Upload 5 files

app (2).py

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+# Import the libraries
+
+import os 
+import uuid
+import joblib
+import json
+
+import gradio as gr
+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'
+
+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_charge(age, sex, bmi, children, somker, region):
+    smaple = {'age': age,
+              'bmi': bmi,
+              'children': children,
+              'sex': sex,
+              'smoker': smoker,
+              'region': region,
+              'prediction': prediction[0]
+             }
+    data_point = pd.DataFrame([sample])
+    prediction = insurance_charge_predicter.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(["southeast","southwest", "northwest", "northeast"], label = "region")
+model_output = gr.Label( label = " Insurance Chaeges")
+
+# Create the gradio interface, make title "HealthyLife Insurance Charge Prediction"
+demo = gr.Interface(
+    fn=predict_insurance_charge,
+    inputs=[age_input, bmi_input, children_input, sex_input, smoker_input, region_input],
+    outputs = model_output,
+    title = "Healthy Insurence Candidate Perdiction",
+    description = "This API will predict and estimate isurance charges based on candidate's attributes"
+    examples = [[33,33.44,5,"male","no", "southeast"],
+                [40,38.20,2,"female","no", "northwest"],
+                [52,36.20,0,"male","no", "northwest"]],
+    concurrency_limit = 16
+
+)
+
+# 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
+0,19,female,27.9,0,yes,southwest,16884.924
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model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1b348d9fc0de7830cd359a7e9e8faa8e83b8d28339bdeb597cca5f339c4e8428
+size 3950

requiermentrts.txt

@@ -0,0 +1,2 @@
+
+scikit-learn==1.0.2

train.py

@@ -0,0 +1,58 @@
+import pandas as pd
+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
+
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error, r2_score
+
+# Read the data
+data_df = pd.read_csv("insurance.csv")
+
+target = 'charges'
+numeric_features = ['age', 'bmi', 'children']
+categorical_features = ['sex', 'smoker','region']
+
+print("Creating data subsets")
+
+X = data_df[numeric_features + categorical_features]
+y = data_df[target]
+
+Xtrain, Xtest, ytrain, ytest = train_test_split(
+    X, y,
+    test_size=0.2,
+    random_state=42
+)
+
+preprocessor = make_column_transformer(
+    (StandardScaler(), numeric_features),
+    (OneHotEncoder(handle_unknown='ignore'), categorical_features)
+)
+
+model_linear_regression = LinearRegression(n_jobs=-1)
+
+print("Estimating Model Pipeline")
+
+model_pipeline = make_pipeline(
+    preprocessor,
+    model_linear_regression
+)
+
+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)
+