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app.py

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+import gradio as gr
+import pickle
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+
+# Load your trained model
+with open("log_reg.pkl", "rb") as f:
+    model = pickle.load(f)
+
+# Load or define your scaler (assumed you saved it similarly)
+with open("scaler.pkl", "rb") as f:
+    scaler = pickle.load(f)
+
+# Define the input features that your model uses, e.g.:
+input_features = [
+    "Age", "Income", "Debt", "Debt_to_Income", 
+    "Payment_History_Num", "Is_Employed",
+    # ... add other numeric features or encoded categorical features needed
+]
+
+def predict_creditworthiness(age, income, debt, payment_history, employment_status):
+    # Feature engineering like in training
+    debt_to_income = debt / (income + 1)
+    payment_map = {"Bad": 0, "Average": 1, "Good": 2}
+    payment_num = payment_map.get(payment_history, 1)
+    employed_num = 1 if employment_status == "Employed" else 0
+
+    # Create feature array in correct order expected by model
+    input_data = np.array([[age, income, debt, debt_to_income, payment_num, employed_num]])
+    
+    # Scale features
+    input_scaled = scaler.transform(input_data)
+
+    # Predict probability and class
+    proba = model.predict_proba(input_scaled)[0, 1]
+    pred_class = model.predict(input_scaled)[0]
+    
+    credit_status = "Good Credit" if pred_class == 1 else "Bad Credit"
+    return f"Prediction: {credit_status} (Probability of good credit: {proba:.2f})"
+
+# Gradio input widgets reflecting your features
+iface = gr.Interface(
+    fn=predict_creditworthiness,
+    inputs=[
+        gr.Number(label="Age", value=30, precision=0),
+        gr.Number(label="Income", value=50000, precision=2),
+        gr.Number(label="Debt", value=5000, precision=2),
+        gr.Dropdown(label="Payment History", choices=["Bad", "Average", "Good"], value="Average"),
+        gr.Radio(label="Employment Status", choices=["Employed", "Unemployed"], value="Employed"),
+    ],
+    outputs="text",
+    title="Credit Scoring Model (Logistic Regression)",
+    description="Enter your financial details to predict creditworthiness."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+
+
+

requirements.txt

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+pandas
+numpy
+flask
+matplotlib
+seaborn
+gradio
+sklearn.preprocessing
+ scikit-learn