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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'

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="healthylife-insurance-charge-log",  # 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_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 = Insurance_charge_predictor.predict(data_point).tolist()

    # 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_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="Insurace Charges")

# Create the gradio interface, make title "HealthyLife Insurance Charge Prediction"

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 insurnace charges based on customer attributes",
    examples=[[33,33.44,5,'male','no','southeast'],
              [58,25.177,0,'male','no','northeast'],
              [52,38.380,2,'female','no','northeast']],
    concurrency_limit=16
)


# Launch with a load balancer
demo.queue()
demo.launch(share=False)

	# 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'

	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="healthylife-insurance-charge-log", # 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_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 = Insurance_charge_predictor.predict(data_point).tolist()

	# 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_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="Insurace Charges")

	# Create the gradio interface, make title "HealthyLife Insurance Charge Prediction"

	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 insurnace charges based on customer attributes",
	examples=[[33,33.44,5,'male','no','southeast'],
	[58,25.177,0,'male','no','northeast'],
	[52,38.380,2,'female','no','northeast']],
	concurrency_limit=16
	)


	# Launch with a load balancer
	demo.queue()
	demo.launch(share=False)