Create app.py

app.py

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+import pandas as pd
+import gradio as gr
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.preprocessing import LabelEncoder
+
+# Sample BFSI dataset
+data = {
+    'Age': [25, 45, 30, 50, 28, 42],
+    'Income': [300000, 1200000, 600000, 1500000, 350000, 1000000],
+    'CreditScore': [720, 800, 760, 820, 710, 790],
+    'RiskAppetite': ['High', 'Low', 'Medium', 'Low', 'High', 'Medium'],
+    'LoanAmount': [200000, 600000, 400000, 750000, 220000, 580000]
+}
+
+# Prepare data
+df = pd.DataFrame(data)
+le = LabelEncoder()
+df['RiskAppetiteEncoded'] = le.fit_transform(df['RiskAppetite'])
+
+X = df[['Age', 'Income', 'CreditScore', 'RiskAppetiteEncoded']]
+y = df['LoanAmount']
+
+# Train model
+model = DecisionTreeRegressor(random_state=42)
+model.fit(X, y)
+
+# Prediction function
+def predict_loan(age, income, credit_score, risk_appetite):
+    encoded_risk = le.transform([risk_appetite])[0]
+    input_df = pd.DataFrame([[age, income, credit_score, encoded_risk]],
+                            columns=['Age', 'Income', 'CreditScore', 'RiskAppetiteEncoded'])
+    prediction = model.predict(input_df)[0]
+    return f"Predicted Loan Amount: ₹{prediction:,.0f}"
+
+# Build Gradio interface
+app = gr.Interface(
+    fn=predict_loan,
+    inputs=[
+        gr.Number(label="Age"),
+        gr.Number(label="Annual Income (₹)"),
+        gr.Number(label="Credit Score"),
+        gr.Dropdown(choices=['High', 'Medium', 'Low'], label="Risk Appetite")
+    ],
+    outputs=gr.Textbox(label="Loan Prediction"),
+    title="Loan Amount Predictor (Tree-Based Regression)",
+    description="Enter customer details to predict the loan amount using a decision tree model."
+)
+
+# Launch the app
+app.launch()