fix lfs

.gitattributes

@@ -0,0 +1,3 @@
+*.gif filter=lfs diff=lfs merge=lfs -text
+images/*.gif filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text

.github/workflows/blank.yml

@@ -17,4 +17,4 @@ jobs:
       - name: Push to hub
         env:
           HF_TOKEN: ${{ secrets.HG }}
-        run: git push https://diivien:[email protected]/spaces/diivien/Music-Popularity-Prediction main
+        run: git push --force https://diivien:[email protected]/spaces/diivien/Music-Popularity-Prediction main

.github/workflows/main.yml

@@ -0,0 +1,16 @@
+name: Check file size
+on:               # or directly `on: [push]` to run the action on every push on any branch
+  pull_request:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check large files
+        uses: ActionsDesk/[email protected]
+        with:
+          filesizelimit: 10485760 # this is 10MB so we can sync to HF Spaces

.gitignore

@@ -2,4 +2,5 @@
 .venv/
 .ipynb_checkpoints/
 catboost_info/
-my_study.db
+my_study.db
+flagged/

Model Building.ipynb

@@ -1138,6 +1138,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
+   "id": "077ca8bd",
    "metadata": {},
    "outputs": [
     {
@@ -1390,7 +1391,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "id": "5bcee11c",
    "metadata": {},
    "outputs": [
@@ -1398,7 +1399,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "c:\\Users\\zheng\\Documents\\ds music project\\.venv\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "c:\\Users\\zheng\\Documents\\MMU SHIT\\YEAR2\\SEM2\\DATA SCIENCE FUNDAMENTALS\\Music-Popularity-Prediction\\.venv\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
       "  from .autonotebook import tqdm as notebook_tqdm\n"
      ]
     }
@@ -1411,7 +1412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "id": "99edd49f",
    "metadata": {},
    "outputs": [],
@@ -1433,7 +1434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 14,
    "id": "7305f2d1",
    "metadata": {},
    "outputs": [],
@@ -5211,7 +5212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 15,
    "id": "a2e122ed",
    "metadata": {},
    "outputs": [
@@ -6315,7 +6316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 17,
    "id": "addeaf5e",
    "metadata": {},
    "outputs": [
@@ -6355,6 +6356,8 @@
     }
    ],
    "source": [
+    "from catboost import CatBoostClassifier\n",
+    "\n",
     "pipe_CatBoost = imbpipeline(\n",
     "    [\n",
     "\n",
@@ -6684,6 +6687,49 @@
    "source": [
     "plot_feature_importance(pipe_CatBoost,X,y)"
    ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "e2780c6e",
+   "metadata": {},
+   "source": [
+    "### Saving the best model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "b88f2d45",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['final_model.pkl']"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import joblib\n",
+    "pipe_final_CatBoost = imbpipeline(\n",
+    "    [\n",
+    "\n",
+    "     ('preprocessor',preprocessor),\n",
+    "              ('over',smoteNC2),\n",
+    "\n",
+    "     ('classifier',CatBoostClassifier(thread_count=-1,silent=True,task_type=\"GPU\"))]\n",
+    "    )\n",
+    "pipe_final_CatBoost.set_params(**cat_best_params)\n",
+    "\n",
+    "pipe_final_CatBoost.fit(X,y)\n",
+    "\n",
+    "joblib.dump(pipe_final_CatBoost,'final_model.pkl')\n"
+   ]
   }
  ],
  "metadata": {

README.md

@@ -1,3 +1,15 @@
+---
+title: Music Popularity Prediction
+emoji: 🚀
+colorFrom: purple
+colorTo: blue
+sdk: gradio
+sdk_version: 3.28.0
+app_file: app.py
+pinned: false
+python_version: 3.10.6
+---
+
 # Music Popularity Prediction
 
 This repository contains a data science project that aims to predict the popularity of music using machine learning techniques.

app.py

@@ -0,0 +1,187 @@
+import gradio as gr
+import pandas as pd
+import joblib
+import os
+import spotipy
+from spotipy.oauth2 import SpotifyClientCredentials
+from Levenshtein import distance
+
+final_model = joblib.load('final_model.pkl')
+print(final_model)
+# Set up authentication with the Spotify API
+sp = spotipy.Spotify(auth_manager=SpotifyClientCredentials(client_id=os.environ['SPOT_API'], client_secret=os.environ['SPOT_SECRET']))
+
+genre_list = ['acoustic', 'afrobeat', 'alt-rock', 'alternative', 'ambient',
+       'anime', 'black-metal', 'bluegrass', 'blues', 'brazil',
+       'breakbeat', 'british', 'cantopop', 'chicago-house', 'children',
+       'chill', 'classical', 'club', 'comedy', 'country', 'dance',
+       'dancehall', 'death-metal', 'deep-house', 'detroit-techno',
+       'disco', 'disney', 'drum-and-bass', 'dub', 'dubstep', 'edm',
+       'electro', 'electronic', 'emo', 'folk', 'forro', 'french', 'funk',
+       'garage', 'german', 'gospel', 'goth', 'grindcore', 'groove',
+       'grunge', 'guitar', 'happy', 'hard-rock', 'hardcore', 'hardstyle',
+       'heavy-metal', 'hip-hop', 'honky-tonk', 'house', 'idm', 'indian',
+       'indie-pop', 'indie', 'industrial', 'iranian', 'j-dance', 'j-idol',
+       'j-pop', 'j-rock', 'jazz', 'k-pop', 'kids', 'latin', 'latino',
+       'malay', 'mandopop', 'metal', 'metalcore', 'minimal-techno', 'mpb',
+       'new-age', 'opera', 'pagode', 'party', 'piano', 'pop-film', 'pop',
+       'power-pop', 'progressive-house', 'psych-rock', 'punk-rock',
+       'punk', 'r-n-b', 'reggae', 'reggaeton', 'rock-n-roll', 'rock',
+       'rockabilly', 'romance', 'sad', 'salsa', 'samba', 'sertanejo',
+       'show-tunes', 'singer-songwriter', 'ska', 'sleep', 'soul',
+       'spanish', 'study', 'swedish', 'synth-pop', 'tango', 'techno',
+       'trance', 'trip-hop', 'turkish', 'world-music']
+
+def get_track_genre(track_id):
+    track = sp.track(track_id)
+    artist = sp.artist(track['artists'][0]['external_urls']['spotify'])
+
+    return artist['genres']
+
+def find_most_similar_genre(my_genres, artist_genres):
+    min_distance = float('inf')
+    most_similar_genre = None
+    for my_genre in my_genres:
+        for artist_genre in artist_genres:
+            d = distance(my_genre, artist_genre)
+            if d < min_distance:
+                min_distance = d
+                most_similar_genre = my_genre
+    return most_similar_genre
+
+def match_genres_to_list(track_id):
+    track_genres=get_track_genre(track_id)
+    return find_most_similar_genre(genre_list,track_genres)
+
+def search_songs(query):
+    results = sp.search(q=query, type="track")
+    songs = [f"{index}. {item['name']} by {item['artists'][0]['name']}" for index, item in enumerate(results["tracks"]["items"])]
+
+    track_ids = [item["id"] for item in results["tracks"]["items"]]
+    return songs, track_ids
+
+
+def get_song_features(song, track_ids):
+    index = int(song.split(".")[0])
+    track_id = track_ids[index]
+    track_info = sp.track(track_id)
+
+    features = sp.audio_features([track_id])[0]
+    genre = match_genres_to_list(track_id)
+    key_map = {0: 'C', 1: 'C#', 2: 'D', 3: 'D#', 4: 'E', 5: 'F', 6: 'F#', 7: 'G', 8: 'G#', 9: 'A', 10: 'A#', 11: 'B'}
+    key = str(key_map[features['key']])
+    mode_map = { 1: "Major", 0: "Minor"}
+    mode = mode_map[features['mode']]
+    
+    explicit_real = track_info['explicit']
+    features_list = [
+        features['duration_ms'],
+        explicit_real,
+        features['danceability'],
+        features['energy'],
+        key,
+        features['loudness'],
+        mode,
+        features['speechiness'],
+        features['acousticness'],
+        features['instrumentalness'],
+        features['liveness'],
+        features['valence'],
+        features['tempo'],
+        str(features['time_signature']),
+        genre
+    ]
+    
+    return features_list
+
+theme = gr.themes.Monochrome(
+    # text_size="text_lg",
+    font=[gr.themes.GoogleFont('Neucha'), 'ui-sans-serif', 'system-ui', 'sans-serif'],
+)
+with gr.Blocks(theme=theme) as demo:
+    with gr.Row():
+        image = gr.HTML("<div style='display: flex; align-items: center;'><img src='file=images/cat-jam.gif' alt='My gif' width='200' height='200'>" +
+                        "<div><h1 style='font-size: 60px; line-height: 24px; margin-left: 50px;'>Music Popularity Prediction</h1>" +
+                        "<h2 style='font-size: 24px; line-height: 18px; margin-left: 50px; margin-top: 50px'>by Keh Zheng Xian</h2></div></div>")
+
+    with gr.Row():
+        with gr.Column():
+            search_box = gr.Textbox(label="Search for songs")
+            song_dropdown = gr.Dropdown(label="Select a song", choices=[])
+            # features_box = gr.Textbox(label="Song features", interactive=False)
+            inputs = [
+                gr.Number(label="duration_ms",interactive=True),
+                gr.Checkbox(label="explicit",interactive=True),
+                gr.Slider(0.0, 1.0, label="danceability",interactive=True),
+                gr.Slider(0.0, 1.0, label="energy",interactive=True),
+                gr.Dropdown(label="key", choices=["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"],interactive=True),
+                gr.Number(label="loudness",interactive=True),
+                gr.Radio(label="mode", choices=["Major", "Minor"],interactive=True),
+                gr.Slider(0.0, 1.0, label="speechiness",interactive=True),
+                gr.Slider(0.0, 1.0, label="acousticness",interactive=True),
+                gr.Slider(0.0, 1.0, label="instrumentalness",interactive=True),
+                gr.Slider(0.0, 1.0, label="liveness",interactive=True),
+                gr.Slider(0.0, 1.0, label="valence",interactive=True),
+                gr.Number(label="tempo",interactive=True),
+                gr.Dropdown(label="time_signature", choices=[3, 4, 5, 6, 7],interactive=True),
+                gr.Dropdown(label="track_genre", choices=genre_list,interactive=True)
+            ]
+            predict_button = gr.Button(label="Predict popularity")
+
+        with gr.Column():
+            popularity_box = gr.HTML("<div style='display: flex; align-items: center;'><img src='file=images/pepe-waiting.gif' alt='My gif 2' width='200' height='200'>" +
+                        "<div><h1 style='font-size: 30px; line-height: 24px; margin-left: 50px;'>Waiting for your song...</h1></div>")
+    track_ids_var = gr.State()
+    def update_dropdown(query,track_ids):
+        songs, track_ids = search_songs(query)
+        return {song_dropdown: gr.update(choices=songs), track_ids_var: track_ids}
+
+    search_box.change(fn=update_dropdown, inputs=[search_box,track_ids_var], outputs=[song_dropdown,track_ids_var])
+
+    def update_features(song,track_ids):
+        print(song)
+        features = get_song_features(song, track_ids)
+        return features
+
+    def predict_popularity(duration_ms, explicit, danceability, energy, key, loudness, mode, speechiness, acousticness, instrumentalness, liveness, valence, tempo, time_signature,track_genre):
+        # Convert the key input from a string to an integer value
+        key_map = {"C": 0, "C#": 1, "D": 2, "D#": 3, "E": 4, "F": 5, "F#": 6, "G": 7, "G#": 8, "A": 9, "A#": 10, "B": 11}
+        key_real = str(key_map[key])
+        
+        explicit_real = int(explicit)
+        # Convert the mode input from a string to an integer value
+        mode_map = {"Major": 1, "Minor": 0}
+        mode_real = mode_map[mode]
+
+        data = {
+            "duration_ms": [duration_ms],
+            "explicit": [explicit_real],
+            "danceability": [danceability],
+            "energy": [energy],
+            "key": [key_real],
+            "loudness": [loudness],
+            "mode": [mode_real],
+            "speechiness": [speechiness],
+            "acousticness": [acousticness],
+            "instrumentalness": [instrumentalness],
+            "liveness": [liveness],
+            "valence": [valence],
+            "tempo": [tempo],
+            "time_signature": [str(time_signature)],
+            "track_genre": [track_genre]
+        }
+
+        df = pd.DataFrame(data)
+
+        # Use your trained model to predict popularity based on the input features
+        if(final_model.predict(df)[0] == 1):
+            return ("<div style='display: flex; align-items: center;'><img src='file=images/pepe-jam.gif' alt='My gif 3' width='200' height='200'>" +
+                        "<div><h1 style='font-size: 30px; line-height: 24px; margin-left: 50px;'>Your song issa boppp</h1></div>")
+        else:
+            return ("<div style='display: flex; align-items: center;'><img src='file=images/pepo-sad-pepe.gif' alt='My gif 4' width='200' height='200'>" +
+                        "<div><h1 style='font-size: 30px; line-height: 24px; margin-left: 50px;'>Not a bop....</h1></div>")
+
+    song_dropdown.change(fn=update_features, inputs=[song_dropdown,track_ids_var], outputs=inputs)
+    predict_button.click(fn=predict_popularity, inputs=inputs, outputs=popularity_box, scroll_to_output=True)
+
+    demo.launch()

final_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1643cf4b328c5addca850ae397d9a5d40ed094394bb5e3e16dfa502c7fb96f18
+size 8825585