Create app.py

app.py

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+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.concurrency import run_in_threadpool
+from pydantic import BaseModel, Field
+from typing import Optional, List
+import pandas as pd
+import numpy as np
+import torch
+from chronos import ChronosPipeline
+from datetime import datetime, timedelta
+import os
+import logging
+import asyncio
+
+# ==========================================
+# 1. KONFIGURASI & METADATA API
+# ==========================================
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+logger = logging.getLogger(__name__)
+
+app = FastAPI(
+    title="Waste Intelligence API - Jakarta Pusat 2025",
+    description="""
+    API Prediksi Volume Sampah Berbasis AI untuk tantangan CASE 2.
+    Sistem menggunakan Model Transformer (Amazon Chronos) untuk memprediksi tumpukan sampah 
+    berdasarkan anomali cuaca (BMKG) dan izin keramaian (Event Data).
+    
+    Fitur Utama:
+    - Prediksi Volume Total (Ton)
+    - Dekomposisi Sampah (Organik vs Plastik) berdasarkan SIPSN KLHK 2025
+    - Rekomendasi Jumlah Armada Truk
+    - Status Risiko Operasional (Safe, Warning, Critical)
+    - Integrasi Jadwal Event Otomatis
+    """,
+    version="1.1.0",
+    contact={
+        "name": "Faril Putra Pratama - SMK Taruna Bangsa",
+        "url": "https://github.com/vibe-coder",
+    }
+)
+
+# Menambahkan dukungan CORS agar Frontend bisa mengakses API
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# ==========================================
+# 2. MODEL & DATA LOADING (STARTUP)
+# ==========================================
+pipeline = None
+df_history = None
+events_data = {}
+
+@app.on_event("startup")
+def load_assets():
+    global pipeline, df_history, events_data
+    logger.info("⏳ Menyiapkan AI Engine (Chronos-T5)...")
+    try:
+        pipeline = ChronosPipeline.from_pretrained(
+            "amazon/chronos-t5-tiny",
+            device_map="cpu", 
+            torch_dtype=torch.float32,
+        )
+        
+        dataset_path = 'dataset_vibe_coder_2025.csv'
+        if os.path.exists(dataset_path):
+            df_history = pd.read_csv(dataset_path)
+            logger.info("✅ Dataset & Model AI berhasil dimuat.")
+        else:
+            logger.warning(f"⚠️ Warning: {dataset_path} tidak ditemukan!")
+            
+        # Memuat jadwal event jika ada
+        event_path = 'event_jakarta_2025.txt'
+        if os.path.exists(event_path):
+            df_events = pd.read_csv(event_path)
+            for _, row in df_events.iterrows():
+                if str(row['Ada_Event']) == '1':
+                    events_data[str(row['Tanggal'])] = {
+                        'Nama_Event': row['Nama_Event'],
+                        'Lokasi': row['Lokasi_Utama']
+                    }
+            logger.info(f"✅ Jadwal {len(events_data)} event otomatis berhasil dimuat.")
+        else:
+            logger.warning(f"⚠️ Warning: {event_path} tidak ditemukan!")
+            
+    except Exception as e:
+        logger.error(f"❌ Gagal memuat asset: {e}")
+
+# ==========================================
+# 3. SCHEMA VALIDATION (DATA MODELS)
+# ==========================================
+class PredictionRequest(BaseModel):
+    hari_ke_depan: int = Field(7, ge=1, le=30, description="Durasi prediksi (1-30 hari)")
+    prediksi_hujan_bmkg: float = Field(0.0, ge=0, description="Estimasi curah hujan (mm)")
+    skala_keramaian: int = Field(0, ge=0, le=3, description="Skala event manual (0=Normal, 1=Kecil, 2=Menengah, 3=Besar) jika jadwal otomatis tidak ada.")
+
+    model_config = {
+        "json_schema_extra": {
+            "examples": [
+                {
+                    "hari_ke_depan": 7,
+                    "prediksi_hujan_bmkg": 25.5,
+                    "skala_keramaian": 0
+                }
+            ]
+        }
+    }
+
+class PredictionResult(BaseModel):
+    tanggal: str
+    total_volume_ton: float
+    sisa_makanan_ton: float
+    plastik_ton: float
+    rekomendasi_truk: int
+    status_risiko: str
+    info_event: Optional[str] = Field(None, description="Informasi jika ada event besar di hari ini")
+
+# ==========================================
+# 4. ENDPOINT LOGIC (BUSINESS LAYER)
+# ==========================================
+@app.get("/", tags=["Sistem"])
+def status_check():
+    return {
+        "status": "Online", 
+        "model": "Chronos-T5 Tiny", 
+        "region": "Jakarta Pusat",
+        "events_loaded": len(events_data)
+    }
+
+def perform_inference(context_tensor, steps):
+    """Fungsi sync untuk inference model yang akan dijalankan di threadpool"""
+    forecast = pipeline.predict(context_tensor.unsqueeze(0), steps)
+    return np.quantile(forecast[0].numpy(), 0.5, axis=0)
+
+@app.post("/api/v1/predict", response_model=List[PredictionResult], tags=["Prediksi Sampah"])
+async def get_waste_forecast(request: PredictionRequest):
+    if df_history is None or pipeline is None:
+        raise HTTPException(status_code=503, detail="Model atau Dataset belum siap.")
+
+    try:
+        # 1. Konteks Data Historis
+        context = torch.tensor(df_history['Volume_Total_Ton'].values)
+        
+        # 2. Forecasting Probabilistik (Asynchronous / Non-blocking)
+        logger.info(f"⏳ Memprediksi {request.hari_ke_depan} hari ke depan...")
+        median_forecast = await run_in_threadpool(perform_inference, context, request.hari_ke_depan)
+
+        # 3. Integrasi Faktor Luar (Case 2: Cuaca & Event Otomatis)
+        results = []
+        last_date = pd.to_datetime(df_history['TANGGAL'].iloc[-1])
+
+        for i, val in enumerate(median_forecast):
+            current_date = last_date + timedelta(days=i+1)
+            date_str = current_date.strftime('%Y-%m-%d')
+            
+            # Logika tambahan berat sampah basah karena hujan
+            rain_impact = (request.prediksi_hujan_bmkg * 2) if request.prediksi_hujan_bmkg > 20 else 0
+            
+            # Logika otomatis vs manual untuk Event
+            event_info = events_data.get(date_str)
+            if event_info:
+                # Jika ada di jadwal kalender otomatis (misal Konser Maroon 5), asumsikan lonjakan super besar
+                event_impact = 350 # Ton ekstra
+                info_text = f"{event_info['Nama_Event']} di {event_info['Lokasi']}"
+            else:
+                # Fallback ke skala input manual
+                event_impact = request.skala_keramaian * 150
+                info_text = None
+            
+            total_vol = float(val + rain_impact + event_impact)
+
+            # Dekomposisi berdasarkan Data SIPSN KLHK 2025 Jakarta Pusat
+            food_waste = total_vol * 0.4987
+            plastic_waste = total_vol * 0.2295
+
+            # Rekomendasi Armada (Kapasitas Truk Standar: 10 Ton)
+            num_trucks = int(np.ceil(total_vol / 10))
+
+            # Penentuan Status Risiko
+            if total_vol > 1300:
+                risk = "CRITICAL ⚠️"
+            elif total_vol > 1100:
+                risk = "WARNING ⚡"
+            else:
+                risk = "SAFE ✅"
+
+            results.append(
+                PredictionResult(
+                    tanggal=date_str,
+                    total_volume_ton=round(total_vol, 2),
+                    sisa_makanan_ton=round(food_waste, 2),
+                    plastik_ton=round(plastic_waste, 2),
+                    rekomendasi_truk=num_trucks,
+                    status_risiko=risk,
+                    info_event=info_text
+                )
+            )
+
+        logger.info("✅ Prediksi berhasil digenerate.")
+        return results
+
+    except Exception as e:
+        logger.error(f"❌ Gagal memproses prediksi: {e}")
+        raise HTTPException(status_code=500, detail=str(e))