Upload app.py

app.py

@@ -10,28 +10,20 @@ import pandas as pd
 OUTPUT_DATA_PATH = "data/processed/app_dataset.csv"
 PREDICTIONS_PATH = "models/predictions/app_predictions.csv"
 UNIQUE_VALUES_PATH = "models/other/unique_column_values.pkl"
+MODEL_PATH = "models/final_model.pkl"
 
 
 def predict(*args: tuple) -> Any:
     app_df = pd.DataFrame(data=[args], columns=columns, index=[0])
     app_df.to_csv(OUTPUT_DATA_PATH, index=False)
-    subprocess.run(
-        [
-            "python",
-            "-m",
-            "src.models.make_predictions",
-            "data/processed/app_dataset.csv",
-            "models/final_model.pkl",
-            "models/predictions/app_predictions.csv",
-        ],
-        shell=True,
-    )
-    predictions = np.genfromtxt(PREDICTIONS_PATH, delimiter=",", skip_header=1)
-    if predictions[2] == 1:
+    model = joblib.load(MODEL_PATH)
+    predictions = model.predict_proba(app_df)
+    print(predictions)
+    if predictions[0][0] <= 0.99:
         message = "Client is considered bad. Issuance of credit is not recommended."
     else:
         message = "Client is considered good. Issuance of credit is allowed."
-    return round(predictions[0], 3), message
+    return round(predictions[0][0], 3), message
 
 
 columns = (