Add FastAPI app with Docker, CI/CD, and linter

Author: sumeyaaaa

register.py

@@ -1,6 +1,7 @@
+from pathlib import Path
+
 import mlflow
 from mlflow.tracking import MlflowClient
-from pathlib import Path
 
 # Set tracking URI
 mlruns_path = Path(

src/PreProcessing.py

@@ -1,12 +1,8 @@
 import pandas as pd
-from sklearn.pipeline import Pipeline
-from sklearn.preprocessing import (
-    StandardScaler,
-    RobustScaler,
-    OneHotEncoder
-)
 from sklearn.compose import ColumnTransformer
 from sklearn.impute import SimpleImputer
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import OneHotEncoder, RobustScaler
 
 
 def process_data(file_path):

src/RFMmetrics.py

@@ -1,6 +1,6 @@
 import pandas as pd
-from sklearn.preprocessing import StandardScaler
 from sklearn.cluster import KMeans
+from sklearn.preprocessing import StandardScaler
 
 
 def load_data(filepath):

src/api/main.py

@@ -1,6 +1,7 @@
 import mlflow
-from fastapi import FastAPI
 import pandas as pd
+from fastapi import FastAPI
+
 from .pydantic_models import CustomerData, PredictionResponse
 
 app = FastAPI()

src/api/pydantic_models.py

@@ -1,5 +1,6 @@
 from pydantic import BaseModel
 
+
 class CustomerData(BaseModel):
     Recency: int
     Frequency: int

src/load.py

@@ -1,7 +1,7 @@
-import pandas as pd
+import matplotlib.pyplot as plt
 import numpy as np
+import pandas as pd
 import seaborn as sns
-import matplotlib.pyplot as plt
 from scipy.stats import chi2_contingency
 
 

src/visualization.py

@@ -1,6 +1,6 @@
+import matplotlib.pyplot as plt
 import pandas as pd
 import seaborn as sns
-import matplotlib.pyplot as plt
 
 
 def plot_feature_distributions(data):

test/test_api.py

@@ -1,9 +1,12 @@
-import sys
 import os
-from unittest.mock import patch, MagicMock
+import sys
+from unittest.mock import MagicMock, patch
+
 
 # Add src to sys.path for imports
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'src')))
+sys.path.insert(0, os.path.abspath(os.path.join(
+    os.path.dirname(__file__), '..', 'src')))
+
 
 # Mock mlflow.sklearn.load_model before importing your app
 mock_model = MagicMock()
@@ -12,12 +15,12 @@
 patcher = patch('mlflow.sklearn.load_model', return_value=mock_model)
 patcher.start()
 
-from api.main import app  # import AFTER patching
-
 from fastapi.testclient import TestClient
+from api.main import app  # import AFTER patching
 
 client = TestClient(app)
 
+
 def test_predict():
     sample_data = {
         "Recency": 1,
@@ -45,12 +48,13 @@ def test_predict():
         "ProductCategory_ticket": False,
         "ProductCategory_transport": False,
         "ProductCategory_tv": False,
-        "ProductCategory_utility_bill": False
+        "ProductCategory_utility_bill": False,
     }
 
     response = client.post("/predict", json=sample_data)
     assert response.status_code == 200
     assert "risk_probability" in response.json()
     assert abs(response.json()["risk_probability"] - 0.7) < 1e-6
 
+
 patcher.stop()