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app.py

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+# Import the libraries
+import joblib
+
+from sklearn.datasets import fetch_openml
+
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import make_column_transformer
+
+from sklearn.pipeline import make_pipeline
+
+from sklearn.model_selection import train_test_split, RandomizedSearchCV
+
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score, classification_report
+
+# 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'
+
+# Run training script 
+import pandas as pd
+    from sklearn.model_selection import train_test_split
+    from sklearn.ensemble import RandomForestRegressor
+   
+import joblib
+
+# Choose and instantiate RandomForestRegressor model
+model = RandomForestRegressor()
+
+
+# Train the model using the training data
+model.fit(X_train, y_train)
+
+# Save the trained model to a file (e.g., 'insurance_model.joblib')
+joblib.dump(model, '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
+log_file.parent.mkdir(exist_ok=True)
+
+
+#######################################################################################################################
+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
+# the functions runs when 'Submit' is clicked or when a API request is made
+
+import pandas as pd
+import joblib
+
+# Assuming 'model' is a trained model loaded using joblib.load()
+
+def predict(age, sex, bmi, children, smoker, region):
+    """Predicts insurance charges based on customer features.
+
+    Args:
+        age (int): Age of the customer.
+        sex (int): Gender of the customer (0 for female, 1 for male).
+        bmi (float): Body Mass Index of the customer.
+        children (int): Number of children the customer has.
+        smoker (int): Smoking status of the customer (0 for yes, 1 for no).
+        region (int): Region of the customer (1-4 representing different regions).
+
+    Returns:
+        float: Predicted insurance charges.
+    """
+
+    # Create a DataFrame with the input features
+    input_data = pd.DataFrame({
+        'age': [age],
+        'sex': [sex],
+        'bmi': [bmi],
+        'children': [children],
+        'smoker': [smoker],
+        'region': [region]
+    })
+
+    # Make the prediction using the loaded model
+    prediction = model.predict(input_data)
+
+    # Return the prediction
+    return prediction[0]
+
+
+
+    # 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
+import gradio as gr
+
+age = gr.inputs.Slider(minimum=18, maximum=64, default=30, step=1, label="Age")
+sex = gr.inputs.Radio(["female", "male"], type)
+
+
+# Create the gradio interface, make title "HealthyLife Insurance Charge Prediction"
+import gradio as gr
+
+# ... (previous code for model training and predict function) ...
+
+# Set up UI components for input and output
+age = gr.inputs.Slider(minimum=18, maximum=64, default=30, step=1, label="Age")
+sex = gr.inputs.Radio(["female", "male"], type="value", default="female", label="Sex")  # Corrected type
+bmi = gr.inputs.Number(default=25, label="BMI")
+children = gr.inputs.Slider(minimum=0, maximum=5, default=0, step=1, label="Children")
+smoker = gr.inputs.Radio(["yes", "no"], type="value", default="no", label="Smoker")  # Corrected type
+region = gr.inputs.Dropdown(["southwest", "southeast", "northwest", "northeast"], type="value", default="southwest", label="Region")  # Corrected type
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=predict, 
+    inputs=[age, sex, bmi, children, smoker, region], 
+    outputs="number",
+    title="HealthyLife Insurance Charge Prediction"
+)
+
+# Launch with a load balancer
+demo.queue()
+demo.launch(share=False)
+

model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:eda470100f233877b51c0daa8ecd4689bd0eb8657dc029ddc948ee6702fd964e
+size 183916

requirements.txt

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+scikit-learn==1.3.2
+numpy==1.26.4
+gradio==4.1.1