initial commit

Dockerfile

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+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+RUN useradd -m -u 1000 user
+
+USER user
+
+ENV HOME=/home/user \
+ PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

LICENSE.md

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+The MIT License (MIT)
+
+Copyright (c) 2022 Himesh Samarasekera
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.

Procfile

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+web: uvicorn main:app --host 0.0.0.0 --port $PORT

insurance_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ce5baecaec7aa0475bc4f500227df4d42dadaa242c2c95ac1a930b8281b755d1
+size 633

main.py

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+#!/usr/bin/env python
+# encoding: utf-8
+
+from fastapi import FastAPI, Form, Depends, Request
+from fastapi.encoders import jsonable_encoder
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel
+import pickle
+import json
+
+app = FastAPI()
+
+model_file = open('insurance_model.pkl', 'rb')
+model = pickle.load(model_file, encoding='bytes')
+
+
+class Msg(BaseModel):
+    msg: str
+
+
+class Req(BaseModel):
+    age: int
+    sex: int
+    smoker: int
+    bmi: float
+    children: int
+    region: int
+
+class Resp(BaseModel):
+    age: int
+    sex: str
+    smoker: str
+    bmi: float
+    children: int
+    region: str
+    insurance_cost: float
+
+
+
+@app.get("/")
+async def root():
+    return {"message": "Hello World. Welcome to FastAPI!"}
+
+def form_req(age: str = Form(...), sex: str = Form(...), smoker: str = Form(...),
+             bmi: str = Form(...), children: str = Form(...), region: str = Form(...)):
+    sBmi = bmi.replace(",", ".")
+    return Req(age=int(age), sex=int(sex), smoker=int(smoker), bmi=float(sBmi), children=int(children), region=int(region))
+
+
+@app.get("/path")
+async def demo_get():
+    return {"message": "This is /path endpoint, use a post request to transform the text to uppercase"}
+
+
+@app.post("/path")
+async def demo_post(inp: Msg):
+    return {"message": inp.msg.upper()}
+
+
+@app.get("/path/{path_id}")
+async def demo_get_path_id(path_id: int):
+    return {"message": f"This is /path/{path_id} endpoint, use post request to retrieve result"}
+
+
+@app.get("/predict/{path_id}")
+async def predict(path_id: int):
+    return {"message":  f"This is /predict/{path_id} endpoint, use post request to retrieve result"}
+
+def get_region_name(region_code):
+    region_mapping = {
+        0: "Northeast",
+        1: "Northwest",
+        2: "Southeast",
+        3: "Southwest"
+    }
+    return region_mapping.get(region_code, "Unknown")
+
+@app.post("/predict")
+async def predict(request: Request, requess: Req = Depends(form_req)):
+    '''
+    Predict the insurance cost based on user inputs
+    and render the result to the html page
+    '''
+    age = requess.age
+    sex = requess.sex
+    smoker = requess.smoker
+    bmi = requess.bmi
+    children = requess.children
+    region = requess.region
+    data = []
+
+    data.append(int(age))
+    data.extend([int(sex)])
+    data.extend([float(bmi)])
+    data.extend([int(children)])
+    data.extend([int(smoker)])
+    data.extend([int(region)])
+
+    prediction = model.predict([data])
+    output = round(prediction[0], 2)
+
+    # Merender index.html dengan data hasil prediksi
+    json_compatible_resp_data = jsonable_encoder(Resp(age=requess.age, sex=sex, smoker=int(smoker), 
+                                                      bmi=requess.bmi, children=requess.children, region=get_region_name(requess.region), insurance_cost=output))
+    return JSONResponse(content=json_compatible_resp_data)
+

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f0e2966701ef9bda29bde5d34b776789a0f614589c179d44bf33dbde8b3362f0
+size 630

pyproject.toml

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+[tool.poetry]
+name = "railway-fastapi-template"
+version = "0.1.0"
+description = "This example starts up a FastAPI server"
+authors = ["DeviousLab <deviouslab@gmail.com>"]
+license = "MIT"
+
+[tool.poetry.dependencies]
+python = "^3.10"
+fastapi = "^0.80.0"
+uvicorn = "^0.18.3"
+
+[tool.poetry.dev-dependencies]
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"

requirements.txt

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+anyio>=3.7.1
+click==8.1.7
+fastapi==0.103.2
+h11==0.14.0
+httptools==0.6.0
+idna==3.4
+pydantic==2.4.2
+python-dotenv==1.0.0
+python-multipart==0.0.6
+PyYAML==6.0.1
+sniffio==1.3.0
+starlette>=0.27.0
+typing_extensions==4.8.0
+uvicorn==0.23.2
+watchfiles==0.20.0
+websockets==11.0.3
+scikit-learn
+jinja2