Upload 4 files

app.py

@@ -0,0 +1,112 @@
+# Import the libraries
+import os
+import uuid
+import joblib
+import json
+
+import gradio as gr
+import pandas as pd
+
+from huggingface_hub import CommitScheduler
+from pathlib import Path
+
+
+
+# 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'
+import train
+train
+
+
+
+
+# Load the freshly trained model from disk
+saved_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="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_charge(age, bmi, children, sex, smoker, region ):
+    sample = {
+
+        'age': age,
+        'bmi': bmi,
+        'children': children,
+        'sex': sex,
+        'smoker': smoker,
+        'region': region,
+
+    }
+    data_point = pd.DataFrame([sample])
+    prediction = saved_model.predict(data_point).tolist()
+    # if prediction is less than zero  assign zero
+    if prediction[0] < 0:
+        prediction[0] = 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
+
+# age =  gr.Number(label="age")
+age =  gr.Slider(1, 100, step =1, minimum =1, maximum = 100,   label="age", info='Age between 1 and 100')
+bmi = gr.Number(label="bmi")
+# children = gr.Number(label='children')
+children = gr.Slider(label='children', step =1, minimum =0, maximum = 10,  info = 'Enter number of children')
+sex = gr.Dropdown(label='sex', choices=['male', 'female'])
+smoker = gr.Dropdown(label='smoker', choices=['yes', 'no'])
+region = gr.Dropdown(label='region', choices =['southwest', 'southeast', 'northwest', 'northeast'])
+charge = gr.Number(label="Prediction")
+
+
+
+# Create the gradio interface, make title "HealthyLife Insurance Charge Prediction"
+demo = gr.Interface(
+    fn = predict_charge,
+    inputs = [age, bmi, children, sex, smoker, region,],
+    outputs = charge,
+    title = 'HealthyLife Insurance Charge Prediction',
+    description = 'Calculate charges')
+
+
+# Launch with a load balancer
+demo.queue()
+demo.launch(share=False)

insurance.csv

@@ -0,0 +1,1339 @@
+index,age,sex,bmi,children,smoker,region,charges
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requirements.txt

@@ -0,0 +1,3 @@
+numpy==1.26.4
+scikit-learn==1.2.2
+joblib

train.py

@@ -0,0 +1,53 @@
+
+import math
+import pandas as pd
+import numpy as np
+
+from datasets import load_dataset
+import sklearn
+import joblib
+
+from sklearn.datasets import fetch_openml
+
+from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder
+from sklearn.compose import make_column_transformer
+from sklearn.impute import SimpleImputer
+from sklearn.pipeline import Pipeline
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.pipeline import make_pipeline
+from sklearn.model_selection import train_test_split, RandomizedSearchCV
+from sklearn.linear_model import LinearRegression
+
+data_df = pd.read_csv('insurance.csv')
+
+data_df = data_df.drop(columns=['index'])
+categorical_features = ['sex', 'smoker', 'region']
+numerical_features = ['age', 'bmi', 'children', ]
+target = 'charges'
+
+print("Creating data subsets")
+
+X = data_df[numerical_features + categorical_features]
+y = data_df[target]
+
+Xtrain, Xtest, ytrain, ytest = train_test_split( X, y, test_size=0.2, random_state=42)
+
+numerical_pipeline = Pipeline([('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())])
+categorical_pipeline = Pipeline([('imputer', SimpleImputer(strategy='most_frequent')),('onehot', OneHotEncoder(handle_unknown='ignore'))])
+
+preprocessor = make_column_transformer((numerical_pipeline, numerical_features), (categorical_pipeline, categorical_features))
+model_linear_regression = LinearRegression(n_jobs=-1)
+model_pipeline = make_pipeline(preprocessor,  model_linear_regression )
+
+print("Estimating Model Pipeline")
+model_pipeline.fit(Xtrain, ytrain)
+
+print("Logging Metrics")
+print(f"R-squared: {r2_score(ytest, model_pipeline.predict(Xtest))}")
+
+print("Serializing Model")
+
+saved_model_path = "model.joblib"
+
+joblib.dump(model_pipeline, saved_model_path)