Create app.py

app.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Sat Nov 16 22:21:29 2024
+@author: User
+"""
+
+import gradio as gr
+import pandas as pd
+import pickle
+from sklearn.metrics.pairwise import cosine_similarity
+import heapq
+
+# Load data and model
+df = pd.read_csv('./DATA/spotify_millsongdata.csv')
+
+# Load saved embeddings
+with open("./DATA/lyrics_embeddings.pkl", "rb") as f:
+    lyrics_embeddings = pickle.load(f)
+
+# List of artists and songs
+artists = df['artist'].unique()
+song_titles = df['song']
+
+# Recommendation logic
+def recommend_songs(song_index, top_n=5, batch_size=100):
+    top_sim_scores = []
+    num_batches = len(df) // batch_size + 1
+
+    for i in range(num_batches):
+        start_idx = i * batch_size
+        end_idx = min((i + 1) * batch_size, len(df))
+
+        # Compute cosine similarity for the current batch
+        cosine_sim_batch = cosine_similarity(
+            lyrics_embeddings[start_idx:end_idx],
+            [lyrics_embeddings[song_index]]
+        )
+
+        # Select the top N most similar songs
+        for j, sim_score in enumerate(cosine_sim_batch):
+            global_idx = start_idx + j
+            heapq.heappush(top_sim_scores, (sim_score[0], global_idx))
+            if len(top_sim_scores) > top_n + 1:
+                heapq.heappop(top_sim_scores)
+
+    # Exclude the selected song itself and return the most similar songs with their similarity scores
+    top_sim_scores = sorted(top_sim_scores, key=lambda x: x[0], reverse=True)[1:top_n+1]
+    recommended_songs = [(song_titles[i[1]], df['link'][i[1]], round(i[0], 2)) for i in top_sim_scores]
+
+    return recommended_songs
+
+# Interface logic function
+def get_songs_by_artist(artist_name):
+    filtered_songs = df[df['artist'] == artist_name]['song'].tolist()
+    return gr.update(choices=filtered_songs, value=filtered_songs[0] if filtered_songs else None)
+
+
+def gradio_recommend(song_title):
+    try:
+        # Find the index of the selected song
+        song_index = song_titles[song_titles == song_title].index[0]
+        # Get recommended songs
+        recommendations = recommend_songs(song_index)
+
+        # Format the output, making song links clickable
+        result = "<div style='text-align: left;'>"
+        for song, link, sim_score in recommendations:
+            result += f"<b>Song Name:</b> {song}<br>"
+            result += f"<b>Search Link:</b> <a href='https://www.google.com/search?q={link}' target='_blank'>{link}</a><br>"
+            result += f"<b>Lyrics Similarity:</b> {sim_score:.2f}<br><br>"
+        result += "</div>"
+        return result
+    except IndexError:
+        return "Song not found."
+
+# Create Gradio multi-page interface
+with gr.Blocks(css="""
+@media (max-width: 768px) {
+    .gr-container { 
+        width: 100%; 
+        padding: 10px; 
+        box-sizing: border-box; 
+    }
+    .gr-dropdown select {
+    width: 100%;
+    height: 40px; /* Limit height */
+    font-size: 16px;
+    padding: 5px;
+    box-sizing: border-box;
+    }
+    .gr-button { 
+        width: 100%; 
+        font-size: 16px; 
+        margin-top: 10px; 
+    }
+    .gr-html, .gr-row { 
+        width: 100%; 
+        font-size: 16px; 
+        margin: 10px 0; 
+    }
+    h1 { 
+        font-size: 24px; 
+    }
+    p { 
+        font-size: 14px; 
+    }
+    .gr-dropdown::after {
+    content: '';
+    width: 12px;
+    height: 12px;
+    border: solid black;
+    border-width: 0 2px 2px 0;
+    display: inline-block;
+    transform: rotate(45deg);
+    margin-left: 10px;
+   }
+}
+""") as demo:
+
+    gr.Markdown(
+        """
+        <div style="text-align: center; padding: 20px;">
+            <h1 style="color: #1DB954;">Music Recommendation System</h1>
+            <p style="font-size: 18px;">Get the most relevant song recommendations based on lyrics similarity</p>
+        </div>
+        """
+    )
+
+    # Page 1: Select artist
+    with gr.Row():
+        with gr.Column():
+          artist_dropdown = gr.Dropdown(choices=list(artists), label="Select Artist")
+          next_button = gr.Button("Next")
+
+    # Page 2: Select song and get recommendations
+    with gr.Row(visible=False) as song_selection_row:
+        song_dropdown = gr.Dropdown(label="Select Song")
+        recommend_button = gr.Button("Get Recommendations")
+        output = gr.HTML(label="Recommended Similar Songs")
+
+    # Event bindings
+    artist_dropdown.change(get_songs_by_artist, inputs=artist_dropdown, outputs=song_dropdown)
+    next_button.click(lambda: gr.update(visible=True), None, song_selection_row)
+    recommend_button.click(gradio_recommend, inputs=song_dropdown, outputs=output)
+
+if __name__ == "__main__":
+    demo.launch()