text: """
  The entry point for your prediction algorithm.
  """
  
  from __future__ import annotations
  import argparse
  import csv
  import itertools
  from pathlib import Path
  import pprint
  from typing import Any
  import zipfile
  
  from Bio.PDB.PDBParser import PDBParser
  from Bio.PDB.vectors import calc_dihedral
  from Bio.PDB.Structure import Structure
  import temppathlib
  
  import numpy as np
  import pandas as pd
  import torch
  import torch.nn as nn
  import pickle
  
  
  class Model(nn.Module):
  
      def __init__(self, n_in, layers, p=0.5, out_sz=1):
          super().__init__()
          self.drop_first_layer = nn.Dropout(p)
  
          layerlist = []
  
          for i in layers:
              layerlist.append(nn.Linear(n_in, i))
              layerlist.append(nn.ReLU(inplace=True))
              layerlist.append(nn.BatchNorm1d(i))
              layerlist.append(nn.Dropout(p))
              n_in = i
          layerlist.append(nn.Linear(layers[-1], out_sz))
  
          self.layers = nn.Sequential(*layerlist)
  
      def forward(self, X):
          # X = self.drop_first_layer(X)
          x = self.layers(X)
          return x
  
  def predict(pdb_file: Path) -> float:
      """
      The function that puts it all together: parsing the PDB file, generating
      features from it and performing inference with the ML model.
      """
  
      # parse PDB
      parser = PDBParser()
      structure = parser.get_structure(pdb_file.stem, pdb_file)
  
      # featurize + perform inference
      features = featurize(structure)
      predicted_solubility = ml_inference(features)
  
      return predicted_solubility
  
  
  def featurize(structure: Structure) -> list[Any]:
      """
      Calculates 3D ML features from the `structure`.
      """
  
      # get all the residues
      residues = [res for res in structure.get_residues()]
  
      # calculate some random 3D features (you should be smarter here!)
      protein_length = residues[1]["CA"] - residues[-2]["CA"]
      angle = calc_dihedral(
          residues[1]["CA"].get_vector(),
          residues[2]["CA"].get_vector(),
          residues[-3]["CA"].get_vector(),
          residues[-2]["CA"].get_vector(),
      )
      # create the feature vector
      features = [protein_length, angle]
  
      return features
  
  
  def ml_inference(features: list[Any]) -> float:
      """
      This would be a function where you normalize/standardize your features and
      then feed them to your trained ML model (which you would load from a file).
      """
  
      # this is my stupid manual ML model
      if features[0] > 15.0 and features[1] > 0.5:
          return 60
      elif features[0] > 30.0 and features[1] > 1.5:
          return 80
  
      return 20
  
  
  if __name__ == "__main__":
      # use_cuda = torch.cuda.is_available()
      # device = torch.device("cpu")
      # # torch.backends.cudnn.benchmark = True
      #
      # model1 = Model(1225, [1024, 1024, 512], p=0.6, out_sz=1).to(device)
      # model1.load_state_dict(torch.load("MLP_r_0.493.pt", map_location=torch.device("cpu")))
      # model1.eval()
  
      # Features PFAM
      df_pfam = pd.read_csv('data/features_pfam.csv')
      # PhysProp features
      df_pp = pd.read_csv('data/features_phys_prop.csv')
      # Protdes features
      df_protdes = pd.read_csv('data/features_protdes_combined.csv')
  
      df_ck = pd.read_csv('data/features_CKSAAP.csv')
  
      df_test = pd.read_csv('data/test.csv')
  
      # Merging the data
      df_test = pd.merge(df_test, df_pfam, on='id')
      df_test = pd.merge(df_test, df_pp, on='id')
      df_test = pd.merge(df_test, df_protdes, on='id')
      df_test = pd.merge(df_test, df_ck, on='id')
  
      df_test = df_test.fillna(0)
  
      X_test = df_test.drop(['seq', 'id'], axis=1)
      rf = pickle.load(open('RF.pkl', 'rb'))
      # X_test = sc_X.transform(X_test)
      # X_test = torch.tensor(np.array(X_test), dtype=torch.float)
  
      # with torch.no_grad():
      #     y_test_pred = model1(X=X_test.to(device))
      y_test_pred = rf.predict(X_test)
  
      df_test["solubility"] = y_test_pred
      predictions_df = df_test[["id", "solubility"]]
      predictions_df.columns = ["protein", "solubility"]
  
  
      # set up argument parsing
      parser = argparse.ArgumentParser()
      parser.add_argument("--infile", type=str, default="data/test.zip")
      args = parser.parse_args()
  
      predictions = []
      # use a temporary directory so we don't pollute our repo
      # with temppathlib.TemporaryDirectory() as tmpdir:
      #     # unzip the file with all the test PDBs
      #     with zipfile.ZipFile(args.infile, "r") as zip_:
      #         zip_.extractall(tmpdir.path)
  
      for id, sol in zip(list(predictions_df["protein"]), list(predictions_df["solubility"])):
          predictions.append({"protein": id, "solubility": sol})
          # iterate over all test PDBs and generate predictions
          # for test_pdb in tmpdir.path.glob("*.pdb"):
          #     predictions.append({"protein": test_pdb.stem, "solubility": predict(test_pdb)})
  
      # save to csv file, this will be used for benchmarking
      predictions_df.to_csv("predictions.csv", index=False)
      # outpath = "predictions.csv"
      # with open(outpath, "w") as fh:
      #     writer = csv.DictWriter(fh, fieldnames=["protein", "solubility"])
      #     writer.writeheader()
      #     writer.writerows(predictions)
  
      # print predictions to screen
      pp = pprint.PrettyPrinter(indent=4)
      pp.pprint(predictions)
  