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Dockerfile

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+
+
+FROM python:3.8-slim
+
+WORKDIR /app
+
+ADD . /app
+
+RUN apt-get update && apt-get install -y libgomp1
+
+RUN pip install -r requirements.txt
+
+EXPOSE 8000
+
+CMD ["python", "lr_api.py"]

lr_api.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b8456656657111aa1a380cb259b8aa4078cc70ab2aa976ee1653ec53bc21b9ce
+size 7020

lr_api.py

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+# -*- coding: utf-8 -*-
+
+import pandas as pd
+from pycaret.classification import load_model, predict_model
+from fastapi import FastAPI
+import uvicorn
+from pydantic import create_model
+
+# Create the app
+app = FastAPI()
+
+# Load trained Pipeline
+model = load_model("lr_api")
+
+# Create input/output pydantic models
+input_model = create_model("lr_api_input", **{'Id': 216, 'WeekofPurchase': 265, 'StoreID': 7, 'PriceCH': 1.8600000143051147, 'PriceMM': 2.130000114440918, 'DiscCH': 0.3700000047683716, 'DiscMM': 0.0, 'SpecialCH': 1, 'SpecialMM': 0, 'LoyalCH': 0.974931001663208, 'SalePriceMM': 2.130000114440918, 'SalePriceCH': 1.4900000095367432, 'PriceDiff': 0.6399999856948853, 'Store7': 'Yes', 'PctDiscMM': 0.0, 'PctDiscCH': 0.19892500340938568, 'ListPriceDiff': 0.27000001072883606, 'STORE': 0})
+output_model = create_model("lr_api_output", prediction='CH')
+
+
+# Define predict function
+@app.post("/predict", response_model=output_model)
+def predict(data: input_model):
+    data = pd.DataFrame([data.dict()])
+    predictions = predict_model(model, data=data)
+    return {"prediction": predictions["prediction_label"].iloc[0]}
+
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="127.0.0.1", port=8000)

requirements.txt

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+
+pycaret
+fastapi
+uvicorn