initial commit

app.py

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+# Convert the relevant parts of your notebook into a Python script
+import numpy as np
+import pandas as pd
+from sklearn.metrics.pairwise import cosine_similarity
+import gradio as gr
+
+# Load the data
+df = pd.read_csv('song_dataset.csv')
+
+# Create user-song matrix
+interaction_matrix = df.pivot_table(
+    index='user',
+    columns='song',
+    values='play_count',
+    fill_value=0
+)
+
+def search_songs(query):
+    if not query or len(query) < 2:
+        return gr.update(choices=[])
+    try:
+        matches = song_choices[
+            song_choices['display'].str.lower().str.contains(query.lower(), regex=False)
+        ]['display'].tolist()
+        return gr.update(choices=matches[:10])
+    except Exception as e:
+        print(f"Search error: {e}")
+        return gr.update(choices=[])
+
+def add_to_selection(new_song, current_selections):
+    if not current_selections:
+        current_selections = []
+    
+    if new_song and new_song not in current_selections and len(current_selections) < 5:
+        current_selections.append(new_song)
+    
+    return gr.update(choices=current_selections, value=current_selections)
+
+def make_recommendations(selected_songs, n_recommendations=5):
+    if not selected_songs:
+        return "Please select at least one song to get recommendations."
+        
+    temp_user_profile = pd.Series(0, index=interaction_matrix.columns)
+    for song in selected_songs:
+        song_id = song_choices[song_choices['display'] == song]['song'].iloc[0]
+        temp_user_profile[song_id] = 1
+    
+    user_sim = cosine_similarity([temp_user_profile], interaction_matrix)[0]
+    
+    unheard_songs = list(set(interaction_matrix.columns) - 
+                        set([song_choices[song_choices['display'] == song]['song'].iloc[0] 
+                             for song in selected_songs]))
+    
+    pred_ratings = []
+    for song in unheard_songs:
+        pred = np.sum(user_sim * interaction_matrix[song]) / np.sum(np.abs(user_sim))
+        pred_ratings.append((song, pred))
+    
+    ratings = np.array([r[1] for r in pred_ratings])
+    if len(ratings) > 0:
+        min_rating, max_rating = ratings.min(), ratings.max()
+        if max_rating > min_rating:
+            normalized_ratings = 1 + 4 * (ratings - min_rating) / (max_rating - min_rating)
+        else:
+            normalized_ratings = [3.0] * len(ratings)
+    else:
+        return "No recommendations found for the selected songs."
+    
+    recommendations = list(zip([r[0] for r in pred_ratings], normalized_ratings))
+    recommendations = sorted(recommendations, key=lambda x: x[1], reverse=True)[:n_recommendations]
+    
+    output = ""
+    for song_id, rating in recommendations:
+        song_details = df[df['song'] == song_id].iloc[0]
+        output += f"Title: {song_details['title']}\n"
+        output += f"Artist: {song_details['artist_name']}\n"
+        output += f"Year: {song_details['year']}\n"
+        output += f"Rating: {rating:.2f}/5.00\n"
+        output += "-" * 50 + "\n"
+    
+    return output
+
+# Create song choices for dropdown
+song_choices = df[['song', 'title', 'artist_name']].drop_duplicates()
+song_choices['display'] = song_choices['title'] + " - " + song_choices['artist_name']
+song_list = song_choices['display'].tolist()
+
+# Create Gradio interface
+with gr.Blocks(title="Wanna Be Spotify") as iface:
+    gr.Markdown("# 🎵 Wanna Be Spotify")
+    gr.Markdown("Search and select up to 5 songs you've enjoyed to get personalized recommendations!")
+    
+    with gr.Row():
+        search_box = gr.Textbox(
+            label="Search for songs",
+            placeholder="Type song or artist name...",
+            show_label=True
+        )
+    
+    with gr.Row():
+        search_results = gr.Radio(
+            choices=[],
+            label="Search Results",
+            interactive=True,
+            show_label=True
+        )
+    
+    with gr.Row():
+        selected_songs = gr.Dropdown(
+            choices=[],
+            label="Selected Songs",
+            interactive=True,
+            multiselect=True,
+            max_choices=5,
+            show_label=True
+        )
+    
+    with gr.Row():
+        recommendations = gr.Textbox(
+            label="Recommendations",
+            interactive=False,
+            lines=10,
+            show_label=True
+        )
+    
+    submit_btn = gr.Button("Get Recommendations")
+
+    search_box.change(
+        fn=search_songs,
+        inputs=[search_box],
+        outputs=[search_results]
+    )
+    
+    search_results.change(
+        fn=add_to_selection,
+        inputs=[search_results, selected_songs],
+        outputs=[selected_songs]
+    )
+    
+    submit_btn.click(
+        fn=make_recommendations,
+        inputs=[selected_songs],
+        outputs=[recommendations]
+    )
+
+# Launch the interface
+iface.launch()