deploy api prediksi status gizi

Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY requirements-hf.txt .
+RUN pip install --no-cache-dir -r requirements-hf.txt
+
+COPY app.py .
+COPY model/ ./model/
+
+EXPOSE 7860
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel, field_validator
+from typing import List, Optional
+import pandas as pd
+import pickle
+import os
+
+# === PATH MODEL ===
+HERE = os.path.dirname(os.path.abspath(__file__))
+MODEL_DIR = os.path.join(HERE, "model")
+MODEL_PATH = os.path.join(MODEL_DIR, "svm.pkl")
+LE_JK_PATH = os.path.join(MODEL_DIR, "le_jenis_kelamin.pkl")
+LE_STATUS_PATH = os.path.join(MODEL_DIR, "le_status_gizi.pkl")
+
+FEATURE_ORDER = ["tinggi", "berat", "umur_bulan", "jenis_kelamin_encoded"]
+
+GENDER_ALIASES = {"L": "L", "P": "P", "l": "L", "p": "P", "Male": "L", "Female": "P", "M": "L", "F": "P"}
+
+# === Input pakai camelCase ===
+class Item(BaseModel):
+    tinggiCm: float
+    beratKg: float
+    usiaBulan: int
+    jenisKelamin: str
+    jenisKelaminEncoded: Optional[int] = None
+
+    @field_validator("jenisKelamin")
+    @classmethod
+    def normalisasi_jk(cls, v):
+        if v is None:
+            return v
+        v = str(v).strip()
+        return GENDER_ALIASES.get(v, GENDER_ALIASES.get(v.upper(), v.upper()[0]))
+
+    def to_features(self, le_jk):
+        try:
+            jk_encoded = (
+                int(self.jenisKelaminEncoded)
+                if self.jenisKelaminEncoded is not None
+                else int(le_jk.transform([self.jenisKelamin])[0])
+            )
+        except Exception:
+            raise ValueError(f"Jenis kelamin '{self.jenisKelamin}' tidak dikenali encoder")
+
+        return {
+            "tinggi": float(self.tinggiCm),
+            "berat": float(self.beratKg),
+            "umur_bulan": int(self.usiaBulan),
+            "jenis_kelamin_encoded": jk_encoded,
+        }
+
+class BatchRequest(BaseModel):
+    data: List[Item]
+
+# === Fungsi bantu ===
+def load_pickle(path):
+    if not os.path.exists(path):
+        raise FileNotFoundError(f"Tidak ditemukan: {path}")
+    with open(path, "rb") as f:
+        return pickle.load(f)
+
+# === Muat model & encoder ===
+try:
+    model = load_pickle(MODEL_PATH)
+    le_jk = load_pickle(LE_JK_PATH)
+    le_status = load_pickle(LE_STATUS_PATH)
+    _load_error = None
+except Exception as e:
+    model = le_jk = le_status = None
+    _load_error = e
+
+app = FastAPI(
+    title="API Prediksi Status Gizi (camelCase)",
+    description="API untuk prediksi status gizi anak",
+    version="1.0.0"
+)
+
+@app.get("/")
+def root():
+    return {"message": "API Prediksi Status Gizi", "status": "running"}
+
+@app.get("/health")
+def health():
+    return {"status": "ok"} if _load_error is None else {"status": "error", "detail": str(_load_error)}
+
+@app.post("/predict")
+def predict(item: Item):
+    if any(x is None for x in (model, le_jk, le_status)):
+        raise HTTPException(status_code=500, detail=f"Gagal memuat model: {_load_error}")
+    try:
+        row = item.to_features(le_jk)
+        X = pd.DataFrame([row])[FEATURE_ORDER]
+        y_enc = int(model.predict(X)[0])
+        y_label = str(le_status.inverse_transform([y_enc])[0])
+        return {"label": y_label, "labelEncoded": y_enc}
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+@app.post("/predictBatch")
+def predict_batch(req: BatchRequest):
+    if any(x is None for x in (model, le_jk, le_status)):
+        raise HTTPException(status_code=500, detail=f"Gagal memuat model: {_load_error}")
+    try:
+        rows = [item.to_features(le_jk) for item in req.data]
+        X = pd.DataFrame(rows)[FEATURE_ORDER]
+        y_enc = model.predict(X)
+        y_label = le_status.inverse_transform(y_enc)
+        results = [{"label": str(lbl), "labelEncoded": int(enc)} for lbl, enc in zip(y_label, y_enc)]
+        return {"results": results}
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=str(e))

model/le_jenis_kelamin.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:457bfe436860b313b799a51756043cd0c6f5af16fc7f85a85e6a43dc3886758a
+size 251

model/le_status_gizi.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:751540ac5db9f5dd674901e217129e1cb711712e2daac9338723461ab7a21334
+size 286

model/svm.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:18924a4ffb3ea6a4f9123ea2de35b582c067bababf1463cd9389a16439f3ba55
+size 12395

requirements-hf.txt

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+fastapi==0.104.1
+uvicorn==0.24.0
+pandas==2.1.4
+numpy==1.26.2
+scikit-learn==1.3.2
+pydantic==2.5.2
+python-multipart==0.0.6