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| # Import the libraries | |
| import os | |
| import uuid | |
| import joblib | |
| import json | |
| # Run the training script placed in the same directory as app.py | |
| # The training script will train and persist a linear regression | |
| # model with the filename 'model.joblib' | |
| os.system("python train.py") | |
| # Load the freshly trained model from disk | |
| insurance_charge_predictor = joblib.load("model.joblib") | |
| # Prepare the logging functionality | |
| log_file = Path("logs/") / f"data_{uuid.uuid4()}.json" | |
| log_folder = log_file.parent | |
| scheduler = CommitScheduler( | |
| repo_id="insurance-charge-mlops-logs", # provide a name "insurance-charge-mlops-logs" for the repo_id | |
| repo_type="dataset", | |
| folder_path=log_folder, | |
| path_in_repo="data", | |
| every=2 | |
| ) | |
| # Define the predict function which will take features, convert to dataframe and make predictions using the saved model | |
| # the functions runs when 'Submit' is clicked or when a API request is made | |
| def predict_insurance_charge(age, sex, bmi, children, smoker, region): | |
| sample = { | |
| 'age': age, | |
| 'bmi': bmi, | |
| 'children': children, | |
| 'sex': sex, | |
| 'smoker': smoker, | |
| 'region': region | |
| } | |
| data_point = pd.DataFrame([sample]) | |
| prediction = insurance_charge_predictor.predict(data_point).tolist() | |
| # While the prediction is made, log both the inputs and outputs to a log file | |
| # While writing to the log file, ensure that the commit scheduler is locked to avoid parallel | |
| # access | |
| with scheduler.lock: | |
| with log_file.open("a") as f: | |
| f.write(json.dumps( | |
| { | |
| 'age': age, | |
| 'bmi': bmi, | |
| 'children': children, | |
| 'sex': sex, | |
| 'smoker': smoker, | |
| 'region': region, | |
| 'prediction': prediction[0] | |
| } | |
| )) | |
| f.write("\n") | |
| return prediction[0] | |
| # Set up UI components for input and output | |
| age_input = gr.Number(label='age') | |
| bmi_input = gr.Number(label='bmi') | |
| children_input = gr.Number(label='children') | |
| sex_input = gr.Dropdown(['male','female'], label='sex') | |
| smoker_input = gr.Dropdown(['yes','no'], label='smoker') | |
| region_input = gr.Dropdown(['southwest', 'southeast', 'northwest', 'northeast'], label = 'region') | |
| model_output = gr.Label(label='Insurance Charges') | |
| # Create the gradio interface, make title "HealthyLife Insurance Charge Prediction" | |
| demo = gr.Interface( | |
| fn=predict_insurance_charge, | |
| inputs=[age_inut, bmi_input, children_input, sex_input, smoker_input, region_input], | |
| outputs=model_output, | |
| title='HealthyLife Insurance Charge Prediction', | |
| description='This API allows you to predict the estimating insurance charges based on customer attributes', | |
| examples= [[33,33.44,4,'male','no','southeast'], | |
| [62,25.1,2,'male','no','southwest'], | |
| [65,26.7,2,'female','no','southwest']], | |
| concurrency_limit=16 | |
| ) | |
| # Launch with a load balancer | |
| demo.queue() | |
| demo.launch(share=False) | |