Add application file

Dockerfile

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+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+
+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

@@ -1,8 +1,8 @@
 ---
 title: Heart Disease Prediction
-emoji: 😻
-colorFrom: yellow
-colorTo: gray
+emoji: 🫀
+colorFrom: red
+colorTo: blue
 sdk: docker
 pinned: false
 ---

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+import pandas as pd
+from scipy.stats import boxcox
+from joblib import load
+
+
+app = FastAPI()
+
+
+class HeartModel(BaseModel):
+    age: int
+    sex: int
+    trestbps: int
+    chol: int
+    fbs: int
+    thalach: int
+    exang: int
+    oldpeak: float
+    slope: int
+    ca: int
+    cp_1: bool
+    cp_2: bool
+    cp_3: bool
+    restecg_1: bool
+    restecg_2: bool
+    thal_1: bool
+    thal_2: bool
+    thal_3: bool
+
+
+@app.get("/")
+def read_root():
+    return {"Heart": "Prediction"}
+
+
+@app.post("/predict")
+def read_item(data: HeartModel):
+    lambdas = {
+        "age": 1.1884623210915386,
+        "trestbps": -0.566961719937906,
+        "chol": -0.12552647234590764,
+        "thalach": 2.4454557922261086,
+        "oldpeak": 0.17759774936241574,
+    }
+
+    with open("svm.pkl", "rb") as f:
+        clf = load(f)
+
+    data = data.dict()
+    data["oldpeak"] = data["oldpeak"] + 0.001
+    for col in lambdas.keys():
+        if data[col] > 0:
+            data[col] = boxcox(data[col], lmbda=lambdas[col])
+
+    data = pd.DataFrame([data])
+
+    target = clf.predict(data)
+    return {"target": target[0].item()}

requirements.txt

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+
+fastapi
+uvicorn[standard]

svm.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:42c1b7fbc6c9e31114f0dff8811a3bc6e12cec431309c1402cac5aee8f6d3795
+size 39290