Set up

Dockerfile

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+# Read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+# you will also find guides on how best to write your Dockerfile
+
+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"]

README copy.md

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+---
+title: test-if-api
+emoji: ⚡
+colorFrom: blue
+colorTo: green
+sdk: docker
+app_file: app.py
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+import numpy as np
+import pickle
+from huggingface_hub import hf_hub_download
+
+app = FastAPI()
+
+# Download your model pickle from the Hub on startup
+model_path = hf_hub_download(repo_id="nataliegref/test-model", filename="trained_causal_model_v2.pkl")
+
+with open(model_path, "rb") as f:
+    model = pickle.load(f)
+
+class InputData(BaseModel):
+    X: list  
+
+@app.post("/predict")
+def predict(data: InputData):
+    X = np.array(data.X)
+    effect = model.effect(X).tolist()
+    return {"effect": effect}

requirements.txt

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+huggingface_hub
+fastapi
+uvicorn
+numpy
+scikit-learn
+econml