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model without scheduler

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
+
+# 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",
+#     repo_type="dataset",
+#     folder_path=log_folder,
+#     path_in_repo="data",
+#     every=2
+# )
+
+Load the freshly trained model from disk
+machine_insurance_predictor = joblib.load('model.joblib')
+
+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(
+    ['northeast', 'northwest', 'southeast', 'southwest'],
+    label='Region'
+)
+
+model_output = gr.Label(label="insurance charge")
+
+# 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
+# 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, bmi, children, sex, smoker, region):
+    sample = {
+        'Age': age,
+        'BMI': bmi,
+        'Children': children,
+        'Sex': sex,
+        'Smoker': smoker,
+        'Region': region,
+    }
+    data_point = pd.DataFrame([sample])
+    prediction = machine_insurance_predictor.predict(data_point).tolist()
+
+    # 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]
+
+# Create the gradio interface, make title "HealthyLife Insurance Charge Prediction"
+# Set up UI components for input and output
+demo = gr.Interface(
+    fn=predict_insurance_charge,
+    inputs=[age_input, bmi_input, children_input, sex_input, smoker_input, region_input],
+    outputs=model_output,
+    title="Insurance Charge Predictor",
+    description="This API allows you to predict the companies insurance charges",
+    allow_flagging="auto",
+    concurrency_limit=8
+)
+
+# Launch with a load balancer
+demo.queue()
+demo.launch(share=False)
+
+# 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'

model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:52d04e6329819bf9c7b9f7cd109c69142c8b7794b91d25a75b409322d11cf433
+size 4150

requirements.txt

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+scikit-learn==1.5.1