streamlit files

app.py

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+import streamlit as st
+import pandas as pd
+from scipy.sparse import csr_matrix, coo_matrix
+from sklearn.metrics.pairwise import cosine_similarity
+
+#loading the song_dataset [in cache form to minimize resource usage]
+@st.cache_data
+def load_songData(path_to_file):
+    df =pd.read_csv(path_to_file)
+    return df
+    
+#calling load_songData function
+song_df =  load_songData("song_dataset.csv")
+
+
+# create a series with song IDs as index and titles as values
+song_titles_series = song_df.drop_duplicates(subset=["song"]).set_index("song")["title"]
+
+# sparse item-item similarity: transpose sparse matrix because we want item-item similarity (songs as rows)
+interaction_matrix = song_df.pivot_table(index="user", columns="song", values="play_count", fill_value=0)
+sparse_matrix = csr_matrix(interaction_matrix)
+
+item_similarity_sparse = cosine_similarity(sparse_matrix.T, dense_output=False)
+coo = coo_matrix(item_similarity_sparse)
+
+item_similarity_df = pd.DataFrame({
+        "item_1": interaction_matrix.columns[coo.row],
+        "item_2": interaction_matrix.columns[coo.col],
+        "similarity": coo.data,
+    })
+
+#for song-based recommendation engine call
+
+def recommend_similar_items_sparse(selected_songs, top_n):
+    scores = {}
+    for song in selected_songs:
+        # getting all rows where item_1 is the selected song
+        similar_items = item_similarity_df[item_similarity_df["item_1"] == song]
+        
+        for _, row in similar_items.iterrows():
+            similar_song = row["item_2"]
+            similarity = row["similarity"]
+            #filtering out songs already listened to by user
+            if similar_song not in selected_songs:
+                scores[similar_song] = scores.get(similar_song, 0) + similarity
+                
+        recommended_songs = sorted(scores.items(), key=lambda x: x[1], reverse=True)[:top_n]
+        return [song for song, score in recommended_songs]
+        
+
+# Streamlit Interface
+st.title("Song Recommendation Engine[Proj_charlie]")
+st.write("**Please select as many songs as you've listened to.**")
+
+# Geting User inputs
+all_songs = song_titles_series.index.tolist()
+selected_songs = st.multiselect(
+    "Select Song(s):", options=all_songs, format_func=lambda x: song_titles_series[x],
+)
+
+
+# Recommendation Magic
+
+if st.button("Get Recommendations"):
+    if not selected_songs:
+        st.warning("Please select song(s) you have listened to!")
+    else:
+        recommendations = recommend_similar_items_sparse(selected_songs, top_n=10)
+        if recommendations:
+            # displaying selected songs:
+            st.subheader("Great! You selected:")
+            for idx, song in enumerate(selected_songs, start=1):
+                st.write(f"{idx}. {song_titles_series.get(song, song)}")
+                
+            # displaying recommended songs
+            st.subheader("Top Recommended songs:")
+            for idx, song in enumerate(recommendations, start=1):
+                st.write(f"{idx}. {song_titles_series.get(song, song)}")
+        else:
+            st.info("Oops! No recommendations available for selected songs.")

requirements.txt

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+pandas==2.2.3
+scikit_learn==1.5.2
+scipy==1.14.1
+streamlit==1.40.1