initial commit

.dockerfile

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

app.py

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+from fastapi import FastAPI, Form, Depends, Request
+from fastapi.encoders import jsonable_encoder
+from fastapi.responses import JSONResponse
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import pickle
+
+app = FastAPI()
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Load the pre-trained model
+with open('insurance_model.pkl', 'rb') as model_file:
+    model = pickle.load(model_file, encoding='bytes')
+
+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))
+
+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)
+
+    sex = "Male" if requess.sex == 1 else "Female"
+    smoker = "Yes" if requess.smoker == 1 else "No"
+
+    # Render index.html with prediction results
+    json_compatible_resp_data = jsonable_encoder(Resp(
+        age=requess.age,
+        sex=sex,
+        smoker=smoker, 
+        bmi=requess.bmi,
+        children=requess.children,
+        region=get_region_name(requess.region),
+        insurance_cost=output
+    ))
+    return JSONResponse(content=json_compatible_resp_data)
+

insurance_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e3025a6bf31989a5271a2864b8bce71a9344eaf8b4bcc80d70eb5194f2fae885
+size 634

requirement.txt

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+fastapi
+pydantic
+python-multipart
+uvicorn
+starlette
+scikit-learn