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main.py

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+import requests
+from urllib.parse import urlencode
+from utils import *
+import os
+
+# Constants
+CLIENT_ID = os.getenv("CLIENT_ID")
+CLIENT_SECRET = os.getenv("CLIENT_SECRET")
+REDIRECT_URI = "https://huggingface.co/spaces/Add1E/SpotifyHitPrediction"
+AUTH_URL = "https://accounts.spotify.com/authorize"
+TOKEN_URL = "https://accounts.spotify.com/api/token"
+
+
+def get_track_audio_features(track_id, access_token):
+    endpoint = f"https://api.spotify.com/v1/audio-features/{track_id}"
+    headers = {
+        'Authorization': f'Bearer {access_token}',
+    }
+    response = requests.get(endpoint, headers=headers)
+    return response.json()
+
+
+def get_track_information(track_id, access_token):
+    endpoint = f"https://api.spotify.com/v1/tracks/{track_id}"
+    headers = {
+        'Authorization': f'Bearer {access_token}',
+    }
+    response = requests.get(endpoint, headers=headers)
+    return response.json()
+
+
+def extract_track_id_from_url(url):
+    # For a Spotify URL
+    if 'open.spotify.com' in url and '/track/' in url:
+        return url.split('track/')[1]
+    # For a Spotify URI
+    elif 'spotify:track:' in url:
+        return url.split(':')[-1]
+    else:
+        return None  # Or handle invalid URL/URI appropriately
+
+
+def app():
+
+    # Step 1: User Authorization
+    if st.session_state.get('code') is None:
+        auth_params = {
+            'client_id': CLIENT_ID,
+            'response_type': 'code',
+            'redirect_uri': REDIRECT_URI,
+            'scope': 'user-read-private',  # Modify as per your required scopes
+        }
+        st.code(st.session_state)
+        st.write('Please log in to Spotify')
+        st.markdown(f"[Log In]({AUTH_URL}?{urlencode(auth_params)})", unsafe_allow_html=True)
+    else:#
+        # Step 2: Request Access Token
+        if 'access_token' not in st.session_state or (st.session_state['access_token'] is None):
+            auth_code = st.session_state['code']
+
+            token_data = {
+                'grant_type': 'authorization_code',
+                'code': auth_code,
+                'redirect_uri': REDIRECT_URI,
+                'client_id': CLIENT_ID,
+                'client_secret': CLIENT_SECRET,
+            }
+            token_r = requests.post(TOKEN_URL, data=token_data)
+            token_response = token_r.json()
+            st.session_state['access_token'] = token_response.get('access_token')
+
+        if 'access_token' in st.session_state:
+            st.write("Enter a Spotify Track URL or URI to get its audio features.")
+            track_input = st.text_input("Spotify Track URL/URI", "")
+            if st.button("Get Audio Features"):
+                track_id = extract_track_id_from_url(track_input)
+                if track_id:
+                    audio_features = get_track_audio_features(track_id, st.session_state['access_token'])
+                    features = [None] * 9
+                    features[0] = audio_features["tempo"]   # Beats per minute
+                    features[1] = audio_features["energy"]
+                    features[2] = audio_features["danceability"]
+                    features[3] = audio_features["loudness"]   # Loudness in dB
+                    features[4] = audio_features["liveness"]
+                    features[5] = audio_features["valence"]
+                    features[6] = audio_features["duration_ms"] / 1000   # Umrechnung von Millisekunden in Sekunden
+                    features[7] = audio_features["acousticness"]
+                    features[8] = audio_features["speechiness"]
+                    feature_display = {
+                        "tempo": features[0],
+                        "energy" : features[1],
+                        "danceability":features[2],
+                        "loudness":features[3],
+                        "liveness":features[4],
+                        "valence":features[5],
+                        "duration_in_s":features[6],
+                        "acousticness":features[7],
+                        "speechiness":features[8]
+                    }
+                    st.write(feature_display)
+                    predictions = predict_popularity(features)
+                    st.write(f"Random Forest Prediction: {predictions[0]}")
+                    st.write(f"Regression Prediction: {predictions[1]}")
+
+                else:
+                    st.error("Please enter a valid Spotify Track URL or URI.")
+                if track_id:
+                    track_info = get_track_information(track_id, st.session_state['access_token'])
+                    if track_info:
+                        st.write(f"Popularität: {track_info.get('popularity', 'Nicht verfügbar')}")
+                    else:
+                        st.write("Track-Informationen konnten nicht abgerufen werden.")
+                else:
+                    st.write("Bitte geben Sie eine gültige Track-ID ein.")
+
+if 'code' not in st.session_state:
+    query_params = st.experimental_get_query_params()
+    st.session_state['code'] = query_params.get('code', [None])[0]
+
+app()

top50.csv

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+"","Track.Name","Artist.Name","Genre","Beats.Per.Minute","Energy","Danceability","Loudness..dB..","Liveness","Valence.","Length.","Acousticness..","Speechiness.","Popularity"
+"1","Se�orita","Shawn Mendes","canadian pop",117,55,76,-6,8,75,191,4,3,79
+"2","China","Anuel AA","reggaeton flow",105,81,79,-4,8,61,302,8,9,92
+"3","boyfriend (with Social House)","Ariana Grande","dance pop",190,80,40,-4,16,70,186,12,46,85
+"4","Beautiful People (feat. Khalid)","Ed Sheeran","pop",93,65,64,-8,8,55,198,12,19,86
+"5","Goodbyes (Feat. Young Thug)","Post Malone","dfw rap",150,65,58,-4,11,18,175,45,7,94
+"6","I Don't Care (with Justin Bieber)","Ed Sheeran","pop",102,68,80,-5,9,84,220,9,4,84
+"7","Ransom","Lil Tecca","trap music",180,64,75,-6,7,23,131,2,29,92
+"8","How Do You Sleep?","Sam Smith","pop",111,68,48,-5,8,35,202,15,9,90
+"9","Old Town Road - Remix","Lil Nas X","country rap",136,62,88,-6,11,64,157,5,10,87
+"10","bad guy","Billie Eilish","electropop",135,43,70,-11,10,56,194,33,38,95
+"11","Callaita","Bad Bunny","reggaeton",176,62,61,-5,24,24,251,60,31,93
+"12","Loco Contigo (feat. J. Balvin & Tyga)","DJ Snake","dance pop",96,71,82,-4,15,38,185,28,7,86
+"13","Someone You Loved","Lewis Capaldi","pop",110,41,50,-6,11,45,182,75,3,88
+"14","Otro Trago - Remix","Sech","panamanian pop",176,79,73,-2,6,76,288,7,20,87
+"15","Money In The Grave (Drake ft. Rick Ross)","Drake","canadian hip hop",101,50,83,-4,12,10,205,10,5,92
+"16","No Guidance (feat. Drake)","Chris Brown","dance pop",93,45,70,-7,16,14,261,12,15,82
+"17","LA CANCI�N","J Balvin","latin",176,65,75,-6,11,43,243,15,32,90
+"18","Sunflower - Spider-Man: Into the Spider-Verse","Post Malone","dfw rap",90,48,76,-6,7,91,158,56,5,91
+"19","Lalala","Y2K","canadian hip hop",130,39,84,-8,14,50,161,18,8,88
+"20","Truth Hurts","Lizzo","escape room",158,62,72,-3,12,41,173,11,11,91
+"21","Piece Of Your Heart","MEDUZA","pop house",124,74,68,-7,7,63,153,4,3,91
+"22","Panini","Lil Nas X","country rap",154,59,70,-6,12,48,115,34,8,91
+"23","No Me Conoce - Remix","Jhay Cortez","reggaeton flow",92,79,81,-4,9,58,309,14,7,83
+"24","Soltera - Remix","Lunay","latin",92,78,80,-4,44,80,266,36,4,91
+"25","bad guy (with Justin Bieber)","Billie Eilish","electropop",135,45,67,-11,12,68,195,25,30,89
+"26","If I Can't Have You","Shawn Mendes","canadian pop",124,82,69,-4,13,87,191,49,6,70
+"27","Dance Monkey","Tones and I","australian pop",98,59,82,-6,18,54,210,69,10,83
+"28","It's You","Ali Gatie","canadian hip hop",96,46,73,-7,19,40,213,37,3,89
+"29","Con Calma","Daddy Yankee","latin",94,86,74,-3,6,66,193,11,6,91
+"30","QUE PRETENDES","J Balvin","latin",93,79,64,-4,36,94,222,3,25,89
+"31","Takeaway","The Chainsmokers","edm",85,51,29,-8,10,36,210,12,4,84
+"32","7 rings","Ariana Grande","dance pop",140,32,78,-11,9,33,179,59,33,89
+"33","0.958333333333333","Maluma","reggaeton",96,71,78,-5,9,68,176,22,28,89
+"34","The London (feat. J. Cole & Travis Scott)","Young Thug","atl hip hop",98,59,80,-7,13,18,200,2,15,89
+"35","Never Really Over","Katy Perry","dance pop",100,88,77,-5,32,39,224,19,6,89
+"36","Summer Days (feat. Macklemore & Patrick Stump of Fall Out Boy)","Martin Garrix","big room",114,72,66,-7,14,32,164,18,6,89
+"37","Otro Trago","Sech","panamanian pop",176,70,75,-5,11,62,226,14,34,91
+"38","Antisocial (with Travis Scott)","Ed Sheeran","pop",152,82,72,-5,36,91,162,13,5,87
+"39","Sucker","Jonas Brothers","boy band",138,73,84,-5,11,95,181,4,6,80
+"40","fuck, i'm lonely (with Anne-Marie) - from �13 Reasons Why: Season 3�","Lauv","dance pop",95,56,81,-6,6,68,199,48,7,78
+"41","Higher Love","Kygo","edm",104,68,69,-7,10,40,228,2,3,88
+"42","You Need To Calm Down","Taylor Swift","dance pop",85,68,77,-6,7,73,171,1,5,90
+"43","Shallow","Lady Gaga","dance pop",96,39,57,-6,23,32,216,37,3,87
+"44","Talk","Khalid","pop",136,40,90,-9,6,35,198,5,13,84
+"45","Con Altura","ROSAL�A","r&b en espanol",98,69,88,-4,5,75,162,39,12,88
+"46","One Thing Right","Marshmello","brostep",88,62,66,-2,58,44,182,7,5,88
+"47","Te Robar�","Nicky Jam","latin",176,75,67,-4,8,80,202,24,6,88
+"48","Happier","Marshmello","brostep",100,79,69,-3,17,67,214,19,5,88
+"49","Call You Mine","The Chainsmokers","edm",104,70,59,-6,41,50,218,23,3,88
+"50","Cross Me (feat. Chance the Rapper & PnB Rock)","Ed Sheeran","pop",95,79,75,-6,7,61,206,21,12,82

utils.py

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+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.ensemble import RandomForestRegressor
+import pandas as pd
+from tqdm.auto import tqdm
+import streamlit as st
+tqdm.pandas()
+
+
+def predict_popularity(features):
+    predictions = [None] * 2
+    predictions[0], predictions[1] = rf_model.predict([features]), model.predict([features])
+    return predictions
+
+
+
+
+data = pd.read_csv('top50.csv', encoding='ISO-8859-1')
+print(data.head())
+
+# Let's also describe the data to get a sense of the distributions
+print(data.describe())
+# Selecting the features and the target variable
+X = data.drop(['Unnamed: 0', 'Track.Name', 'Artist.Name', 'Genre', 'Popularity'], axis=1)
+y = data['Popularity']
+
+# Splitting the data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Initializing the Linear Regression model
+model = LinearRegression()
+
+# Fitting the model
+model.fit(X_train, y_train)
+
+# Making predictions
+y_pred = model.predict(X_test)
+
+# Calculating the performance metrics
+mse = mean_squared_error(y_test, y_pred)
+r2 = r2_score(y_test, y_pred)
+# Initialize the Random Forest Regressor
+rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
+
+# Fitting the model
+rf_model.fit(X_train, y_train)
+
+# Making predictions
+rf_pred = rf_model.predict(X_test)
+
+# Calculating the performance metrics
+rf_mse = mean_squared_error(y_test, rf_pred)
+rf_r2 = r2_score(y_test, rf_pred)
+
+
+# Feature importances
+feature_importances = rf_model.feature_importances_
+
+# Create a pandas series with feature importances
+importances = pd.Series(feature_importances, index=X.columns)
+
+# Sort the feature importances in descending order
+sorted_importances = importances.sort_values(ascending=False)