Create SpotifyProject.py

ProjectSolution/SpotifyProject.py

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+#import Spotipy
+import ast
+from typing import List
+from os import listdir
+import spotipy
+import requests
+import pandas as pd
+
+def get_streamings(path: str = 'AData') -> List[dict]:
+
+    files = ['AData/' + x for x in listdir(path)
+             if x.split('.')[0][:-1] == 'StreamingHistory']
+
+    all_streamings = []
+    totalTimeByTrack = {}
+
+    for file in files: 
+        with open(file, 'r', encoding='UTF-8') as f:
+            new_streamings = ast.literal_eval(f.read())
+            for streaming in new_streamings:
+                if streaming['trackName'] in totalTimeByTrack:
+                    totalTimeByTrack[streaming['trackName']] += round(streaming['msPlayed']/60000)
+                else:
+                    totalTimeByTrack[streaming['trackName']] = round(streaming['msPlayed']/60000)
+                all_streamings.append(streaming)
+    return totalTimeByTrack
+
+
+def get_id(track_name: str, token: str) -> str:
+    headers = {
+    'Accept': 'application/json',
+    'Content-Type': 'application/json',
+    'Authorization': f'Bearer ' + token,
+    }
+    params = [
+    ('q', track_name),
+    ('type', 'track'),
+    ]
+    try:
+        response = requests.get('https://api.spotify.com/v1/search', 
+                    headers = headers, params = params, timeout = 5)
+        json = response.json()
+        first_result = json['tracks']['items'][0]
+        track_id = first_result['id']
+        return track_id
+    except:
+        return None
+
+
+def get_features(track_id: str, token: str) -> dict:
+    sp = spotipy.Spotify(auth=token)
+    try:
+        features = sp.audio_features([track_id])
+        return features[0]
+    except:
+        return None  
+
+def trim_features(features: dict) -> dict:
+    # TODO: Only grab certain features
+    pass
+
+
+#get_streamings()
+
+username = 'qut9zcs5mbfdmc5cykwudcjtj'
+client_id = '248c415c42004414acfa6cc8fe305bc3'
+client_secret = 'acb2bbbec3184747b5851fa482419ade'
+redirect_url = 'http://localhost:7777/callback'
+scope = 'user-read-recently-played'
+
+token = spotipy.util.prompt_for_user_token(username=username,
+                                   scope=scope,
+                                   client_id=client_id,
+                                   client_secret=client_secret,
+                                   redirect_uri=redirect_url)
+
+streamings = get_streamings()
+sortedListByStreamingTimes = sorted([x for x in streamings.values()])
+topX = int(len(sortedListByStreamingTimes)*.5)
+dataset = pd.DataFrame()
+
+featureFinalValues = {}
+for streamingName,timePlayed in streamings.items(): 
+    if timePlayed < sortedListByStreamingTimes[topX]:
+            continue
+
+    ID = get_id(streamingName,token)
+
+    # TODO: Make function to extract features we care about
+    features = get_features(ID,token)
+
+    # TODO: Grab genere
+
+    # get averages for each value in feature_avgs
+    for key,value in features.items():
+
+        if key in featureFinalValues:
+            featureFinalValues[key][0] += value*timePlayed
+            featureFinalValues[key][1] += timePlayed
+        else:
+            featureFinalValues[key] = [value*timePlayed,timePlayed]
+
+
+    # Associtae indexs
+    bigFive = [50,50,50,50,50]
+
+
+
+
+#print("Streamings:",streamings)
+#print("\nID:",ID)
+#print("\nfeatures",features)
+
+# ID 1
+# Dictionary where ID is the key
+# the value will be a tuple
+# two dictionaries
+# Dict1 will have all a person songs that are in the top 75% of their listening.
+# Keys for this dictioanry will be the song name and value will be a tuple of time listen to in total and the artist name
+
+
+# ITF dictionary 2 od the tuple will be the quetion number ids and participant responses
+