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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+spotify_data.csv filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,133 @@
+
+from __future__ import print_function
+
+import os
+import json
+import time
+import sys
+
+
+import pandas as pd
+import numpy as np  
+import seaborn as sn
+import gradio as gr
+
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.pipeline import Pipeline
+from sklearn.model_selection import train_test_split
+from sklearn.neighbors import NearestNeighbors
+
+
+
+import spotipy
+from spotipy.oauth2 import SpotifyClientCredentials
+
+
+sp = spotipy.Spotify(client_credentials_manager=SpotifyClientCredentials())
+
+df = pd.read_csv('spotify_data.csv')
+
+
+df = df.drop(columns=['Unnamed: 0', "Unnamed: 0.1", "pos", "artist_uri", "album_uri", "duration_ms_x", "album_name", "name", "type", "id", "track_href", "analysis_url", "duration_ms_y", "time_signature", "artist_pop", "track_pop"])
+
+
+df.drop_duplicates(subset=['uri'], inplace=True)
+df.reset_index(drop=True, inplace=True)
+df_num = df.select_dtypes(include = ['float64', 'int64'])
+
+
+numeric_cols = ['acousticness', 'danceability', 'energy', 'instrumentalness', 'liveness', 'loudness', 'speechiness', 'tempo', 'valence']
+categorical_cols = ['key', 'mode']
+
+# Create the preprocessing pipeline
+preprocessing_pipeline = ColumnTransformer(
+    transformers=[
+        ('num', StandardScaler(), numeric_cols),
+        ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_cols)
+    ])
+
+# Apply the preprocessing pipeline to your DataFrame
+df_processed = preprocessing_pipeline.fit_transform(df_num)
+
+num_cols_transformed = numeric_cols
+cat_cols_transformed = preprocessing_pipeline.named_transformers_['cat'].get_feature_names_out(categorical_cols)
+
+# Combine the transformed column names
+all_cols_transformed = num_cols_transformed + cat_cols_transformed.tolist()
+
+# Convert the processed NumPy array back to a DataFrame
+df_processed = pd.DataFrame(df_processed, columns=all_cols_transformed)
+
+
+def transform_query(track_uri):
+    audio_features = sp.audio_features(track_uri)[0]
+    track_data = []
+    track_dict = {
+        'acousticness': audio_features['acousticness'],
+        'danceability': audio_features['danceability'],
+        'energy': audio_features['energy'],
+        'instrumentalness': audio_features['instrumentalness'],
+        'liveness': audio_features['liveness'],
+        'loudness': audio_features['loudness'],
+        'speechiness': audio_features['speechiness'],
+        'tempo': audio_features['tempo'],
+        'valence': audio_features['valence'],
+        'key': audio_features['key'],
+        'mode': audio_features['mode']
+    }
+    
+    track_data.append(track_dict)
+    query_data = pd.DataFrame(track_data)
+    return query_data
+
+
+knn_model = NearestNeighbors(n_neighbors=10, algorithm='auto', metric='euclidean')
+knn_model.fit(df_processed) # I'm using all the data for KNN
+
+# Function to find similar songs to the input URI
+def find_similar_songs(track_uri):
+
+    query_data = transform_query(track_uri)
+    
+    # Scale the query data using the same scaler
+    query_data_scaled = preprocessing_pipeline.transform(query_data)
+    query_data_scaled_df = pd.DataFrame(query_data_scaled, columns=all_cols_transformed)
+
+    # Find the most similar songs using the KNN model
+    distances, indices = knn_model.kneighbors(query_data_scaled_df, n_neighbors=10)
+
+    # Retrieve the Artist Name, Song Name, and Track URI of the most similar songs
+    similar_songs = []
+    for index in indices[0]:
+        artist_name = df.iloc[index]['artist_name']
+        song_name = df.iloc[index]['track_name']
+        similar_uri = df.iloc[index]['uri']
+        
+        track_id = similar_uri.split(":")[-1]
+        full_url = f"https://open.spotify.com/track/{track_id}"
+
+        similar_songs.append((artist_name, song_name, full_url))
+        
+    return similar_songs
+
+
+similar_songs = find_similar_songs('https://open.spotify.com/track/6rDaCGqcQB1urhpCrrD599?si=2ac7add2ea054ab2')
+
+
+def format_output(similar_songs):
+    output = []
+    for song in similar_songs:
+        output.append({"Artist Name": song[0], "Song Name": song[1], "Spotify Track URL": song[2]})
+    return pd.DataFrame(output)
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=find_similar_songs,  # Your find_similar_songs function
+    inputs=gr.Textbox(label="Enter Spotify Track URL"),
+    outputs=gr.Dataframe(headers=["Artist Name", "Song Name", "Spotify Track URL"]),
+    live=True
+)
+
+
+iface.launch("share=True")

spotify_data.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:867146aa2b2922a59168d1e79f13c490fe5d4c401551285e980a7c7108f56d2f
+size 33742074