Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import pickle
+
+# Load the trained model
+with open('catboost_model.pkl', 'rb') as model_file:
+    regressor = pickle.load(model_file)
+
+# Define the prediction function
+def predict_insurance_cost(age, sex, bmi, children, smoker, region):
+    # Create a DataFrame from the input data
+    input_data = pd.DataFrame(
+        [[age, sex, bmi, children, smoker, region]],
+        columns=['age', 'sex', 'bmi', 'children', 'smoker', 'region']
+    )
+    
+    # Make prediction
+    prediction = regressor.predict(input_data)
+    return f'The insurance cost is USD {prediction[0]:.2f}'
+
+# Set up the Gradio interface
+inputs = [
+    gr.Slider(minimum=18, maximum=100,step=1, value=31, label="Age"),
+    gr.Radio(choices=['Female', 'Male']),
+    gr.Slider(minimum=10.0, maximum=50.0, step=0.1, value=25.74, label="BMI"),
+    gr.Slider(minimum=0, maximum=10, value=0,step=1, label="Children"),
+    gr.Radio(choices=['NO', 'Yes']),
+    gr.Dropdown(choices=['Southwest', 'Southeast',  'Northwest', 'Northeast'], label="Region (Southwest, Southeast, Northwest, Northeast)")
+]
+
+output = gr.Textbox(label="Predicted Insurance Cost")
+
+# Create the Gradio interface
+gr.Interface(fn=predict_insurance_cost, inputs=inputs, outputs=output, title="Medical Insurance Cost Predictor", description="Predict the insurance cost based on various parameters.").launch()