Spaces:
Sleeping
Sleeping
File size: 10,151 Bytes
18f3f9b ca9af3c 18f3f9b ca9af3c 18f3f9b ca9af3c 18f3f9b ca9af3c 18f3f9b ca9af3c 18f3f9b ca9af3c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 | import os
import json
import joblib
import numpy as np
import pandas as pd
import gradio as gr
from pathlib import Path
# Modellarning lokal manzili
MODELS_DIR = Path("/home/user/app/outputs/models")
_fraud_predictor = None
_credit_predictor = None
def load_predictors():
global _fraud_predictor, _credit_predictor
if _fraud_predictor is None:
# Modellarni to'g'ridan-to'g'ri ko'rsatilgan papkadan yuklaymiz
fraud_dir = MODELS_DIR / "fraud"
credit_dir = MODELS_DIR / "credit"
_fraud_predictor = SimpleFraudPredictor(fraud_dir)
_credit_predictor = SimpleCreditPredictor(credit_dir)
class SimpleFraudPredictor:
def __init__(self, model_dir):
self.fe_artifacts = joblib.load(model_dir / "fe_artifacts.pkl")
self.prep_artifacts = joblib.load(model_dir / "prep_artifacts.pkl")
self.fs_artifacts = joblib.load(model_dir / "fs_artifacts.pkl")
self.model = joblib.load(model_dir / "fraud_model.pkl")
self.xgb = joblib.load(model_dir / "tuned_xgb.pkl")
self.lgb = joblib.load(model_dir / "tuned_lgb.pkl")
self.cat = joblib.load(model_dir / "tuned_cat.pkl")
with open(model_dir / "fraud_model_metadata.json") as f:
meta = json.load(f)
self.threshold = meta["threshold"]
self.model_name = meta["model_name"]
self.final_feats = self.fs_artifacts["final_features"]
def predict(self, transaction: dict) -> dict:
import sys
sys.path.insert(0, "/home/user/app")
from src.features.ieee_cis.feature_engineer import feature_engineer_test
from src.data.ieee_cis.preprocessor import preprocess_test
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
df = pd.DataFrame([transaction])
df = feature_engineer_test(df, self.fe_artifacts)
df = preprocess_test(df, self.prep_artifacts)
df = df.apply(pd.to_numeric, errors="coerce").fillna(0.0)
missing = [c for c in self.final_feats if c not in df.columns]
for col in missing:
df[col] = 0.0
X = df[self.final_feats]
if isinstance(self.model, (LogisticRegression, Pipeline)):
meta_input = np.column_stack([
self.xgb.predict_proba(X)[:, 1],
self.lgb.predict_proba(X)[:, 1],
self.cat.predict_proba(X)[:, 1],
])
prob = self.model.predict_proba(meta_input)[:, 1][0]
else:
prob = self.model.predict_proba(X)[:, 1][0]
return {
"fraud_probability": round(float(prob), 4),
"is_fraud": bool(prob >= self.threshold),
"threshold": self.threshold,
"model": self.model_name,
}
class SimpleCreditPredictor:
def __init__(self, model_dir):
self.fe_artifacts = joblib.load(model_dir / "fe_artifacts.pkl")
self.prep_artifacts = joblib.load(model_dir / "prep_artifacts.pkl")
self.fs_artifacts = joblib.load(model_dir / "fs_artifacts.pkl")
self.model = joblib.load(model_dir / "credit_model.pkl")
self.xgb = joblib.load(model_dir / "tuned_xgb.pkl")
self.lgb = joblib.load(model_dir / "tuned_lgb.pkl")
self.cat = joblib.load(model_dir / "tuned_cat.pkl")
with open(model_dir / "credit_model_metadata.json") as f:
meta = json.load(f)
self.threshold = meta["threshold"]
self.model_name = meta["model_name"]
self.final_feats = self.fs_artifacts["final_features"]
def predict(self, applicant: dict) -> dict:
import sys
sys.path.insert(0, "/home/user/app")
from src.features.home_credit.feature_engineer import feature_engineer_test
from src.data.home_credit.preprocessor import preprocess_test
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
df = pd.DataFrame([applicant])
empty_tables = {
"bureau": pd.DataFrame(), "bureau_balance": pd.DataFrame(),
"previous_app": pd.DataFrame(), "pos_cash": pd.DataFrame(),
"installments": pd.DataFrame(), "credit_card": pd.DataFrame(),
}
df = feature_engineer_test(df, empty_tables, self.fe_artifacts)
df = preprocess_test(df, self.prep_artifacts)
df = df.apply(pd.to_numeric, errors="coerce").fillna(0.0)
missing = [c for c in self.final_feats if c not in df.columns]
for col in missing:
df[col] = 0.0
X = df[self.final_feats]
if isinstance(self.model, (LogisticRegression, Pipeline)):
meta_input = np.column_stack([
self.xgb.predict_proba(X)[:, 1],
self.lgb.predict_proba(X)[:, 1],
self.cat.predict_proba(X)[:, 1],
])
prob = self.model.predict_proba(meta_input)[:, 1][0]
else:
prob = self.model.predict_proba(X)[:, 1][0]
return {
"default_probability": round(float(prob), 4),
"will_default": bool(prob >= self.threshold),
"threshold": self.threshold,
"model": self.model_name,
}
def predict_fraud(txn_id, txn_dt, txn_amt, prod_cd, card4, card6, p_email, r_email, dev_type):
load_predictors()
payload = {
"TransactionID": int(txn_id), "TransactionDT": int(txn_dt),
"TransactionAmt": float(txn_amt), "ProductCD": prod_cd,
"card4": card4, "card6": card6,
"P_emaildomain": p_email, "R_emaildomain": r_email,
"DeviceType": dev_type,
}
result = _fraud_predictor.predict(payload)
color = "RED — FRAUD" if result["is_fraud"] else "GREEN — LEGITIMATE"
output = f"Decision: {color}\nFraud Probability: {result['fraud_probability']}\nThreshold: {result['threshold']}\nModel: {result['model']}"
return output, round(result["fraud_probability"] * 100, 2)
def predict_credit(sk_id, amt_credit, amt_income, amt_annuity, days_birth, days_employed, ext1, ext2, ext3):
load_predictors()
payload = {
"SK_ID_CURR": int(sk_id), "AMT_CREDIT": float(amt_credit),
"AMT_INCOME_TOTAL": float(amt_income), "AMT_ANNUITY": float(amt_annuity),
"DAYS_BIRTH": int(days_birth), "DAYS_EMPLOYED": int(days_employed),
}
if ext1: payload["EXT_SOURCE_1"] = float(ext1)
if ext2: payload["EXT_SOURCE_2"] = float(ext2)
if ext3: payload["EXT_SOURCE_3"] = float(ext3)
result = _credit_predictor.predict(payload)
color = "RED — HIGH RISK" if result["will_default"] else "GREEN — LOW RISK"
output = f"Decision: {color}\nDefault Probability: {result['default_probability']}\nThreshold: {result['threshold']}\nModel: {result['model']}"
return output, round(result["default_probability"] * 100, 2)
# Gradio Interfeysi
with gr.Blocks(title="FinRiskGuard", theme=gr.themes.Soft()) as demo:
gr.Markdown("# FinRiskGuard\n### Fraud Detection and Credit Default Scoring")
with gr.Tabs():
with gr.Tab("Fraud Detection"):
gr.Markdown("#### IEEE-CIS | AUC-ROC: 0.9258 | Threshold: 0.44")
with gr.Row():
with gr.Column():
txn_id = gr.Number(label="TransactionID", value=2987004, precision=0)
txn_dt = gr.Number(label="TransactionDT", value=86400, precision=0)
txn_amt = gr.Number(label="TransactionAmt", value=117.5)
prod_cd = gr.Dropdown(["W","H","C","S","R"], label="ProductCD", value="W")
card4 = gr.Dropdown(["visa","mastercard","american express","discover"], label="card4", value="visa")
card6 = gr.Dropdown(["debit","credit","debit or credit","charge card"], label="card6", value="debit")
p_email = gr.Textbox(label="P_emaildomain", value="gmail.com")
r_email = gr.Textbox(label="R_emaildomain", value="gmail.com")
dev_type = gr.Dropdown(["desktop","mobile"], label="DeviceType", value="desktop")
btn_f = gr.Button("Predict", variant="primary")
with gr.Column():
fraud_result = gr.Textbox(label="Result")
fraud_gauge = gr.Number(label="Fraud Probability (%)")
btn_f.click(predict_fraud,
inputs=[txn_id, txn_dt, txn_amt, prod_cd, card4, card6, p_email, r_email, dev_type],
outputs=[fraud_result, fraud_gauge])
with gr.Tab("Credit Scoring"):
gr.Markdown("#### Home Credit | AUC-ROC: 0.7849 | Threshold: 0.50")
with gr.Row():
with gr.Column():
sk_id = gr.Number(label="SK_ID_CURR", value=100002, precision=0)
amt_cred = gr.Number(label="AMT_CREDIT", value=406597.5)
amt_inc = gr.Number(label="AMT_INCOME_TOTAL", value=202500.0)
amt_ann = gr.Number(label="AMT_ANNUITY", value=24700.5)
d_birth = gr.Number(label="DAYS_BIRTH", value=-9461, precision=0)
d_emp = gr.Number(label="DAYS_EMPLOYED", value=-637, precision=0)
ext1 = gr.Number(label="EXT_SOURCE_1", value=None)
ext2 = gr.Number(label="EXT_SOURCE_2", value=0.651)
ext3 = gr.Number(label="EXT_SOURCE_3", value=0.493)
btn_c = gr.Button("Predict", variant="primary")
with gr.Column():
credit_result = gr.Textbox(label="Result")
credit_gauge = gr.Number(label="Default Probability (%)")
btn_c.click(predict_credit,
inputs=[sk_id, amt_cred, amt_inc, amt_ann, d_birth, d_emp, ext1, ext2, ext3],
outputs=[credit_result, credit_gauge])
gr.Markdown("---\nFinRiskGuard | XGBoost · LightGBM · CatBoost · Stacking · SHAP")
if __name__ == "__main__":
demo.launch() |