Update app.py

app.py

@@ -1,47 +1,47 @@
-from fastapi import FastAPI
-from pydantic import BaseModel
-import joblib
-import pandas as pd
-
-app = FastAPI()
-
-# Load artifacts
-model = joblib.load("models/balanced_model_random_forest.pkl")
-scaler = joblib.load("models/scaler.pkl")
-encoders = joblib.load("models/encoders_dict.pkl")
-
-class LoanApplication(BaseModel):
-    person_age: float
-    person_gender: str
-    person_education: str
-    person_income: float
-    person_emp_exp: int
-    person_home_ownership: str
-    loan_amnt: float
-    loan_intent: str
-    loan_int_rate: float
-    loan_percent_income: float
-    cb_person_cred_hist_length: float
-    credit_score: int
-    previous_loan_defaults_on_file: str
-    debt_to_income: float
-    age_group: str
-
-@app.get("/")
-def root():
-    return {"message": "Credit Risk API is running inside Docker!"}
-
-@app.post("/predict")
-def predict(application: LoanApplication):
-    df = pd.DataFrame([application.dict()])
-
-    # Apply encoders
-    for col, encoder in encoders.items():
-        df[col] = encoder.transform(df[col].astype(str))
-
-    # Apply scaler
-    scaling_cols = ["person_age","person_income","loan_amnt","credit_score","loan_int_rate"]
-    df[scaling_cols] = scaler.transform(df[scaling_cols])
-
-    prediction = model.predict(df)[0]
-    return {"loan_status": int(prediction)}
+from fastapi import FastAPI
+from pydantic import BaseModel
+import joblib
+import pandas as pd
+
+app = FastAPI()
+
+# Load artifacts
+model = joblib.load("models/base_random_forest_model.pkl")
+scaler = joblib.load("models/scaler.pkl")
+encoders = joblib.load("models/encoders_dict.pkl")
+
+class LoanApplication(BaseModel):
+    person_age: float
+    person_gender: str
+    person_education: str
+    person_income: float
+    person_emp_exp: int
+    person_home_ownership: str
+    loan_amnt: float
+    loan_intent: str
+    loan_int_rate: float
+    loan_percent_income: float
+    cb_person_cred_hist_length: float
+    credit_score: int
+    previous_loan_defaults_on_file: str
+    debt_to_income: float
+    age_group: str
+
+@app.get("/")
+def root():
+    return {"message": "Credit Risk API is running inside Docker!"}
+
+@app.post("/predict")
+def predict(application: LoanApplication):
+    df = pd.DataFrame([application.dict()])
+
+    # Apply encoders
+    for col, encoder in encoders.items():
+        df[col] = encoder.transform(df[col].astype(str))
+
+    # Apply scaler
+    scaling_cols = ["person_age","person_income","loan_amnt","credit_score","loan_int_rate"]
+    df[scaling_cols] = scaler.transform(df[scaling_cols])
+
+    prediction = model.predict(df)[0]
+    return {"loan_status": int(prediction)}