Create app.py

app.py

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+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 logistic 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",
+    repo_type="dataset",
+    folder_path=log_folder,
+    path_in_repo="data",
+    every=2
+)
+
+# Define the predict function that runs when 'Submit' is clicked or when a API request is made
+def predict_insurance_charge(age, sex, bmi, children, smoker, region):
+    sample = {
+        'age': age,
+        'bmi': bmi,
+        'children': children,
+        'sex': sex,
+        'smoker': smoker,
+        'region': region
+    }
+
+    data_point = pd.DataFrame([sample])
+    prediction = insurance_charge_predictor.predict(data_point).tolist()
+
+    # While the prediction is made, log both the inputs and outputs to a local 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 Charges")
+
+# Create the interface
+demo = gr.Interface(
+    fn=predict_insurance_charge,
+    inputs=[age_input, bmi_input, children_input,sex_input, smoker_input, region_input],
+    outputs=model_output,
+    title="HealthyLife Insurance Charge Prediction",
+    description="This API allows you to predict the estimating insurance charges based on customer attributes",
+    examples=[[33,33.44,5,'male','no','southeast'],
+              [58,25.175,0,'male','no','northeast'],
+              [52,38.380,2,'female','no','northeast']],
+    concurrency_limit=16
+)
+
+# Launch with a load balancer
+demo.queue()
+demo.launch(share=False)