def predict(addr, model_name, input_lin, batch=False):
url = "http://%s/%s/predict" % (addr, model_name)
if batch:
req_json = json.dumps({'input_batch': [[x.val for x in input_lin]]})
else:
req_json = json.dumps({'input': [input_lin.val]})
headers = {'Content-type': 'application/json'}
r = requests.post(url, headers=headers, data=req_json)
input_lin.make_prediction()
return Lineage.add_node(input_lin, model_name, json.loads(r.text)["output"][0])
def predict(addr, model_name, input_lin, batch=False):
url = "http://%s/%s/predict" % (addr, model_name)
if batch:
req_json = json.dumps({'input_batch': [[x.val for x in input_lin]]})
else:
req_json = json.dumps({'input': [input_lin.val]})
headers = {'Content-type': 'application/json'}
r = requests.post(url, headers=headers, data=req_json)
# print(json.loads(r.text))
str_r = json.loads(r.text)["output"].replace("[", "").replace("]",
"").split()
vals = [float(i) for i in str_r]
new_lineage_objs = [
Lineage.add_node(input_lin[i], model_name, vals[i])
for i in range(len(input_lin))
]
return new_lineage_objs