web app

.dockerignore

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+.dvc
+data
+notebooks
+reports

Dockerfile

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+FROM python:3.10
+ENV PYTHONPATH=/app
+WORKDIR /app
+COPY . .
+RUN pip install -U pip 
+RUN pip install -r requirements.txt
+EXPOSE 8080
+CMD uvicorn src.app.main:app --host 0.0.0.0 --port 8080

src/app/main.py

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+import json
+import sys
+from pathlib import Path
+
+import uvicorn
+
+src_path = Path(__file__).parent.parent.resolve()
+sys.path.append(str(src_path))
+
+from typing import List
+
+import pandas as pd
+from fastapi import Body, FastAPI, Request
+from fastapi.middleware.cors import CORSMiddleware
+from joblib import load
+from pydantic import BaseModel
+
+from utils.load_params import load_params
+
+app = FastAPI()
+# https://fastapi.tiangolo.com/tutorial/cors/#use-corsmiddleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+params = load_params(params_path='params.yaml')
+model_path = params.train.model_path
+feat_cols = params.base.feat_cols
+model = load(filename=model_path)
+
+class Customer(BaseModel):
+    CreditScore: int
+    Age: int
+    Tenure: int
+    Balance: float
+    NumOfProducts: int
+    HasCrCard: int
+    IsActiveMember: int
+    EstimatedSalary: float
+
+class Request(BaseModel):
+    data: List[Customer]
+
+@app.post("/predict")
+async def predict(info: Request = Body(..., example={
+    "data": [
+        {
+            "CreditScore": 619,
+            "Age": 42,
+            "Tenure": 2,
+            "Balance": 0,
+            "NumOfProducts": 1,
+            "HasCrCard": 1,
+            "IsActiveMember": 1,
+            "EstimatedSalary": 101348.88
+        },
+        {
+            "CreditScore": 699,
+            "Age": 39,
+            "Tenure": 21,
+            "Balance": 0,
+            "NumOfProducts": 2,
+            "HasCrCard": 0,
+            "IsActiveMember": 0,
+            "EstimatedSalary": 93826.63
+        }
+    ]
+})):
+    json_list = json.loads(info.json())
+    data = json_list['data']
+    input_data = pd.DataFrame(data)
+    probs = model.predict_proba(input_data)[:,0]
+    probs = probs.tolist()
+    return probs
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)