def download_checkpoint(checkpoint: int):
"""
Downloads a zip of model .pt files.
:param checkpoint: The name of the checkpoint to download.
"""
ckpt = db.query_db(f'SELECT * FROM ckpt WHERE id = {checkpoint}', one = True)
models = db.get_models(checkpoint)
model_data = io.BytesIO()
with zipfile.ZipFile(model_data, mode='w') as z:
for model in models:
model_path = os.path.join(app.config['CHECKPOINT_FOLDER'], f'{model["id"]}.pt')
z.write(model_path, os.path.basename(model_path))
model_data.seek(0)
return send_file(
model_data,
mimetype='application/zip',
as_attachment=True,
attachment_filename=f'{ckpt["ckpt_name"]}.zip',
cache_timeout=-1
)
def predict():
"""Renders the predict page and makes predictions if the method is POST."""
if request.method == 'GET':
return render_predict()
# Get arguments
ckpt_id = request.form['checkpointName']
if request.form['textSmiles'] != '':
smiles = request.form['textSmiles'].split()
elif request.form['drawSmiles'] != '':
smiles = [request.form['drawSmiles']]
else:
# Upload data file with SMILES
data = request.files['data']
data_name = secure_filename(data.filename)
data_path = os.path.join(app.config['TEMP_FOLDER'], data_name)
data.save(data_path)
# Check if header is smiles
possible_smiles = get_header(data_path)[0]
smiles = [possible_smiles
] if Chem.MolFromSmiles(possible_smiles) is not None else []
# Get remaining smiles
smiles.extend(get_smiles(data_path))
models = db.get_models(ckpt_id)
model_paths = [
os.path.join(app.config['CHECKPOINT_FOLDER'], f'{model["id"]}.pt')
for model in models
]
task_names = load_task_names(model_paths[0])
num_tasks = len(task_names)
gpu = request.form.get('gpu')
train_args = load_args(model_paths[0])
# Build arguments
arguments = [
'--test_path', 'None', '--preds_path',
os.path.join(app.config['TEMP_FOLDER'],
app.config['PREDICTIONS_FILENAME']), '--checkpoint_paths',
*model_paths
]
if gpu is not None:
if gpu == 'None':
arguments.append('--no_cuda')
else:
arguments += ['--gpu', gpu]
# Handle additional features
if train_args.features_path is not None:
# TODO: make it possible to specify the features generator if trained using features_path
arguments += [
'--features_generator', 'rdkit_2d_normalized',
'--no_features_scaling'
]
elif train_args.features_generator is not None:
arguments += ['--features_generator', *train_args.features_generator]
if not train_args.features_scaling:
arguments.append('--no_features_scaling')
# Parse arguments
args = PredictArgs().parse_args(arguments)
# Run predictions
preds = make_predictions(args=args, smiles=smiles)
if all(p is None for p in preds):
return render_predict(errors=['All SMILES are invalid'])
# Replace invalid smiles with message
invalid_smiles_warning = 'Invalid SMILES String'
preds = [
pred if pred is not None else [invalid_smiles_warning] * num_tasks
for pred in preds
]
return render_predict(
predicted=True,
smiles=smiles,
num_smiles=min(10, len(smiles)),
show_more=max(0,
len(smiles) - 10),
task_names=task_names,
num_tasks=len(task_names),
preds=preds,
warnings=["List contains invalid SMILES strings"]
if None in preds else None,
errors=["No SMILES strings given"] if len(preds) == 0 else None)