Blame for app.py

import os
import spaces
import gradio as gr
from fastapi import HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
import joblib
import pandas as pd
import numpy as np

# --- 1. LOAD MODEL AI ---
# Mengecek di root folder atau di dalam folder models/
MODEL_PATH = "deteks_isolation_forest.pkl"
if not os.path.exists(MODEL_PATH) and os.path.exists("models/deteks_isolation_forest.pkl"):
    MODEL_PATH = "models/deteks_isolation_forest.pkl"

try:
    ai_model = joblib.load(MODEL_PATH)
    print(f"Model AI berhasil di-load dari {MODEL_PATH}!")
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'
]

# --- 2. FUNGSI UTAMA DENGAN @spaces.GPU (Terhubung Langsung ke Gradio) ---
@spaces.GPU
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:
    if ai_model is None:
        raise RuntimeError("Model belum ter-load. Pastikan file .pkl ada di repositori.")

    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)
    }

    df_input = pd.DataFrame([features_dict])[FITUR_AI]
    raw_score = float(-ai_model.score_samples(df_input)[0])

    # Konversi raw_score ke persentil (skala 0 - 100)
    ai_percentile = float(np.clip((raw_score - 0.35) / 0.40 * 100, 0, 100))

    # Hitung & Normalisasi Skor Rules (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))

    # Risk Fusion (60% Rules + 40% AI)
    review_priority = (0.6 * rules_norm) + (0.4 * ai_percentile)

    # Reason Codes (Explainability)
    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")

    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)
        }
    }

# --- 3. ANTARMUKA GRADIO ---
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**. Endpoint REST API tersedia di `POST /api/v1/analyze_risk`.")
    
    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)")

    # Fungsi @spaces.GPU didaftarkan langsung ke komponen Gradio
    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,
        api_name="analyze_risk"
    )

# --- 4. TAMBAHKAN CUSTOM FASTAPI ENDPOINT KE SERVER GRADIO ---
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

# Mengaktifkan CORS pada aplikasi FastAPI internal milik Gradio
demo.app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

@demo.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. JALANKAN DENGAN DEMO.LAUNCH() ---
if __name__ == "__main__":
    demo.launch()