def docking():
"""Renders the predict page and makes predictions if the method is POST."""
if request.method == 'GET':
return render_docking()
# Create and modify args
parser = ArgumentParser()
add_predict_args(parser)
args = parser.parse_args([])
# Get arguments
ckpt_id = request.form['checkpointName']
app.config[
'TEMP_FOLDER'] = '/work01/home/yjxu/documents/A1_IIPharam_project/PKUMDL_ChemAI/test_space'
args.test_path = app.config['TEMP_FOLDER']
if request.form['textSmiles'] != '':
smiles = request.form['textSmiles'].split()
elif request.form['drawSmiles'] != '':
smiles = [request.form['drawSmiles']]
else:
print(" GOT HERE")
# Upload data file with SMILES
data = request.files['data']
data_name = secure_filename(data.filename)
data_path = os.path.join(app.config['TEMP_FOLDER'], "raw", data_name)
data.save(data_path)
args.preds_path = os.path.join("prediction", data_name)
args.data_name = data_name.split(".")[0]
app.config['PREDICTIONS_FILENAME'] = os.path.join(
"prediction", data_name)
# Check if header is smiles
possible_smiles = get_header(data_path)[0]
smiles = [possible_smiles
] if Chem.MolFromSmiles(possible_smiles) is not None else []
print(data_path)
# Get remaining smiles
smiles.extend(get_smiles(data_path))
print(smiles)
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')
args.checkpoint_paths = model_paths
if gpu is not None:
if gpu == 'None':
args.no_cuda = True
else:
args.gpu = int(gpu)
modify_predict_args(args)
# Run predictions
preds = make_predictions(args)
if all(p is None for p in preds):
return render_docking(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_docking(
predicted=True,
smiles=smiles,
num_smiles=len(smiles),
show_more=0,
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)
def predict_old():
"""Renders the predict page and makes predictions if the method is POST.
@@@
#### example
```
curl -X POST url -F 'checkpointName=<checkpointName option>' -F 'textSmiles=<smiles>'
curl -X POST url -F 'checkpointName=<checkpointName option>' -F 'textSmiles=' -F 'drawSmiles=<drawSmilesInput>'
curl -X POST url -F 'checkpointName=<checkpointName option>' -F 'textSmiles=' -F 'drawSmiles=' -F 'data=@<fileSmilesInput>'
```
#### return
- ##### predict.html
@@@
"""
if request.method == 'GET':
return render_predict()
ckpt_id = request.form['checkpointName']
app.config['TEMP_FOLDER'] = './web/web_log'
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))
# Create and modify args
parser = ArgumentParser()
add_predict_args(parser)
args = parser.parse_args([])
args.test_path = app.config['TEMP_FOLDER']
args.data_name = data_name.split(".")[0]
gpu = request.form.get('gpu')
model = db.get_models(ckpt_id)[0]
model_path = os.path.join(app.config['CHECKPOINT_FOLDER'],
f'{model["id"]}.pt')
task_names = load_task_names(model_path)
num_tasks = len(task_names)
preds_path = os.path.join(app.config['TEMP_FOLDER'], "predict",
app.config['PREDICTIONS_FILENAME'])
args.preds_path = preds_path
args.checkpoint_paths = model_path
if gpu is not None:
if gpu == 'None':
args.no_cuda = True
else:
args.gpu = int(gpu)
train_args = load_args(model_path)
for key, value in vars(train_args).items():
if not hasattr(args, key):
setattr(args, key, value)
modify_predict_args(args)
# Run predictions
preds = make_predictions(args)
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
]
print(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)
def generate():
"""Renders the generate page
Content-Type: application/json
args:
{"checkpointName": "igm_char_rnn, igm_atom_rnn, igm_frag_vae","n_sample_per_model" : "1000", "filter": "None" or "MW_0_1, logP_0_4, SAS_0_5, QED_0_1" }
return:
json {"igm_char_rnn": "path_to_charrnn_file", "igm_atom_rnn": "path_to_atomrnn_file","igm_frag_vae": "path_to_fragvae_file", "filtered": "None" or "path_to_filter_file"}
or
generate.html
@@@
#### example
```
curl -H "Content-Type: application/json" -X POST --data 'args'
```
"""
if request.method == 'GET':
return render_generate()
# Get arguments
print(request.is_json)
content = request.get_json()
print(content)
model_names = content["checkpointName"]
num_sample = content["n_sample_per_model"]
isfilter = content["filter"]
return jsonify({
"igm_char_rnn": "path_to_charrnn_file",
"igm_atom_rnn": "path_to_atomrnn_file",
"igm_frag_vae": "path_to_fragvae_file",
"filtered": "None or path_to_filter_file"
})
ckpt_id = request.form['checkpointName']
app.config['TEMP_FOLDER'] = 'tmp_log_web/generate'
args.test_path = app.config['TEMP_FOLDER']
if request.form['textSmiles'] != '':
smiles = request.form['textSmiles'].split()
elif request.form['drawSmiles'] != '':
smiles = [request.form['drawSmiles']]
else:
print(" GOT HERE")
# Upload data file with SMILES
data = request.files['data']
data_name = secure_filename(data.filename)
data_path = os.path.join(app.config['TEMP_FOLDER'], "raw", data_name)
data.save(data_path)
args.preds_path = os.path.join("prediction", data_name)
args.data_name = data_name.split(".")[0]
app.config['PREDICTIONS_FILENAME'] = os.path.join(
"prediction", data_name)
# Check if header is smiles
possible_smiles = get_header(data_path)[0]
smiles = [possible_smiles
] if Chem.MolFromSmiles(possible_smiles) is not None else []
print(data_path)
# Get remaining smiles
smiles.extend(get_smiles(data_path))
print(smiles)
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')
args.checkpoint_paths = model_paths
if gpu is not None:
if gpu == 'None':
args.no_cuda = True
else:
args.gpu = int(gpu)
modify_predict_args(args)
# Run predictions
preds = make_predictions(args)
if all(p is None for p in preds):
return render_generate(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_generate(
predicted=True,
smiles=smiles,
num_smiles=len(smiles),
show_more=0,
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)
def predict():
"""Renders the predict page and makes predictions if the method is POST.
Content-Type: multipart/form-data
@@@
#### example
```
curl -X POST url -F 'checkpointName=<checkpointName option>' -F 'textSmiles=<smiles>'
curl -X POST url -F 'checkpointName=<checkpointName option>' -F 'textSmiles=' -F 'drawSmiles=<drawSmilesInput>'
curl -X POST url -F 'checkpointName=<checkpointName option>' -F 'textSmiles=' -F 'drawSmiles=' -F 'data=@<fileSmilesInput>' or -F "usejson=True"
```
#### return
- ##### predict.html
- ##### json data if usejson is True
@@@
"""
if request.method == 'GET':
return render_predict()
# Create and modify args
parser = ArgumentParser()
add_predict_args(parser)
args = parser.parse_args([])
# Get arguments
ckpt_id = request.form['checkpointName']
print(ckpt_id)
args.test_path = app.config['TEMP_FOLDER']
if request.form['textSmiles'] != '':
smiles = request.form['textSmiles'].split()
elif request.form['drawSmiles'] != '':
smiles = [request.form['drawSmiles']]
else:
print(" GOT HERE")
# Upload data file with SMILES
data = request.files['data']
# print(data)
data_name = secure_filename(data.filename)
data_path = os.path.join(app.config['TEMP_FOLDER'], "raw", data_name)
data.save(data_path)
args.preds_path = os.path.join("prediction", data_name)
args.data_name = data_name.split(".")[0]
app.config['PREDICTIONS_FILENAME'] = args.preds_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 []
# print(data_path)
# Get remaining smiles
smiles.extend(get_smiles(data_path))
# print(smiles)
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')
args.checkpoint_paths = model_paths
if gpu is not None:
if gpu == 'None':
args.no_cuda = True
else:
args.gpu = int(gpu)
modify_predict_args(args)
# Run predictions
preds = make_predictions(args)
if request.form['usejson']:
preds_arr = np.array(preds)
jsondata = {}
jsondata["smiles"] = smiles
for i, task_name in enumerate(task_names):
jsondata[task_name] = list(preds_arr[:, i])
return jsonify(jsondata)
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=len(smiles),
show_more=0,
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)