thanks gpt
This commit is contained in:
Mathieu Moran
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import spotipy
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from spotipy.oauth2 import SpotifyOAuth
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# Set your Spotify API credentials
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client_id = ''
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client_secret = ''
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redirect_uri = 'http://localhost:8888/callback' # Provide a valid redirect URI
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# Create an authorization flow object
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auth_manager = SpotifyOAuth(client_id=client_id, client_secret=client_secret, redirect_uri=redirect_uri)
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# Get the authorization URL
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auth_url = auth_manager.get_authorize_url()
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# Print the authorization URL and follow it in your browser
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print(f"Please visit this URL to authorize the application: {auth_url}")
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# Once authorized, you will be redirected to the specified redirect URI, and the authorization code will be appended to the URL.
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# Extract the authorization code from the redirected URL and provide it to the auth_manager to obtain the access token.
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authorization_code = input("Enter the authorization code: ")
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auth_manager.get_access_token(authorization_code)
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# Retrieve the access token
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access_token = auth_manager.get_access_token()['access_token']
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print(f"Access Token: {access_token}")
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+102
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from pymongo import MongoClient
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from collections import Counter
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from datetime import datetime
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import requests
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client = MongoClient('MANGODBSRV')
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db = client['cluster0']
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collection = db['spotify']
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# Retrieve all the song documents from the collection
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songs = collection.find()
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# Initialize counters for song counts per month
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song_counts = {}
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# Group songs by month and calculate statistics for each month
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for song in songs:
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song_name = song['song_name'] # Replace 'name' with the correct field name for song name
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artist_name = song['artist'] # Replace 'artist' with the correct field name for artist name
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song_id = song['song_id'] # Replace 'song_id' with the correct field name for the song ID
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play_date = song['date'] # Replace 'date' with the correct field name for the date
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# Parse the play date as a datetime object
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play_date = datetime.strptime(play_date, "%B %d, %Y at %I:%M%p")
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# Extract the month and year from the play date
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month = play_date.strftime("%B %Y")
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# Initialize the counter for the month if not already present
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if month not in song_counts:
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song_counts[month] = Counter()
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# Increment the song count for the month
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song_counts[month][(song_name, artist_name, song_id)] += 1
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# Calculate the total plays for each month
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total_plays = {month: sum(songs.values()) for month, songs in song_counts.items()}
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# Spotify API endpoint for retrieving track information
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endpointArtists = 'https://api.spotify.com/v1/artists'
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endpointTracks = 'https://api.spotify.com/v1/tracks'
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# Spotify API access token
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access_token = 'ACCESSTOKENSPOTIFY' # Replace with your actual access token
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# Function to retrieve the genre for a given song ID
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def get_song_info(song_id):
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headers = {'Authorization': f'Bearer {access_token}'}
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url = f"{endpointTracks}/{song_id}"
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response = requests.get(url, headers=headers)
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data = response.json()
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if 'artists' in data and data['artists']:
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artist = data['artists'][0]
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artist_id = artist['id']
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if 'genres' in artist and artist['genres']:
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genre = artist['genres'][0]
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else:
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genre = 'Unknown Genre'
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else:
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artist_id = 'Unknown Artist'
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genre = 'Unknown Genre'
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return genre, artist_id
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# Function to retrieve the genre for a given song ID
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def get_song_genre(artist_id):
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headers = {'Authorization': f'Bearer {access_token}'}
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url = f"{endpointArtists}/{artist_id}"
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response = requests.get(url, headers=headers)
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data = response.json()
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if 'genres' in data and data['genres']:
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genres = data['genres'][0]
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return genres
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return 'Unknown Genre'
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# Print the statistics for each month
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print("Song Statistics by Month:")
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for month, songs in song_counts.items():
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print(f"Month: {month}")
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print(f"Total Plays: {total_plays[month]}")
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most_played_song = songs.most_common(1)[0]
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most_played_song_name = most_played_song[0][0]
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most_played_song_artist = most_played_song[0][1]
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most_played_song_id = most_played_song[0][2]
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genre = get_song_genre(get_song_info(most_played_song_id)[1])
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print(f"Most Played Song: {most_played_song_name} by {most_played_song_artist} ({most_played_song[1]} plays)")
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print(f"Most Played Song Genre: {genre}")
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print() # Add a new line for better readability
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# Calculate the total plays of all time
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total_plays_all_time = sum(total_plays.values())
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# Print the total plays of all time
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print(f"Total Plays of All Time: {total_plays_all_time}")
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# Close the MongoDB connection
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client.close()
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@@ -0,0 +1,76 @@
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import os
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import gspread
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from google.auth.transport.requests import Request
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from googleapiclient.discovery import build
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from google.oauth2 import service_account
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from pymongo import MongoClient
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# Replace 'credentials.json' with the path to your downloaded credentials JSON file
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credentials = service_account.Credentials.from_service_account_file('credentials.json', scopes=['https://www.googleapis.com/auth/drive'])
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# Create a Drive API service client
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drive_service = build('drive', 'v3', credentials=credentials)
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def get_google_sheets_in_folder(folder_id):
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results = drive_service.files().list(
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q=f"'{folder_id}' in parents and mimeType='application/vnd.google-apps.spreadsheet'",
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fields='files(id, name)'
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).execute()
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sheets = results.get('files', [])
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return sheets
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# Replace 'folder_id' with the actual ID of the folder you want to retrieve sheets from
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folder_id = '1TaG1w1UemFNFzOENX34TjHu-4YWkGRGw'
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# Call the function to get the Google Sheets in the folder
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sheets = get_google_sheets_in_folder(folder_id)
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# Check if the credentials are expired and refresh them if necessary
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if credentials.expired and credentials.refresh_token:
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credentials.refresh(Request())
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# Create a gspread client using the refreshed credentials
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gc = gspread.authorize(credentials)
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# Connect to your MongoDB server
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client = MongoClient('MONGODBSRV')
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# Replace 'mydatabase' and 'mycollection' with your actual database and collection names
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db = client['cluster0']
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collection = db['spotify']
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# Loop through the sheets
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for sheet in sheets:
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sheet_id = sheet['id']
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# Open the Google Sheet by its ID
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sheet = gc.open_by_key(sheet_id)
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# Access a specific worksheet (e.g., the first sheet)
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worksheet = sheet.get_worksheet(0)
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# Extract the data from the worksheet
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data = worksheet.get_all_values()
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# Convert the data into a list of dictionaries
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headers = data[0]
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rows = data[1:]
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rows_dict = []
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# Map the data to the desired field names
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field_names = ['date', 'song_name', 'artist', 'song_id', 'song_url']
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for row in rows:
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row_dict = {}
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for i, field_name in enumerate(field_names):
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row_dict[field_name] = row[i]
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rows_dict.append(row_dict)
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print("-----------------")
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print(row_dict)
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print("-----------------")
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# Insert the data into MongoDB
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collection.insert_many(rows_dict)
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client.close()
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