Create app.py

app.py

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+import pickle
+import gradio as gr
+import pandas as pd
+import statsmodels.api as sm
+
+# Load the model from the file
+with open('linear_regression_model_encoded.pkl', 'rb') as file:
+    loaded_model = pickle.load(file)
+# The model is now loaded and ready to use
+
+train_encoded_columns = [
+    'age', 'bmi', 'bloodpressure', 'children',
+    'gender_male',  
+    'diabetic_Yes', 
+    'smoker_Yes',  
+    'region_northwest', 'region_southeast', 'region_southwest'
+]
+
+# Define the function that will use the model to predict
+def predict(age, bmi, bloodpressure,\
+            children, gender, diabetic, smoker, region):
+    # Create a DataFrame for the input data
+    input_data = pd.DataFrame({
+        'age': [age],
+        'bmi': [bmi],
+        'bloodpressure': [bloodpressure],
+        'children': [children],
+        'gender': [gender],
+        'diabetic': [diabetic],
+        'smoker': [smoker],
+        'region': [region]
+    })
+
+    # One-hot encode the input data
+    input_data_encoded = pd.get_dummies(input_data)
+
+    # Add missing columns as zeros and align the order of columns
+    for column in train_encoded_columns:
+        if column not in input_data_encoded.columns:
+            input_data_encoded[column] = 0
+    input_data_encoded = input_data_encoded[train_encoded_columns]
+
+    # Add a constant term if your model expects an intercept
+    input_data_encoded = sm.add_constant(input_data_encoded, has_constant='add')
+
+    # Make a prediction using the loaded model
+    prediction = loaded_model.predict(input_data_encoded)
+    return prediction[0]
+
+# Define the dropdown options based on the training data categories
+gender_options = ['male', 'female']
+diabetic_options = ['Yes', 'No']
+smoker_options = ['Yes', 'No']
+region_options = ['southwest', 'southeast', 'northwest', 'northeast']
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Number(label="Age"),
+        gr.Number(label="BMI"),
+        gr.Number(label="Blood Pressure"),
+        gr.Number(label="Children"),
+        gr.Dropdown(choices=gender_options, label="Gender", value='male'),
+        gr.Dropdown(choices=diabetic_options, label="Diabetic", value='Yes'),
+        gr.Dropdown(choices=smoker_options, label="Smoker", value='Yes'),
+        gr.Dropdown(choices=region_options, label="Region", value='northwest')
+    ],
+     outputs=gr.Textbox(label="Predicted Claim"),
+     title="Medical Claim Prediction",
+     description="Enter Age, BMI, and Blood Pressure to predict the medical claim",
+     allow_flagging='never')  # Set flagging to 'never'
+
+# Launch the interface
+iface.launch()