
+        options=user_songs,
+        default=user_songs[:1] if len(user_songs) > 0 else None
+    )
+    top_n = st.slider("Number of Recommendations", min_value=1, max_value=10, value=5)
+    get_recs = st.button("Get Recommendations! 🎶")
+
+if get_recs:
+    st.header("🎵 Your Recommendations")
+    recommendations = hybrid_recommendv2(user_id, song_title, top_n)
+    if recommendations.empty:
+        st.error("No recommendations found. Try selecting different songs or users.")
+    else:
+        st.balloons()
+        for idx, row in recommendations.iterrows():
+            youtube_link = f"https://www.youtube.com/results?search_query={row['title']}+{row['artist_name']}"
+            st.markdown(f"""
+                <div class="recommendation-card">
+                    <h3>{row['title']}</h3>
+                    <p><strong>Artist:</strong> {row['artist_name']}</p>
+                    <p><strong>Album:</strong> {row['release']}</p>
+                    <a href="{youtube_link}" target="_blank" class="youtube-link">
+                        Watch on YouTube
+                </div>
+            """, unsafe_allow_html=True)