Correcting format based on flask 8

Author: sumeyaaaa

src/api/main.py

@@ -4,15 +4,19 @@
 
 from .pydantic_models import CustomerData, PredictionResponse
 
+
 app = FastAPI()
 
 # Set MLflow tracking URI to the container's path
 mlflow.set_tracking_uri("file:///app/mlruns")
 
 # Load the model from MLflow artifacts inside Docker
-model_uri = "file:///app/mlruns/1/models/m-b56f931bfa444e04b71e0ac2ecbe00fb/artifacts"
+model_uri = (
+    "file:///app/mlruns/1/models/m-b56f931bfa444e04b71e0ac2ecbe00fb/artifacts"
+)
 model = mlflow.sklearn.load_model(model_uri)
 
+
 @app.post("/predict", response_model=PredictionResponse)
 def predict(data: CustomerData):
     try:
@@ -23,12 +27,3 @@ def predict(data: CustomerData):
         return {"risk_probability": prob}
     except Exception as e:
         return {"detail": f"Prediction failed: {e}"}
-
-#@app.post("/predict", response_model=PredictionResponse)
-#def predict(data: CustomerData):
-    #try:
-        #input_df = pd.DataFrame([data.dict()])
-        #prob = model.predict_proba(input_df)[0][1]
-        #return {"risk_probability": prob}
-    #except Exception as e:
-        #return {"detail": f"Prediction failed: {e}"}

src/api/pydantic_models.py

@@ -29,5 +29,6 @@ class CustomerData(BaseModel):
     ProductCategory_tv: bool
     ProductCategory_utility_bill: bool
 
+
 class PredictionResponse(BaseModel):
     risk_probability: float

src/load.py

@@ -26,8 +26,13 @@ def summary_stats(df):
 
 
 def logarithmic_numerical_distribution(
-    df, columns=['Amount', 'Value', 'FraudResult']
+    df,
+    columns=None,
 ):
+    if columns is None:
+        columns = ['Amount', 'Value', 'FraudResult']
+
+
     """
     Plot log-scale histograms for positive and negative values of specified columns.
     """

src/saveFile.py

@@ -1,8 +1,8 @@
 import pandas as pd
 
-# Define your output path
 output_path = (
-    r"C:\Users\ABC\Desktop\10Acadamy\Week 5\Credit-Risk-Probability-Model"
+    r"C:\Users\ABC\Desktop\10Acadamy\Week 5"
+    r"\Credit-Risk-Probability-Model"
     r"\data\processed"
 )
 

src/visualization.py

@@ -1,5 +1,4 @@
 import matplotlib.pyplot as plt
-import pandas as pd
 import seaborn as sns
 
 

test/test_api.py

@@ -1,7 +1,8 @@
 import os
 import sys
 from unittest.mock import MagicMock, patch
-
+from fastapi.testclient import TestClient
+from api.main import app  # import AFTER patching
 
 # Add src to sys.path for imports
 sys.path.insert(0, os.path.abspath(os.path.join(
@@ -15,8 +16,6 @@
 patcher = patch('mlflow.sklearn.load_model', return_value=mock_model)
 patcher.start()
 
-from fastapi.testclient import TestClient
-from api.main import app  # import AFTER patching
 
 client = TestClient(app)