Merge pull request #45 from raminmohammadi/akhil

Labs/Airflow_Labs/Lab_2/dags/Flask_API.py

@@ -45,7 +45,7 @@ def failure():
 
 # Function to start Flask app
 def start_flask_app():
-    app.run(host='0.0.0.1', port=5555)
+    app.run(host='0.0.0.0', port=5555)
 
 #To address this issue, you can use the TriggerDagRunOperator to trigger a separate DAG once start_flask_API starts the Flask server.
 # This new DAG can handle the Flask server's lifecycle independently, allowing the current DAG to proceed with sending the email.

Labs/Airflow_Labs/Lab_2/dags/main.py

@@ -46,7 +46,7 @@ def notify_failure(context):
 dag  = DAG('Airflow_Lab2', 
            default_args=default_args,
            description='Aiflow-Lab2 DAG Description',
-           schedule_interval='1 * * * *',
+           schedule_interval='*/1 * * * *',
            catchup=False, #The scheduler, by default, will kick off a DAG Run for any data interval that has not been run since the last data interval (or has been cleared). This concept is called Catchup.
            tags=['example'],
            owner_links={"Ramin Mohammadi": "https://github.com/raminmohammadi/MLOps/"}         

Labs/Airflow_Labs/Lab_2/dags/src/model_development.py

@@ -39,25 +39,25 @@ def data_preprocessing(data):
     X_train = ct.fit_transform(X_train)
     X_test = ct.transform(X_test)
 
-    return X_train, X_test, y_train, y_test
+    return X_train, X_test, y_train.values, y_test.values
 
 # Build and save a logistic regression model
 def build_model(data, filename):
     X_train, X_test, y_train, y_test = data
 
-    # Define hyperparameter grid for grid search
-    lr_clf = LogisticRegression()
-    penalty = ['l1', 'l2']
-    C = [0.5, 0.6, 0.7, 0.8]
-    class_weight = [{1: 0.5, 0: 0.5}, {1: 0.4, 0: 0.6}, {1: 0.6, 0: 0.4}, {1: 0.7, 0: 0.3}]
-    solver = ['liblinear', 'saga']
+    # Define hyperparameter grid for grid search if required
+    # lr_clf = LogisticRegression()
+    # penalty = ['l1', 'l2']
+    # C = [0.5, 0.6, 0.7, 0.8]
+    # class_weight = [{1: 0.5, 0: 0.5}, {1: 0.4, 0: 0.6}, {1: 0.6, 0: 0.4}, {1: 0.7, 0: 0.3}]
+    # solver = ['liblinear', 'saga']
 
-    param_grid = dict(
-        penalty=penalty,
-        C=C,
-        class_weight=class_weight,
-        solver=solver
-    )
+    # param_grid = dict(
+    #     penalty=penalty,
+    #     C=C,
+    #     class_weight=class_weight,
+    #     solver=solver
+    # )
 
     # Create and train a logistic regression model with the best parameters
     lr_clf = LogisticRegression()

Labs/Airflow_Labs/Lab_2/requirements.txt

@@ -4,4 +4,4 @@ flask
 scikit-learn
 scipy
 pandas
-numpy
+numpy