Deploy DETEKS AI Engine and FastAPI endpoint

app.py

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+import spaces
+import gradio as gr
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import joblib
+import pandas as pd
+import numpy as np
+import uvicorn
+
+# --- 1. INISIALISASI FASTAPI & CORS (Wajib agar Frontend bisa akses) ---
+app = FastAPI(title="DETEKS AI API (ZeroGPU)", version="1.0")
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Mengizinkan akses dari domain frontend mana saja (Vercel/Localhost)
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# --- 2. LOAD MODEL AI ---
+MODEL_PATH = "deteks_isolation_forest.pkl"
+try:
+    ai_model = joblib.load(MODEL_PATH)
+    print("Model AI berhasil di-load!")
+except Exception as e:
+    ai_model = None
+    print(f"Error loading model: {e}")
+
+FITUR_AI = [
+    'n_event_kunjungan', 'n_faskes_kunjungan', 'same_day_hits', 'unbundling_hits',
+    'n_event_obat', 'n_faskes_obat', 'qty_30d', 'min_interval', 'overlap_days'
+]
+
+# --- 3. FUNGSI INFERENSI DENGAN DEKORATOR ZEROGPU (@spaces.GPU) ---
+@spaces.GPU
+def run_ai_inference(features_dict: dict) -> float:
+    """
+    Fungsi inti prediksi anomali yang menggunakan dekorator @spaces.GPU
+    agar memenuhi syarat eksekusi Hugging Face ZeroGPU.
+    """
+    if ai_model is None:
+        raise RuntimeError("Model belum ter-load. Pastikan file .pkl sudah di-upload.")
+    
+    df_input = pd.DataFrame([features_dict])[FITUR_AI]
+    # Semakin negatif score_samples = semakin anomali, kita balik dengan tanda minus (-)
+    raw_score = float(-ai_model.score_samples(df_input)[0])
+    return raw_score
+
+
+def calculate_risk_fusion(
+    participant_key: str,
+    n_event_kunjungan: float,
+    n_faskes_kunjungan: float,
+    same_day_hits: float,
+    unbundling_hits: float,
+    n_event_obat: float,
+    n_faskes_obat: float,
+    qty_30d: float,
+    min_interval: float,
+    overlap_days: float,
+    rules_score_tambahan: float = 0.0
+) -> dict:
+    features_dict = {
+        'n_event_kunjungan': float(n_event_kunjungan),
+        'n_faskes_kunjungan': float(n_faskes_kunjungan),
+        'same_day_hits': float(same_day_hits),
+        'unbundling_hits': float(unbundling_hits),
+        'n_event_obat': float(n_event_obat),
+        'n_faskes_obat': float(n_faskes_obat),
+        'qty_30d': float(qty_30d),
+        'min_interval': float(min_interval),
+        'overlap_days': float(overlap_days)
+    }
+
+    # Jalankan fungsi ZeroGPU (dengan fallback aman jika dipanggil langsung via REST API)
+    try:
+        raw_score = run_ai_inference(features_dict)
+    except Exception:
+        df_input = pd.DataFrame([features_dict])[FITUR_AI]
+        raw_score = float(-ai_model.score_samples(df_input)[0])
+
+    # Konversi raw_score Isolation Forest (biasanya di rentang 0.35 - 0.75) ke skala persentil 0-100
+    ai_percentile = float(np.clip((raw_score - 0.35) / 0.40 * 100, 0, 100))
+
+    # Hitung Skor Rules & Normalisasi ke skala 0-100
+    total_rules = float(same_day_hits) + float(unbundling_hits) + float(rules_score_tambahan)
+    rules_norm = float(min((total_rules / 140.0) * 100, 100.0))
+
+    # Formula Risk Fusion DETEKS: 60% Rules + 40% AI
+    review_priority = (0.6 * rules_norm) + (0.4 * ai_percentile)
+
+    # Buat Alasan Terdeteksi (Explainability / Reason Codes)
+    reasons = []
+    if same_day_hits > 0:
+        reasons.append(f"Kunjungan di hari yang sama ({int(same_day_hits)}x)")
+    if unbundling_hits > 0:
+        reasons.append(f"Indikasi unbundling kunjungan ({int(unbundling_hits)}x)")
+    if min_interval < 25:
+        reasons.append(f"Early refill obat (interval {int(min_interval)} hari)")
+    if overlap_days >= 3:
+        reasons.append(f"Overlap stok obat tinggi ({int(overlap_days)} hari)")
+    if n_faskes_obat > 1:
+        reasons.append(f"Pengambilan obat lintas faskes ({int(n_faskes_obat)} faskes)")
+    if not reasons:
+        reasons.append("Pola terpantau normal / risiko rendah")
+
+    # Tentukan Kategori Risiko (Band)
+    if review_priority >= 70:
+        status = "Tinggi"
+    elif review_priority >= 40:
+        status = "Sedang"
+    else:
+        status = "Rendah"
+
+    return {
+        "participant_key": str(participant_key),
+        "review_priority": round(review_priority, 2),
+        "risk_band": status,
+        "reasons": reasons,
+        "detail_scores": {
+            "total_rules_raw": round(total_rules, 2),
+            "rules_score_normalized": round(rules_norm, 2),
+            "ai_anomaly_percentile": round(ai_percentile, 2),
+            "raw_if_score": round(raw_score, 4)
+        }
+    }
+
+# --- 4. MODEL REQUEST & ENDPOINT FASTAPI ---
+class PatientFeatures(BaseModel):
+    participant_key: str = "P-0001"
+    n_event_kunjungan: float = 2.0
+    n_faskes_kunjungan: float = 1.0
+    same_day_hits: float = 0.0
+    unbundling_hits: float = 0.0
+    n_event_obat: float = 2.0
+    n_faskes_obat: float = 1.0
+    qty_30d: float = 2.0
+    min_interval: float = 27.0
+    overlap_days: float = 4.0
+    rules_score_tambahan: float = 0.0
+
+@app.post("/api/v1/analyze_risk")
+def analyze_risk_api(patient: PatientFeatures):
+    try:
+        return calculate_risk_fusion(
+            participant_key=patient.participant_key,
+            n_event_kunjungan=patient.n_event_kunjungan,
+            n_faskes_kunjungan=patient.n_faskes_kunjungan,
+            same_day_hits=patient.same_day_hits,
+            unbundling_hits=patient.unbundling_hits,
+            n_event_obat=patient.n_event_obat,
+            n_faskes_obat=patient.n_faskes_obat,
+            qty_30d=patient.qty_30d,
+            min_interval=patient.min_interval,
+            overlap_days=patient.overlap_days,
+            rules_score_tambahan=patient.rules_score_tambahan
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# --- 5. ANTARMUKA GRADIO (Wajib untuk ZeroGPU Space) ---
+with gr.Blocks(title="DETEKS AI Engine") as demo:
+    gr.Markdown("## 🏥 DETEKS - AI Risk Scoring Service (ZeroGPU)")
+    gr.Markdown("Antarmuka uji coba model **Hybrid Rules + Isolation Forest**. Untuk dokumentasi REST API, buka endpoint `/docs`.")
+    
+    with gr.Row():
+        with gr.Column():
+            p_key = gr.Textbox(label="ID Peserta (participant_key)", value="P-0951")
+            n_ev_kunj = gr.Number(label="Total Kunjungan RJTL (n_event_kunjungan)", value=5)
+            n_fas_kunj = gr.Number(label="Jumlah Faskes Kunjungan (n_faskes_kunjungan)", value=1)
+            same_day = gr.Number(label="Same Day Hits", value=0)
+            unbundling = gr.Number(label="Unbundling Hits", value=1)
+        with gr.Column():
+            n_ev_obat = gr.Number(label="Frekuensi Tebus Obat (n_event_obat)", value=2)
+            n_fas_obat = gr.Number(label="Jumlah Faskes Obat (n_faskes_obat)", value=2)
+            qty_30d = gr.Number(label="Kuantitas Obat 30 Hari (qty_30d)", value=2)
+            min_int = gr.Number(label="Interval Terpendek Ambil Obat (min_interval)", value=20)
+            overlap = gr.Number(label="Hari Overlap Obat (overlap_days)", value=5)
+            rules_ext = gr.Number(label="Skor Rules Obat Tambahan", value=10)
+
+    btn = gr.Button("🔍 Analisis Risiko Pasien", variant="primary")
+    output_json = gr.JSON(label="Hasil Output DETEKS (JSON)")
+
+    btn.click(
+        fn=calculate_risk_fusion,
+        inputs=[p_key, n_ev_kunj, n_fas_kunj, same_day, unbundling, n_ev_obat, n_fas_obat, qty_30d, min_int, overlap, rules_ext],
+        outputs=output_json
+    )
+
+# Gabungkan Gradio ke dalam aplikasi FastAPI
+app = gr.mount_gradio_app(app, demo, path="/")
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)

deteks_isolation_forest.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:173903eb60b9637cb795219af348d8ad41d552dd775f7ed8048ba1de77b64835
+size 793181

requirements.txt

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+fastapi
+uvicorn
+scikit-learn
+pandas
+numpy
+pydantic
+joblib
+gradio
+spaces