update

Kubernetes/deployment.yaml

@@ -32,9 +32,6 @@ spec:
         volumeMounts:
         - name: persistent-volume
           mountPath: /app/models
-        env:
-        - name: FILE_PATH_DATASET
-          value: "https://homepages.dcc.ufmg.br/~cunha/hosted/cloudcomp-2023s2-datasets/2023_spotify_ds1.csv"
       ## Persistent Volume ##
       volumes:
       - name: persistent-volume

Rest_API/Dockerfile

@@ -14,10 +14,6 @@ RUN adduser \
     --uid "${UID}" \
     appuser
 
-RUN apt-get update && \
-    apt-get install -y wget && \
-    rm -rf /var/lib/apt/lists/
-
 RUN pip install -r requirements.txt
 
 USER appuser

Rest_API/server.py

@@ -59,9 +59,9 @@ def recommend():
 
 @app.route('/api/songs', methods=['GET'])
 def get_songs():
-    with open('/app/Tracknames/updated_track_names.json', 'r') as file:
+    with open('Tracknames/updated_track_names.json', 'r') as file:
         songs_data = json.load(file)
     return jsonify({'songs': songs_data})
 
 if __name__ == '__main__':
-    app.run(port=32196)
+    app.run(port=32196, host="0.0.0.0")

Train_model/ml.py

@@ -1,25 +1,25 @@
 import pickle
 import pandas as pd
-import os
-import ssl
+import requests
+from io import StringIO
 from mlxtend.frequent_patterns import apriori, association_rules
 
-FILE_PATH_DATASET = os.environ.get('FILE_PATH_DATASET')
+URL = "https://homepages.dcc.ufmg.br/~cunha/hosted/cloudcomp-2023s2-datasets/2023_spotify_ds1.csv"
 RULES_PATH = "/app/models/rules.pkl"
 FREK_PATH = "/app/models/freq.pkl"
 
-min_support = 0.05
+min_support = 0.04
 min_threshold = 1
 
 class FreqDatasetMining:
-    def __init__(self, file_path):
-        self.file_path = file_path
+    def __init__(self, file):
+        self.file = file
         self.df = None
         self.freq = None
         self.rules = None
 
     def group_by_pid_and_track_uri(self):
-        df = pd.read_csv(self.file_path)
+        df = self.file
 
         df = df.groupby(['pid', 'track_name'])['track_name'].count().unstack().fillna(0)
 
@@ -48,7 +48,10 @@ def get_rules(self):
 
 
 if __name__ == '__main__':
-    fdm = FreqDatasetMining(FILE_PATH_DATASET)
+    response = requests.get(URL, verify=False)
+    csv_data = StringIO(response.text)
+    df = pd.read_csv(csv_data)
+    fdm = FreqDatasetMining(df)
     fdm.group_by_pid_and_track_uri()
     fdm.get_freq()
     fdm.get_rules()

Train_model/requirements.txt

@@ -1,2 +1,3 @@
 mlxtend
-pandas
+pandas
+requests