Create app.py

app.py

@@ -0,0 +1,47 @@
+import streamlit as st
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics.pairwise import cosine_similarity
+
+# Load your dataset
+df1 = pd.read_csv('your_dataset.csv')  # Replace 'your_dataset.csv' with your actual dataset filename
+
+# Copy the content-based recommendation code
+audio_features = df1[['danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness',
+                      'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo',
+                      'duration_ms', 'time_signature']]
+mood_cats = df1[['mood_cats']]
+
+# Normalize audio features
+scaler = StandardScaler()
+audio_features_scaled = scaler.fit_transform(audio_features)
+
+# Combine mood and audio features
+combined_features = pd.concat([mood_cats, pd.DataFrame(audio_features_scaled)], axis=1)
+
+# Calculate similarity matrix
+similarity_matrix = cosine_similarity(combined_features)
+
+def recommend_cont(song_index, num_recommendations=5):
+    song_similarity = similarity_matrix[song_index]
+    # Get indices and similarity scores of top similar songs
+    similar_songs = sorted(list(enumerate(song_similarity)), key=lambda x: x[1], reverse=True)[1:num_recommendations+1]
+    recommended_song_indices = [idx for idx, similarity in similar_songs]
+    recommended_songs = df1.iloc[recommended_song_indices].copy()
+    recommended_songs['score'] = [similarity for idx, similarity in similar_songs]
+    return recommended_songs
+
+# Streamlit app
+st.title('Content-Based Recommender System')
+
+# Select a song index
+selected_index = st.slider('Select a song index', 0, len(df1)-1, 0)
+
+# Get recommendations
+recommended_songs = recommend_cont(selected_index)
+
+# Display recommended songs using st.write
+st.subheader('Recommended Songs:')
+for index in recommended_songs.index:
+    st.write(f"Song Index: {index}, Title: {recommended_songs.loc[index, 'track_name']}, Artist: {recommended_songs.loc[index, 'track_artist']}, Score: {recommended_songs.loc[index, 'score']}")
+