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# Iris Prediction API
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This repo contains an Iris prediction server.
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To get start the application, run:
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```
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uvicorn app.main:app --host 0.0.0.0 --port 8000
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```
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## Fetching Predictions
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If the API server is running at `http://localhost:8000`, then the following should work in a local Python session:
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```text
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>>> import requests
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>>> response = requests.post(
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... "http://localhost:8000/prediction",
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... json={
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... "sepal_width": 1,
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... "sepal_length": 1,
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... "petal_length": 1,
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... "petal_width": 1,
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... },
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... )
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>>> response.status_code
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200
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>>> response.json()
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{'flower_type': 0}
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```
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[build-system]
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requires = ["setuptools"]
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[metadata]
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name = app
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version = 0.1.0
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[options]
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package_dir =
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app = app
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install_requires =
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anyio==3.6.2
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attrs==22.2.0
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black==23.3.0
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certifi==2022.12.7
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click==8.1.3
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colorama==0.4.6
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exceptiongroup==1.1.1
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fastapi==0.88.0
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h11==0.14.0
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httpcore==0.16.3
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httptools==0.5.0
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httpx==0.23.3
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idna==3.4
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iniconfig==2.0.0
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joblib==1.2.0
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mypy-extensions==1.0.0
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numpy==1.24.2
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packaging==23.0
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pandas==1.5.3
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pathspec==0.11.1
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platformdirs==3.2.0
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pluggy==1.0.0
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pydantic==1.10.7
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pytest==7.2.2
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python-dateutil==2.8.2
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python-dotenv==1.0.0
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pytz==2023.3
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pyyaml==6.0
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rfc3986[idna2008]==1.5.0
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scikit-learn==1.2.2
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scipy==1.9.3
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six==1.16.0
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sniffio==1.3.0
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starlette==0.22.0
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threadpoolctl==3.1.0
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tomli==2.0.1
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typing-extensions==4.5.0
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uvicorn[standard]==0.20.0
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watchfiles==0.19.0
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websockets==11.0
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[options.package_data]
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app = models/*
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