initial upload

app.py

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+# 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'
+
+
+
+# Load the freshly trained model from disk
+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_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
+
+age_input = gr.Number(label='Age')
+sex_input = gr.Dropdown(
+    ['female', 'male'],
+    label='Gender'
+)
+bmi_input = gr.Number(label='Body Mass Index (BMI)')
+children_input = gr.Number(label='Number of Children')
+smoker_input = gr.Dropdown(
+    ['no', 'yes'],
+    label='Smoker'
+)
+region_input = gr.Dropdown(
+    ['northeast', 'northwest', 'southeast', 'southwest'],
+    label='Region'
+)
+
+model_output = gr.Label(label="Charges")
+
+
+
+    # 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
+def predict_insurance_charge(age, sex, bmi, children, smoker, region):
+    sample = {
+        'age': age,
+        'sex': sex,
+        'bmi': bmi,
+        'children': children,
+        'smoker': smoker,
+        'region': region,
+    }
+    data_point = pd.DataFrame([sample])
+    prediction = predictor.predict(data_point).tolist()
+
+
+    with scheduler.lock:
+        with log_file.open("a") as f:
+            f.write(json.dumps(
+                {
+                    'age': age,
+                    'sex': sex,
+                    'bmi': bmi,
+                    'children': children,
+                    'smoker': smoker,
+                    'region': region,
+                    'prediction': prediction[0]
+                }
+            ))
+            f.write("\n")
+
+    return prediction[0]
+
+
+
+# Set up UI components for input and output
+# Create the gradio interface, make title "HealthyLife Insurance Charge Prediction"
+
+demo = gr.Interface(
+    fn=predict_insurance_charge,
+    inputs=[age_input,
+            sex_input,
+            bmi_input,
+            children_input,
+            smoker_input,
+            region_input],
+    outputs=model_output,
+    title="HealthyLife Insurance Charge Prediction",
+    description="This API allows you to predict insurance charges for an individual",
+    allow_flagging="auto",
+    concurrency_limit=8
+)
+
+# 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 sha256:4be404dcd680365b139a4307d6f05d71e7b5ed6553303275f53990e17b6135ef
+size 3998

requirements.txt

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+scikit-learn==1.2.2
+numpy==1.25.2
+joblib