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Copy pathtest_pydantic.py
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# Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from fastapi.testclient import TestClient
from pydantic import BaseModel
from litserve import LitAPI, LitServer
from litserve.utils import wrap_litserve_start
class PredictRequest(BaseModel):
input: float
class PredictResponse(BaseModel):
output: float
class SimpleLitAPI(LitAPI):
def setup(self, device):
self.model = lambda x: x**2
def decode_request(self, request: PredictRequest) -> float:
return request.input
def predict(self, x):
return self.model(x)
def encode_response(self, output: float) -> PredictResponse:
return PredictResponse(output=output)
def test_pydantic():
server = LitServer(SimpleLitAPI(), accelerator="cpu", devices=1, timeout=5)
with wrap_litserve_start(server) as server, TestClient(server.app) as client:
response = client.post("/predict", json={"input": 4.0})
assert response.json() == {"output": 16.0}
class NoAnnotationLitAPI(LitAPI):
def setup(self, device):
pass
def predict(self, request):
return {"output": request["input"] ** 2}
def test_swagger_request_body_without_annotation():
server = LitServer(NoAnnotationLitAPI(), accelerator="cpu", devices=1, timeout=5)
schema = server.app.openapi()
predict_post = schema["paths"]["/predict"]["post"]
assert "requestBody" in predict_post, "Swagger must expose a requestBody for /predict"
assert "application/json" in predict_post["requestBody"]["content"]