# +++
import os
import uuid
import joblib
import json

# IMPORTANT: I already installed the package "gradio" in my current Virtual Environment (VEnvDSDIL_gpu_Py3.12) as:  pip install -q gradio_client
#            Do NOT install "gradio_client" package again in Anaconda otherwise it will mess up the package.
import gradio as gr
import pandas as pd

# must install the package "huggingface_hub" first in the current python Virtual Environment, with pip, not with conda, as follows
# pip install huggingface_hub
# i.e., in the command line interface within the activated Virtual Environment:
#  (VEnvDSDIL_gpu_Py3.12) epalvarez@DSDILmStation01:~ $ pip install huggingface_hub
from huggingface_hub import CommitScheduler
from pathlib import Path

# path = Path.cwd()

log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
log_folder = log_file.parent

hf_token = os.environ.get('HF_TOKEN')
print(hf_token)

# Scheduler will log every 2 API calls:
scheduler = CommitScheduler(
    repo_id="machine-failure-logs",
    repo_type="dataset",
    folder_path=log_folder,
    path_in_repo="data",
    every=2
)

machine_failure_predictor = joblib.load('model_mf.joblib')

air_temperature_input = gr.Number(label='Air temperature [K]')
process_temperature_input = gr.Number(label='Process temperature [K]')
rotational_speed_input = gr.Number(label='Rotational speed [rpm]')
torque_input = gr.Number(label='Torque [Nm]')
tool_wear_input = gr.Number(label='Tool wear [min]')
type_input = gr.Dropdown(
    ['L', 'M', 'H'],
    label='Type'
)

model_output = gr.Label(label="Machine failure")

def predict_machine_failure(air_temperature, process_temperature, rotational_speed, torque, tool_wear, type):
    sample = {
        'Air temperature [K]': air_temperature,
        'Process temperature [K]': process_temperature,
        'Rotational speed [rpm]': rotational_speed,
        'Torque [Nm]': torque,
        'Tool wear [min]': tool_wear,
        'Type': type
    }
    data_point = pd.DataFrame([sample])
    prediction = machine_failure_predictor.predict(data_point).tolist()

    # Each time we get a prediction we will determine if we should log it to a hugging_face dataset according to the schedule definition outside this function
    with scheduler.lock:
        with log_file.open("a") as f:
            f.write(json.dumps(
                {
                    'Air temperature [K]': air_temperature,
                    'Process temperature [K]': process_temperature,
                    'Rotational speed [rpm]': rotational_speed,
                    'Torque [Nm]': torque,
                    'Tool wear [min]': tool_wear,
                    'Type': type,
                    'prediction': prediction[0]
                }
            ))
            f.write("\n")
            
    return prediction[0]

demo = gr.Interface(
    fn=predict_machine_failure,
    inputs=[air_temperature_input, process_temperature_input, rotational_speed_input, 
            torque_input, tool_wear_input, type_input],
    outputs=model_output,
    title="Machine Failure Predictor",
    description="This API allows you to predict the machine failure status of an equipment",
    allow_flagging="auto",
    concurrency_limit=8
)

demo.queue()
demo.launch(share=False)