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from .ml_model import ml_engine
import json
KEY_MAP = {
"gender": "Gender",
"married": "Married",
"dependents": "Dependents",
"education": "Education",
"self_employed": "Self_Employed",
"applicant_income": "ApplicantIncome",
"coapplicant_income": "CoapplicantIncome",
"loan_amount": "LoanAmount",
"loan_amount_term": "Loan_Amount_Term",
"credit_history": "Credit_History",
"property_area": "Property_Area"
}
def map_keys(data: dict) -> dict:
return {KEY_MAP.get(k, k): v for k, v in data.items()}
def prepare_input(base_data: dict) -> dict:
# Compute derived features for the ML Engine
total_income = base_data["ApplicantIncome"] + base_data["CoapplicantIncome"]
# Avoid division by zero
term = base_data["Loan_Amount_Term"] if base_data["Loan_Amount_Term"] > 0 else 360
emi = (base_data["LoanAmount"] * 1000) / term
balance_income = total_income - emi
base_data["Total_Income"] = total_income
base_data["EMI"] = emi
base_data["Balance_Income"] = balance_income
return base_data
def process_pipeline(input_data: dict) -> dict:
"""Evaluates the loan application and runs sensitivity analysis if rejected."""
# 0. Map snake_case API keys to PascalCase Model keys
mapped_input = map_keys(input_data)
# 1. Base Evaluation
processed_input = prepare_input(copy.deepcopy(mapped_input))
result, confidence = ml_engine.predict(processed_input)
dti_ratio = 0.0
if processed_input["Total_Income"] > 0:
dti_ratio = processed_input["EMI"] / processed_input["Total_Income"]
dti_ratio_pct = min(100.0, dti_ratio * 100)
response = {
"prediction": result,
"confidence": confidence,
"dti_ratio": dti_ratio_pct,
"optimized_suggestion": "Your profile meets all current thresholds. No optimization necessary.",
"feature_importances": ml_engine.get_feature_importances(),
"benchmarks": ml_engine.get_benchmarks()
}
# 2. What-If Optimization (If Rejected)
if result == "N":
# Simulate lower loan amount
sim_data_loan = copy.deepcopy(mapped_input)
loan_decrease_needed = 0
loan_approved = False
while sim_data_loan["LoanAmount"] > 10 and not loan_approved:
sim_data_loan["LoanAmount"] -= 10 # Decrease by 10k
loan_decrease_needed += 10
p_input = prepare_input(copy.deepcopy(sim_data_loan))
sim_res, sim_conf = ml_engine.predict(p_input)
if sim_res == "Y":
loan_approved = True
break
# Simulate higher coapplicant income
sim_data_inc = copy.deepcopy(mapped_input)
inc_increase_needed = 0
inc_approved = False
while inc_increase_needed < 20000 and not inc_approved:
sim_data_inc["CoapplicantIncome"] += 1000 # Increase by 1k
inc_increase_needed += 1000
p_input_inc = prepare_input(copy.deepcopy(sim_data_inc))
sim_res_inc, sim_conf_inc = ml_engine.predict(p_input_inc)
if sim_res_inc == "Y":
inc_approved = True
break
# Generate rule-based optimization text (Determnistic)
suggestion = "Unfortunately, we couldn't find a minor adjustment to approve your loan. Consider improving your CIBIL score."
if loan_approved and inc_approved:
suggestion = f"Reducing your loan request by ₹{loan_decrease_needed * 1000} OR adding a co-applicant income of ~₹{inc_increase_needed} would likely result in approval."
elif loan_approved:
suggestion = f"Reducing your loan request by ₹{loan_decrease_needed * 1000} would likely result in an approval."
elif inc_approved:
suggestion = f"Adding a co-applicant income of ~₹{inc_increase_needed} would likely result in an approval."
if mapped_input["Credit_History"] == 0:
suggestion = "Your Credit History is 0. This is the primary blocking factor. Improving your credit standing is required before other adjustments will work."
response["optimized_suggestion"] = suggestion
return response
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