def predict_image(module_name):
if request.path.split("/")[-1] not in cv_module:
return {"error": "Module {} is not available.".format(module_name)}
req_id = request.data.get("id")
global use_gpu, batch_size_dict
img_base64 = request.form.getlist("image")
extra_info = {}
for item in list(request.form.keys()):
extra_info.update({item: request.form.getlist(item)})
for key in extra_info.keys():
if isinstance(extra_info[key], list):
extra_info[key] = utils.base64s_to_cvmats(
eval(extra_info[key][0])["b64s"]) if isinstance(
extra_info[key][0], str
) and "b64s" in extra_info[key][0] else extra_info[key]
file_name_list = []
if img_base64 != []:
for item in img_base64:
ext = item.split(";")[0].split("/")[-1]
if ext not in ["jpeg", "jpg", "png"]:
return {"result": "Unrecognized file type"}
filename = req_id + "_" \
+ utils.md5(str(time.time()) + item[0:20]) \
+ "." \
+ ext
img_data = base64.b64decode(item.split(',')[-1])
file_name_list.append(filename)
with open(filename, "wb") as fp:
fp.write(img_data)
else:
file = request.files.getlist("image")
for item in file:
file_name = req_id + "_" + item.filename
item.save(file_name)
file_name_list.append(file_name)
module = default_module_manager.get_module(module_name)
predict_func_name = cv_module_method.get(module_name, "")
if predict_func_name != "":
predict_func = eval(predict_func_name)
else:
module_type = module.type.split("/")[-1].replace("-", "_").lower()
predict_func = eval("predict_" + module_type)
batch_size = batch_size_dict.get(module_name, 1)
if file_name_list == []:
file_name_list = None
if extra_info == {}:
extra_info = None
results = predict_func(module, file_name_list, req_id, batch_size,
extra_info)
r = {"results": str(results)}
return r
def predict_text(module_name):
if request.path.split("/")[-1] not in nlp_module:
return {"error": "Module {} is not available.".format(module_name)}
req_id = request.data.get("id")
inputs = {}
for item in list(request.form.keys()):
inputs.update({item: request.form.getlist(item)})
files = {}
for file_key in list(request.files.keys()):
files[file_key] = []
for file in request.files.getlist(file_key):
file_name = req_id + "_" + file.filename
files[file_key].append(file_name)
file.save(file_name)
module = default_module_manager.get_module(module_name)
results = predict_nlp(module=module,
input_text=inputs,
req_id=req_id,
batch_size=batch_size_dict.get(module_name, 1),
extra=files)
return results