Upload 5 files

docker-compose.yml

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+version: "3"
+
+
+
+services:
+
+
+
+  app:
+
+    container_name: airflow_ml_model
+
+    build: .
+
+    command: bash -c "uvicorn main:app --host 0.0.0.0 --port 8000"
+    volumes:
+
+      - .:/app
+
+    ports:
+
+      - 8000:8000
+      

dockerfile

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+FROM python:3.9
+
+RUN pip install joblib
+RUN pip install python-multipart
+
+WORKDIR /app
+
+COPY requirements.txt /app
+
+# Upgrade pip and install Python packages
+RUN pip3 install --upgrade pip && \
+    pip3 install --no-cache-dir -r requirements.txt
+
+COPY . /app
+
+
+EXPOSE 8000

gradient_boosting.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:64632f01e67f085ec3944223d00aae47e64eb351a3699768e71a90078fe798b8
+size 166972

main.py

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+from fastapi import FastAPI, Response, status, File, Form, UploadFile
+from pydantic import BaseModel
+import joblib
+
+class Airflow(BaseModel):
+    topic: str
+
+app = FastAPI()
+
+model = joblib.load("gradient_boosting.joblib")
+
+@app.post("/api/volume_prediction")
+async def predict_volume(vol_moving_avg: float = Form(), adj_close_rolling_med: float = Form()):
+    features = [vol_moving_avg, adj_close_rolling_med]
+    predicted_volume = model.predict([features])[0]
+    return {"predicted_volume": predicted_volume}

requirements.txt

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+fastapi
+uvicorn
+scikit-learn
+python-multipart
+joblib