Update app.py

app.py

@@ -1,61 +1,51 @@
-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()
-
-
-
+import gradio as gr
+import pickle
+import numpy as np
+
+# Load model and scaler
+with open("log_reg.pkl", "rb") as f:
+    model = pickle.load(f)
+
+with open("scaler.pkl", "rb") as f:
+    scaler = pickle.load(f)
+
+def predict_creditworthiness(age, income, debt, payment_history, employment_status):
+    try:
+        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
+
+        input_data = np.array([[age, income, debt, debt_to_income, payment_num, employed_num]])
+        
+        # --- Debug prints ---
+        print("Input data", input_data)
+        print("Input shape", input_data.shape)
+        print("Scaler expects", scaler.mean_.shape)   # Should be (6,)
+
+        input_scaled = scaler.transform(input_data)
+        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})"
+    except Exception as e:
+        # Print error for debug
+        print("Prediction error:", str(e))
+        return f"Error: {str(e)}"
+
+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()