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.gitattributes

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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.md

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----
-title: Protoai Api
-emoji: 🐢
-colorFrom: indigo
-colorTo: indigo
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Model Temp API v2
+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
+from typing import List, Optional
+
+
+# Download your model pickle from the Hub on startup
+#model_path = hf_hub_download(repo_id="Projects-by-IF/causal-model-Z15-v2", filename="trained_causal_model_v4.pkl")
+model_path = hf_hub_download(repo_id="DIGMMUNI/trained_causal_model_v1", filename="trained_causal_model_v1.pkl")
+
+with open(model_path, "rb") as f:
+    model = pickle.load(f)
+
+import joblib
+import os
+MODEL_DIR = r"./model"
+MODEL_FILE = "trained_causal_model_v1.pkl"
+model_path = os.path.join(MODEL_DIR, MODEL_FILE)
+
+app = FastAPI()
+
+#model = joblib.load(model_path)
+
+class InputData(BaseModel):
+    X: List
+    T0: Optional[List] = None
+    T1: Optional[List] = None
+
+@app.post("/model-effect")
+def predict(data: InputData):
+    X = np.array(data.X)
+
+    if data.T0 is not None and data.T1 is not None:
+        T0 = np.array(data.T0)
+        T1 = np.array(data.T1)
+        effect = model.effect(X=X, T0=T0, T1=T1).tolist()
+    else:
+        effect = model.effect(X).tolist()
+
+    return {"effect": effect}

requirements.txt

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