Delete app.py

app.py

@@ -1,112 +0,0 @@
-# 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 y 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)