Initial commit

.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

.github/workflows/sync_to_hf.yaml

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+name: Sync to Hugging Face hub
+on:
+  push:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          HF_USERNAME: ${{ vars.HF_USERNAME }}
+          HF_SPACE_NAME: ${{ vars.HF_SPACE_NAME}}
+        run: git push https://$HF_USERNAME:${HF_TOKEN}@huggingface.co/spaces/${HF_USERNAME}/${HF_SPACE_NAME} main

.gitignore

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+.env
+.vscode/
+.venv/
+__pycache__/

Dockerfile

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+# Dockerfile
+# Use a lightweight official Python image as the base
+FROM python:3.12-slim
+# Set the working directory inside the container
+WORKDIR /app
+# Copy the requirements file and install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+# Copy the rest of your application code
+COPY ./app .
+# Expose the port your FastAPI application will run on
+EXPOSE 7860
+# Command to run the FastAPI application with Uvicorn
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

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+---
+title: ML API
+emoji: 🤖
+colorFrom: blue
+colorTo: gray
+sdk: docker
+app_port: 7860
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app/app.py

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+# -*- coding: utf-8 -*-
+
+from fastapi import FastAPI
+from fastapi.responses import RedirectResponse
+from pydantic import BaseModel
+import joblib
+import pandas as pd
+
+app = FastAPI()
+model = joblib.load("./pipeline.joblib")
+
+class Input(BaseModel):
+    CUST_NBR: str
+    MENU_TYP_DESC: str
+    PYR_SEG_CD: str
+    DIV_NBR: str
+    WKLY_ORDERS: float
+    PERC_EB: float
+    AVG_WKLY_SALES: float
+    AVG_WKLY_CASES: float
+
+class Output(BaseModel):
+    prediction: list[int]
+
+@app.post("/predict", response_model=Output)
+def predict(data: list[Input]) -> Output:
+    print(data)
+    data = [item.model_dump() for item in data]
+    data = pd.DataFrame(data)
+    prediction = model.predict(data).tolist()
+    return {"prediction":prediction}
+
+@app.get("/")
+def home():
+    return RedirectResponse(url="/docs", status_code=302)

app/request_test.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Thu Nov 20 17:00:22 2025
+
+@author: dkondic
+"""
+import requests
+
+data = [
+        {"CUST_NBR":"1111",
+         "MENU_TYP_DESC":"MEXICAN",
+         "PYR_SEG_CD":"Education",
+         "DIV_NBR":"20",
+         "WKLY_ORDERS": 1,
+         "PERC_EB":0.80,
+         "AVG_WKLY_SALES":2656.04,
+         "AVG_WKLY_CASES":67.00}]
+
+response = requests.post("http://localhost:7860/predict", json=data)
+print(response.json())

docker-compose.yaml

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+services:
+  app:
+    build: .
+    container_name: app
+    restart: unless-stopped
+    ports:
+      - "7860:7860" 

requirements.txt

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+annotated-doc==0.0.4
+annotated-types==0.7.0
+anyio==4.11.0
+click==8.3.1
+colorama==0.4.6
+fastapi==0.121.3
+h11==0.16.0
+idna==3.11
+joblib==1.5.2
+mord==0.7
+numpy==2.3.5
+pandas==2.3.3
+pydantic==2.12.4
+pydantic_core==2.41.5
+python-dateutil==2.9.0.post0
+pytz==2025.2
+scikit-learn==1.7.2
+scipy==1.16.3
+six==1.17.0
+sniffio==1.3.1
+starlette==0.50.0
+threadpoolctl==3.6.0
+typing-inspection==0.4.2
+typing_extensions==4.15.0
+tzdata==2025.2
+uvicorn==0.38.0