Merge branch 'develop' of https://github.com/JeanBaptiste-dlb/Skiptify …

…into develop

backend/src/app.py

@@ -1,7 +1,11 @@
 from flask import Flask, redirect, url_for
 from backend.src.spotify_api.api_interface import SPOTIFY_API_INTERFACE
+import pandas as pd
+import numpy as np
+from flask_cors import CORS
 
 app = Flask(__name__)
+CORS(app)
 app.debug = True
 
 sp = SPOTIFY_API_INTERFACE()
@@ -15,18 +19,12 @@ def index():
 
 @app.route('/current_info')
 def current_info():
-    data = sp.get_current_song()
-    dict = data.to_dict()
-    return {'data': dict}, 200  # return data and 200 OK code
-
-@app.route('/skipped_songs')
-def skipped_song():
-    data = sp.get_current_song()
-    dict = data.to_dict()
-    return {'data': dict}, 200  # return data and 200 OK code
+    data = sp.get_metadata_current()
+    if data is not None:
+        dict = data.to_dict()
+    else:
+        dict = pd.Series()
+    return dict, 200  # return data and 200 OK code
 
 if __name__ == '__main__':
     app.run(debug=True)
-
-
-

backend/src/spotify_api/api_interface.py

@@ -6,6 +6,7 @@
 import spotipy.util as util
 from pathlib import Path
 from datetime import datetime
+from sklearn.metrics import pairwise_distances
 
 
 class SPOTIFY_API_INTERFACE:
@@ -151,11 +152,68 @@ def get_features(self, track_id) -> pd.Series:
         features_results = self.sp.audio_features([track_id])
         return pd.Series(features_results[0])
 
+    def get_features_to_list(self, track_id):
+        features_results = self.sp.audio_features([track_id])
+        json_features = json.dumps(features_results)
+        features_data = json.loads(json_features)
+
+        # Convert features dictionary to a list
+        features_list = list(features_data[0].values())
+        return features_list
+
     def get_metadata(self, track_id):
         self.sp._update_scope("user-read-currently-playing")
         name = self.sp.currently_playing()["item"]["name"]
         album_title = self.sp.currently_playing()["item"]["album"][""]
         metadata = {"name": name}
         return metadata
 
+    def calc_dist(self, feature1, feature2):
+        dss = np.vstack((feature1, feature2))
+        distance_matrix = pairwise_distances(dss)
+        return distance_matrix[0][1]
+
+    def is_song_skipped(self, track_id):
+        """Check if the given is skipped based on distance
 
+        Args:
+            track_id (str): id of the song to check if it should be skipped
+        
+        Return:
+            True: skip
+            False: skip
+        """
+        # self.sp._update_scope("user-read-currently-playing")
+        # get features of the skipped songs
+        df_skipped = pd.read_csv('../../../data/player_data/skipped.csv', usecols=[i for i in range(11)])
+        normalized_d_skipped = (df_skipped - df_skipped.mean()) / (df_skipped.max() - df_skipped.min())
+
+        # get features of the non-skipped songs
+        df_nonskipped = pd.read_csv('../../../data/player_data/non_skipped.csv', usecols=[i for i in range(11)])
+        normalized_d_nonskipped = (df_nonskipped - df_nonskipped.mean()) / (df_nonskipped.max() - df_nonskipped.min())
+
+        # get features of the song to check the similarity distance
+        feature1 = self.get_features_to_list(track_id)
+        feature1 = feature1[:11]
+
+        # calc distance to the skipped
+        distances_skipped = []
+        for i in range(len(normalized_d_skipped)):
+            feature2 = normalized_d_skipped.iloc[i,:]
+            dist_tmp = self.calc_dist(feature1, feature2)
+            distances_skipped.append(dist_tmp)
+
+        # calc distance to the skipped
+        distances_nonskipped = []
+        for i in range(len(normalized_d_nonskipped)):
+            feature3 = normalized_d_nonskipped.iloc[i,:]
+            dist_tmp = self.calc_dist(feature1, feature3)
+            distances_nonskipped.append(dist_tmp)
+
+        # compare distances
+        dist_skipped = np.array(distances_skipped).mean()
+        dist_nonskipped = np.array(distances_skipped).mean()
+        if dist_skipped > dist_nonskipped:
+            return False
+        else:
+            return True

notebooks/skiptify_get_currently_song.ipynb

@@ -0,0 +1,87 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "import spotipy\n",
+    "\n",
+    "from sklearn.metrics import pairwise_distances\n",
+    "from my_token import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scope = 'user-read-currently-playing'\n",
+    "# scope = 'user-modify-playback-state'\n",
+    "# scope = 'user-read-recently-played'\n",
+    "\n",
+    "# To connect succesfully you need to provide your own Spotify Credentials\n",
+    "# You can do this signing up in https://developer.spotify.com/ and creating a new app.\n",
+    "token = spotipy.util.prompt_for_user_token(\n",
+    "    USERNAME, scope, client_id=SPOTIPY_CLIENT_ID, client_secret=SPOTIPY_CLIENT_SECRET, redirect_uri=SPOTIPY_REDIRECT_URI)\n",
+    "\n",
+    "sp = spotipy.Spotify(auth=token)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'3c8iiZGfEammKJuWTErE5x'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# method currently playing return an actual song on Spotify\n",
+    "current_song = sp.currently_playing()\n",
+    "current_song['item']['id']"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.13 ('base')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "c6e4e9f98eb68ad3b7c296f83d20e6de614cb42e90992a65aa266555a3137d0d"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}