evaluations/validating.py

import re
from core.conversion import noop_logger

def evaluate(llm, zip, readmes, log_fn=noop_logger):
  log_fn("TITLE", "\nLooking for examples for running the model...")
  overall = "No"
  patterns = {
      'tensorflow': [
          r'tf\.keras\.models\.load_model',  # TensorFlow model loading
          r'tf\.saved_model\.load',
          r'\.predict',  # Running inference
      ],
      'pytorch': [
          r'torch\.load',  # PyTorch model loading
          r'torch\.jit\.load',  # PyTorch JIT model loading
          r'\.eval',  # Running inference
      ]
  }

  files = [file_path for file_path in zip.namelist() if ((file_path.endswith(".py") | file_path.endswith(".ipynb")))]
  for file_path in files:
      code = zip.open(file_path).read().decode("utf-8")
      for framework, regex_list in patterns.items():
          for pattern in regex_list:
              if re.search(pattern, code):
                  log_fn("LOG", f"Found code for evaluating a model in {framework} framework in file: {file_path}")
                  overall = "Yes"

  for readme in readmes:
    if (readme):
        if ((len(re.findall("testing", readme)) > 0)):
                log_fn("LOG", "Found information about evaluations in readme")
                overall = "Yes"

  if (overall == "No"):
    log_fn("ERROR", "Found no code for evaluating the model.")
  return overall