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)