Project: s41467-024-46523-1 | Function: __init__ (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Implements the core neural network prediction algorithm for HOBO-LUBO energy gap calculation using pre-trained models and Avalon fingerprints

**KEY TERMS:** {
  "CHT_fp": "cycloheptatriene fingerprint features",
  "EP_fp": "enophile fingerprint features",
  "CHT_HOBO_model": "highest occupied bond orbital prediction model",
  "EP_LUBO_model": "lowest unoccupied bond orbital prediction model",
  "HOBO_LUBOGAP": "orbital energy gap prediction",
  "pkl": "trained neural network model files"
}

**CONTEXT CODE:** #Tsubasa Ito Chiba University

import sys, os, pickle, warnings
import tkinter as tk
from tkinter.font import Font
import pandas as pd
import numpy as np
from rdkit import Chem
from rdkit.Avalon.pyAvalonTools import  GetAvalonFP 
from rdkit.Chem import Draw

#########################################################################################################################################################
warnings.simplefilter('ignore')
now_path = os.path.dirname(__file__)
sys.path.append(now_path)
fn_CHT = 4096
fn_EP = 4096
CHT_NCD_list = list(range(fn_EP))
EP_list = list(range(fn_EP))
##########################################################################################################################################################
class Window1(tk.Frame):

    def predict(self,txt1,txt2):
        CHT_smiles = str(txt1.get(1.0, tk.END+"-1c"))
        txt1.delete("1.0","end")
        EP_smiles = str(txt2.get(1.0, tk.END+"-1c"))
        txt2.delete("1.0","end")
        self.action = Window2(CHT_smiles, EP_smiles)

    def Exit(self):
        self.master.destroy()

    def __init__(self, master = None):
        super().__init__(master)
        # Setting of Text Area
        self.master.title(r"HOBO_LUBO_Predictor")
        self.master.geometry("300x190")

        # Setting of Fonts
        font1 = Font(family="Arial", size=14, weight=tk.font.BOLD, slant="italic")
        font2 = Font(family="Arial", size=12, weight=tk.font.BOLD)

        # Setting of Label
        label1 = tk.Label(self.master,text="CHT / NCD", font=font1)
        label2 = tk.Label(self.master,text="Enophile", font=font1)
        label1.place (x=110, y=5)
        label2.place (x=110, y=65)

        # Setting of CHT's SMILES Entry
        txt1 = tk.Text(height=2, width=38, bg="bisque", fg="black", font=font2)
        txt1.place(x=15, y=25)

        # Setting of EP's SMILES Entry
        txt2 = tk.Text(height=2, width=38, bg="bisque", fg="black", font=font2)
        txt2.place(x=15, y=85)

        # Setting of Predict Button
        bt_pred = tk.Button(text=u'Predict !!', bg ="pale green", height=1, width=10, font=font2, command=lambda:self.predict(txt1,txt2)) 
        bt_pred.place(x=30, y=140)

        # Setting of Exit Button
        bt_exit = tk.Button(self.master, text='Exit', bg="pale green", height=1, width=10, font=font2, command=lambda:self.Exit())
        bt_exit.place(x=160, y=140)

class Window2():

    @classmethod
    def Exit(cls, master=None):
        master.destroy()

    @classmethod
    def figure(cls,smiles):
        mols = [Chem.MolFromSmiles(smiles)]
        img = Draw.MolToFile(mols[0],'test.png', size=(200, 200))
        return img

    @classmethod
    def Result_window(cls, CHT_smiles, EP_smiles, list):
        # MASKED: Processes and displays HOBO-LUBO energy gap prediction results with molecular structure visualization and numerical outputs
        raise NotImplementedError("This function is masked for testing purposes.")
    
    @classmethod
    def StoF(cls, smiles, fn):
        # MASKED: Converts SMILES molecular representations to 4096-bit Avalon fingerprints for neural network input processing
        raise NotImplementedError("This function is masked for testing purposes.")

    def __init__(self, CHT_smiles, EP_smiles):
        # MASKED: Implements the core neural network prediction algorithm for HOBO-LUBO energy gap calculation using pre-trained models and Avalon fingerprints
        raise NotImplementedError("This function is masked for testing purposes.")

if __name__ =='__main__' :
    root = tk.Tk()
    window = Window1(master=root)
    window.mainloop()

Complete the function implementation replacing "raise NotImplementedError".