# 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 and 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)