Spaces:
Runtime error
Runtime error
| # Import the libraries | |
| import gradio as gr | |
| import joblib | |
| import pandas as pd | |
| # 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' | |
| # Load the freshly trained model from disk | |
| model = 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="-----------", # 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 | |
| def predict_insu_charges(age, bmi, children, sex, smoker, region): | |
| sample = { | |
| 'Age': age, | |
| 'bmi' : bmi, | |
| 'children' : children, | |
| 'sex' : sex, | |
| 'smoker' : smoker, | |
| 'region' : region | |
| } | |
| data_point = pd.DataFrame([sample]) | |
| result = model.predict(data_point) | |
| print(result) | |
| return result | |
| # the functions runs when 'Submit' is clicked or when a API request is made | |
| # 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="Number of children") | |
| sex_input = gr.Dropdown(['Female','Male'],label="Age") | |
| smoker_input = gr.Dropdown(['Yes','No'],label="smoker?") | |
| region_input = gr.Dropdown(['SouthWest','NorthWest','SouthEast','NorthEast'],label="Age") | |
| model_output = gr.Label(label="charges") | |
| # Create the gradio interface, make title "HealthyLife Insurance Charge Prediction" | |
| demo = gr.Interface(fn=predict_insu_charges, | |
| inputs = ['age_input', 'bmi_input','children_input','sex_input','smoker_input','region_input'], | |
| outputs = model_output, | |
| title = "HealthyLife Insurance Charge Prediction", | |
| description = "For predicting insurance charges", | |
| allow_flagging = "auto") | |
| interface.launch(share=True) | |
| # Launch with a load balancer | |
| demo.queue() | |
| demo.launch(share=False) | |