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from .utils import log | |
import re | |
def evaluate(verbose, llm, zip, readme): | |
log(verbose, "LOG", "\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'model\.predict', # Running inference | |
r'model\(.+\)' # Direct model invocation for inference | |
], | |
'pytorch': [ | |
r'torch\.load', # PyTorch model loading | |
r'torch\.jit\.load', # PyTorch JIT model loading | |
r'model\.eval', # Running inference | |
r'model\(.+\)' # Direct model invocation for 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(verbose, "LOG", f"Found code for evaluating a model in {framework} framework in file: {file_path}") | |
overall = "Yes" | |
if (readme): | |
if ((len(re.findall("testing", readme)) > 0)): | |
log(verbose, "LOG", "Found information about evaluations in readme") | |
overall = "Yes" | |
return overall |