# 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'