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@app.post("/predict", response_model=PredictResponse) | |
def predict(input: PredictRequest): | |
return PredictResponse(data=[0.0]) |
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version: "3" | |
services: | |
fastapi-ml-quickstart: | |
build: . | |
ports: | |
- 8000:8000 | |
entrypoint: ["pytest"] |
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# This GitHub action describes the continuous integration workflow for pull requests to master. | |
name: ci | |
on: | |
pull_request: | |
branches: | |
- master | |
push: | |
branches: |
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version: "3" | |
services: | |
fastapi-ml-quickstart: | |
build: . | |
ports: | |
- 8000:8000 |
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FROM ubuntu:19.10 | |
COPY ./api /api/api | |
COPY requirements.txt /requirements.txt | |
RUN apt-get update \ | |
&& apt-get install python3-dev python3-pip -y \ | |
&& pip3 install -r requirements.txt | |
ENV PYTHONPATH=/api |
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import pandas as pd | |
from fastapi import File, UploadFile, HTTPException | |
from starlette.status import HTTP_422_UNPROCESSABLE_ENTITY | |
@app.post("/predict_csv") | |
def predict_csv(csv_file: UploadFile = File(...), model: Model = Depends(get_model)): | |
try: | |
df = pd.read_csv(csv_file.file).astype(float) |
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from itertools import product | |
from starlette.status import HTTP_422_UNPROCESSABLE_ENTITY | |
@pytest.mark.parametrize( | |
"n_instances, test_data_n_features", | |
product(range(1, 10), [n for n in range(1, 20) if n != n_features]), | |
) | |
def test_predict_with_wrong_input( |
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import pytest | |
import random | |
from starlette.testclient import TestClient | |
from starlette.status import HTTP_200_OK | |
from api.ml.model import n_features | |
@pytest.mark.parametrize("n_instances", range(1, 10)) |
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import pytest | |
from starlette.testclient import TestClient | |
from ..main import app | |
from ..ml.model import get_model | |
from .mocks import MockModel | |
def get_model_override(): |
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import numpy as np | |
class MockModel: | |
def __init__(self, model_path: str = None): | |
self._model_path = None | |
self._model = None | |
def predict(self, X: np.ndarray) -> np.ndarray: | |
n_instances = len(X) |
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