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| from fastapi import FastAPI, HTTPException, Query | |
| from fastapi.middleware.cors import CORSMiddleware | |
| from fastapi.concurrency import run_in_threadpool | |
| from fastapi.responses import HTMLResponse, StreamingResponse | |
| from fastapi.staticfiles import StaticFiles | |
| from pydantic import BaseModel, Field, field_validator | |
| from typing import Optional, List, Dict, Any | |
| import pandas as pd | |
| import numpy as np | |
| import torch | |
| import joblib | |
| import httpx | |
| import io | |
| import csv | |
| import json | |
| from chronos import ChronosPipeline | |
| from datetime import datetime, timedelta | |
| import os, logging, re | |
| # ========================================== | |
| # 1. APPLICATION CONFIGURATION | |
| # ========================================== | |
| logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") | |
| logger = logging.getLogger(__name__) | |
| app = FastAPI( | |
| title="Waste Intelligence API - DKI Jakarta 2026", | |
| version="4.0.0 (Multi-Region & Live News)", | |
| description="AI-powered waste prediction for 44 sub-districts with spatial awareness, live weather, and news monitoring" | |
| ) | |
| app.add_middleware( | |
| CORSMiddleware, | |
| allow_origins=["*"], | |
| allow_credentials=True, | |
| allow_methods=["*"], | |
| allow_headers=["*"], | |
| ) | |
| # Mount static files to serve the dashboard UI, CSS, and JS | |
| if not os.path.exists("static"): | |
| os.makedirs("static") | |
| app.mount("/static", StaticFiles(directory="static"), name="static") | |
| # ========================================== | |
| # 2. 44 KECAMATAN DATABASE (DLH Jakarta Calibrated) | |
| # ========================================== | |
| KECAMATAN_DATABASE = { | |
| # 1. JAKARTA PUSAT (8 Kecamatan) - Total: 1150 Ton | |
| "Menteng": {"latitude": -6.1950, "longitude": 106.8322, "normal_avg": 120.0, "warning_threshold": 160.0, "critical_threshold": 180.0, "city": "Jakarta Pusat"}, | |
| "Senen": {"latitude": -6.1822, "longitude": 106.8452, "normal_avg": 180.0, "warning_threshold": 220.0, "critical_threshold": 240.0, "city": "Jakarta Pusat"}, | |
| "Cempaka Putih": {"latitude": -6.1802, "longitude": 106.8686, "normal_avg": 90.0, "warning_threshold": 120.0, "critical_threshold": 140.0, "city": "Jakarta Pusat"}, | |
| "Johar Baru": {"latitude": -6.1866, "longitude": 106.8572, "normal_avg": 70.0, "warning_threshold": 95.0, "critical_threshold": 110.0, "city": "Jakarta Pusat"}, | |
| "Kemayoran": {"latitude": -6.1628, "longitude": 106.8438, "normal_avg": 180.0, "warning_threshold": 220.0, "critical_threshold": 240.0, "city": "Jakarta Pusat"}, | |
| "Sawah Besar": {"latitude": -6.1554, "longitude": 106.8322, "normal_avg": 110.0, "warning_threshold": 145.0, "critical_threshold": 165.0, "city": "Jakarta Pusat"}, | |
| "Tanah Abang": {"latitude": -6.2104, "longitude": 106.8122, "normal_avg": 250.0, "warning_threshold": 320.0, "critical_threshold": 350.0, "city": "Jakarta Pusat"}, | |
| "Gambir": {"latitude": -6.1764, "longitude": 106.8190, "normal_avg": 150.0, "warning_threshold": 195.0, "critical_threshold": 215.0, "city": "Jakarta Pusat"}, | |
| # 2. JAKARTA UTARA (6 Kecamatan) - Total: 1350 Ton | |
| "Penjaringan": {"latitude": -6.1264, "longitude": 106.7822, "normal_avg": 280.0, "warning_threshold": 350.0, "critical_threshold": 380.0, "city": "Jakarta Utara"}, | |
| "Tanjung Priok": {"latitude": -6.1322, "longitude": 106.8722, "normal_avg": 260.0, "warning_threshold": 320.0, "critical_threshold": 350.0, "city": "Jakarta Utara"}, | |
| "Koja": {"latitude": -6.1214, "longitude": 106.9133, "normal_avg": 190.0, "warning_threshold": 240.0, "critical_threshold": 270.0, "city": "Jakarta Utara"}, | |
| "Cilincing": {"latitude": -6.1288, "longitude": 106.9452, "normal_avg": 290.0, "warning_threshold": 370.0, "critical_threshold": 400.0, "city": "Jakarta Utara"}, | |
| "Pademangan": {"latitude": -6.1328, "longitude": 106.8422, "normal_avg": 140.0, "warning_threshold": 180.0, "critical_threshold": 200.0, "city": "Jakarta Utara"}, | |
| "Kelapa Gading": {"latitude": -6.1552, "longitude": 106.9022, "normal_avg": 190.0, "warning_threshold": 240.0, "critical_threshold": 270.0, "city": "Jakarta Utara"}, | |
| # 3. JAKARTA BARAT (8 Kecamatan) - Total: 1550 Ton | |
| "Cengkareng": {"latitude": -6.1528, "longitude": 106.7322, "normal_avg": 340.0, "warning_threshold": 420.0, "critical_threshold": 460.0, "city": "Jakarta Barat"}, | |
| "Grogol Petamburan": {"latitude": -6.1622, "longitude": 106.7882, "normal_avg": 220.0, "warning_threshold": 280.0, "critical_threshold": 310.0, "city": "Jakarta Barat"}, | |
| "Kalideres": {"latitude": -6.1428, "longitude": 106.7022, "normal_avg": 260.0, "warning_threshold": 330.0, "critical_threshold": 360.0, "city": "Jakarta Barat"}, | |
| "Kebon Jeruk": {"latitude": -6.1922, "longitude": 106.7722, "normal_avg": 210.0, "warning_threshold": 260.0, "critical_threshold": 290.0, "city": "Jakarta Barat"}, | |
| "Kembangan": {"latitude": -6.1828, "longitude": 106.7382, "normal_avg": 180.0, "warning_threshold": 230.0, "critical_threshold": 250.0, "city": "Jakarta Barat"}, | |
| "Palmerah": {"latitude": -6.2028, "longitude": 106.7882, "normal_avg": 160.0, "warning_threshold": 200.0, "critical_threshold": 220.0, "city": "Jakarta Barat"}, | |
| "Taman Sari": {"latitude": -6.1454, "longitude": 106.8182, "normal_avg": 100.0, "warning_threshold": 130.0, "critical_threshold": 150.0, "city": "Jakarta Barat"}, | |
| "Tambora": {"latitude": -6.1500, "longitude": 106.8000, "normal_avg": 80.0, "warning_threshold": 110.0, "critical_threshold": 125.0, "city": "Jakarta Barat"}, | |
| # 4. JAKARTA SELATAN (10 Kecamatan) - Total: 1850 Ton | |
| "Cilandak": {"latitude": -6.2928, "longitude": 106.7922, "normal_avg": 180.0, "warning_threshold": 230.0, "critical_threshold": 250.0, "city": "Jakarta Selatan"}, | |
| "Jagakarsa": {"latitude": -6.3328, "longitude": 106.8222, "normal_avg": 220.0, "warning_threshold": 280.0, "critical_threshold": 310.0, "city": "Jakarta Selatan"}, | |
| "Kebayoran Baru": {"latitude": -6.2422, "longitude": 106.7982, "normal_avg": 210.0, "warning_threshold": 260.0, "critical_threshold": 290.0, "city": "Jakarta Selatan"}, | |
| "Kebayoran Lama": {"latitude": -6.2488, "longitude": 106.7722, "normal_avg": 230.0, "warning_threshold": 290.0, "critical_threshold": 320.0, "city": "Jakarta Selatan"}, | |
| "Mampang Prapatan": {"latitude": -6.2522, "longitude": 106.8182, "normal_avg": 120.0, "warning_threshold": 150.0, "critical_threshold": 170.0, "city": "Jakarta Selatan"}, | |
| "Pancoran": {"latitude": -6.2622, "longitude": 106.8382, "normal_avg": 130.0, "warning_threshold": 160.0, "critical_threshold": 180.0, "city": "Jakarta Selatan"}, | |
| "Pasar Minggu": {"latitude": -6.2828, "longitude": 106.8438, "normal_avg": 240.0, "warning_threshold": 300.0, "critical_threshold": 330.0, "city": "Jakarta Selatan"}, | |
| "Pesanggrahan": {"latitude": -6.2588, "longitude": 106.7588, "normal_avg": 160.0, "warning_threshold": 200.0, "critical_threshold": 220.0, "city": "Jakarta Selatan"}, | |
| "Setiabudi": {"latitude": -6.2228, "longitude": 106.8282, "normal_avg": 190.0, "warning_threshold": 240.0, "critical_threshold": 270.0, "city": "Jakarta Selatan"}, | |
| "Tebet": {"latitude": -6.2288, "longitude": 106.8482, "normal_avg": 170.0, "warning_threshold": 210.0, "critical_threshold": 230.0, "city": "Jakarta Selatan"}, | |
| # 5. JAKARTA TIMUR (10 Kecamatan) - Total: 2100 Ton | |
| "Cakung": {"latitude": -6.1828, "longitude": 106.9482, "normal_avg": 350.0, "warning_threshold": 430.0, "critical_threshold": 470.0, "city": "Jakarta Timur"}, | |
| "Cipayung": {"latitude": -6.3128, "longitude": 106.9022, "normal_avg": 140.0, "warning_threshold": 180.0, "critical_threshold": 200.0, "city": "Jakarta Timur"}, | |
| "Ciracas": {"latitude": -6.3228, "longitude": 106.8782, "normal_avg": 190.0, "warning_threshold": 240.0, "critical_threshold": 270.0, "city": "Jakarta Timur"}, | |
| "Duren Sawit": {"latitude": -6.2228, "longitude": 106.9282, "normal_avg": 300.0, "warning_threshold": 370.0, "critical_threshold": 410.0, "city": "Jakarta Timur"}, | |
| "Jatinegara": {"latitude": -6.2222, "longitude": 106.8682, "normal_avg": 240.0, "warning_threshold": 300.0, "critical_threshold": 330.0, "city": "Jakarta Timur"}, | |
| "Kramat Jati": {"latitude": -6.2722, "longitude": 106.8682, "normal_avg": 220.0, "warning_threshold": 270.0, "critical_threshold": 300.0, "city": "Jakarta Timur"}, | |
| "Makasar": {"latitude": -6.2622, "longitude": 106.8782, "normal_avg": 160.0, "warning_threshold": 200.0, "critical_threshold": 220.0, "city": "Jakarta Timur"}, | |
| "Matraman": {"latitude": -6.2022, "longitude": 106.8582, "normal_avg": 130.0, "warning_threshold": 160.0, "critical_threshold": 180.0, "city": "Jakarta Timur"}, | |
| "Pasar Rebo": {"latitude": -6.3122, "longitude": 106.8522, "normal_avg": 150.0, "warning_threshold": 190.0, "critical_threshold": 210.0, "city": "Jakarta Timur"}, | |
| "Pulo Gadung": {"latitude": -6.1922, "longitude": 106.8922, "normal_avg": 220.0, "warning_threshold": 270.0, "critical_threshold": 300.0, "city": "Jakarta Timur"}, | |
| # 6. KEPULAUAN SERIBU (2 Kecamatan) - Total: 20 Ton | |
| "Kepulauan Seribu Utara": {"latitude": -5.5722, "longitude": 106.5522, "normal_avg": 11.0, "warning_threshold": 15.0, "critical_threshold": 18.0, "city": "Kepulauan Seribu"}, | |
| "Kepulauan Seribu Selatan": {"latitude": -5.7722, "longitude": 106.6522, "normal_avg": 9.0, "warning_threshold": 12.0, "critical_threshold": 15.0, "city": "Kepulauan Seribu"} | |
| } | |
| ALLOWED_LOCATIONS = list(KECAMATAN_DATABASE.keys()) | |
| # ========================================== | |
| # 3. INPUT VALIDATION & SCHEMAS | |
| # ========================================== | |
| class PredictionRequest(BaseModel): | |
| forecast_days: int = Field(7, ge=1, le=30, description="Forecast horizon in days (1-30)") | |
| rainfall_mm: float = Field(0.0, ge=0, description="Precipitation override. 0.0 means Auto (Open-Meteo)") | |
| event_scale: int = Field(0, ge=0, le=5, description="Manual event crowd scale (0=none, 5=massive)") | |
| location: str = Field(..., description="Target sub-district (Kecamatan)") | |
| start_date: Optional[str] = Field(None, description="Start date: YYYY-MM-DD") | |
| granularity: str = Field("daily", pattern="^(daily|hourly)$", description="Granularity") | |
| model_type: str = Field("chronos", pattern="^(chronos|gradient_boosting)$", description="AI model type") | |
| def validate_location(cls, v: str) -> str: | |
| if v not in ALLOWED_LOCATIONS: | |
| raise ValueError(f"Kecamatan not recognized. Use one of the 44 sub-districts in Jakarta.") | |
| return v | |
| class PredictionResult(BaseModel): | |
| date: str | |
| location: str | |
| total_volume_ton: float | |
| organic_waste_ton: float | |
| plastic_waste_ton: float | |
| paper_waste_ton: float | |
| metal_waste_ton: float | |
| glass_waste_ton: float | |
| textile_waste_ton: float | |
| other_waste_ton: float | |
| recommended_trucks: int | |
| risk_status: str | |
| event_info: Optional[str] = None | |
| hourly_breakdown: Optional[List[Dict[str, Any]]] = None | |
| class LogisticsPlan(BaseModel): | |
| trucks_needed: int | |
| manpower: int | |
| estimated_duration_hours: float | |
| efficiency_rate: str | |
| class PredictionData(BaseModel): | |
| prediction_results: List[PredictionResult] | |
| logistics_plan: LogisticsPlan | |
| class APIResponse(BaseModel): | |
| status: str | |
| message: str | |
| confidence_score: float | |
| data: PredictionData | |
| class AlertResponse(BaseModel): | |
| status: str | |
| alert_count: int | |
| alerts: List[Dict[str, Any]] | |
| last_updated: str | |
| # ========================================== | |
| # 4. GLOBAL STATE & MODELS | |
| # ========================================== | |
| pipeline = None | |
| model_gbr = None | |
| df_history = None | |
| events_data = {} | |
| WEATHER_CACHE = {} | |
| HOURLY_PATTERN = { | |
| 0:0.02, 1:0.01, 2:0.01, 3:0.01, 4:0.02, 5:0.03, | |
| 6:0.05, 7:0.07, 8:0.06, 9:0.05, 10:0.04, 11:0.04, | |
| 12:0.04, 13:0.04, 14:0.04, 15:0.04, 16:0.05, 17:0.06, | |
| 18:0.07, 19:0.06, 20:0.05, 21:0.04, 22:0.03, 23:0.02 | |
| } | |
| # ========================================== | |
| # 5. HELPER FUNCTIONS | |
| # ========================================== | |
| def parse_flexible_date(date_input: str, default_year: int = 2026) -> pd.Timestamp: | |
| if not date_input: return None | |
| date_input = date_input.strip() | |
| for fmt in ["%Y-%m-%d", "%d-%m-%Y", "%m-%d", "%d %B %Y", "%d %b %Y", "%B %d, %Y"]: | |
| try: | |
| parsed = datetime.strptime(date_input, fmt) | |
| if fmt == "%m-%d": parsed = parsed.replace(year=default_year) | |
| return pd.Timestamp(parsed) | |
| except ValueError: continue | |
| raise ValueError(f"Unrecognized date format: '{date_input}'") | |
| def get_risk_status(volume: float, location: str) -> str: | |
| config = KECAMATAN_DATABASE.get(location, KECAMATAN_DATABASE["Menteng"]) | |
| if volume > config["critical_threshold"]: | |
| return "CRITICAL" | |
| elif volume > config["warning_threshold"]: | |
| return "WARNING" | |
| return "SAFE" | |
| def distribute_to_hourly(daily_volume: float) -> List[Dict[str, Any]]: | |
| pattern = HOURLY_PATTERN.copy() | |
| total_factor = sum(pattern.values()) | |
| hourly_results = [] | |
| high_thresh = (daily_volume / 24) * 2.0 | |
| med_thresh = (daily_volume / 24) * 1.2 | |
| for h in range(24): | |
| vol = round(daily_volume * (pattern[h] / total_factor), 2) | |
| risk = "HIGH" if vol > high_thresh else "MEDIUM" if vol > med_thresh else "LOW" | |
| hourly_results.append({ | |
| "hour": f"{h:02d}:00", | |
| "estimated_volume_ton": vol, | |
| "risk_indicator": risk, | |
| "confidence_range": {"lower": round(vol*0.85, 2), "upper": round(vol*1.15, 2)} | |
| }) | |
| return hourly_results | |
| async def fetch_rainfall_forecast(lat: float, lon: float, days: int) -> dict: | |
| """Fetch daily rainfall forecast from Open-Meteo API (with 30-min in-memory caching and short timeout)""" | |
| cache_key = f"{lat:.2f}_{lon:.2f}_{days}" | |
| now = datetime.now() | |
| # Expiration Cache Check | |
| if cache_key in WEATHER_CACHE: | |
| cached_data, timestamp = WEATHER_CACHE[cache_key] | |
| if now - timestamp < timedelta(minutes=30): | |
| logger.info(f"⚡ Weather cache hit for {cache_key}") | |
| return cached_data | |
| url = f"https://api.open-meteo.com/v1/forecast?latitude={lat}&longitude={lon}&daily=precipitation_sum&timezone=Asia/Jakarta&forecast_days={days}&past_days=2" | |
| try: | |
| async with httpx.AsyncClient() as client: | |
| response = await client.get(url, timeout=1.5) # Short timeout | |
| if response.status_code == 200: | |
| data = response.json() | |
| daily = data.get("daily", {}) | |
| times = daily.get("time", []) | |
| precip = daily.get("precipitation_sum", []) | |
| result = {times[i]: float(precip[i]) for i in range(len(times)) if i < len(precip)} | |
| # Save to cache | |
| WEATHER_CACHE[cache_key] = (result, now) | |
| return result | |
| except Exception as e: | |
| logger.error(f"Failed to fetch weather from Open-Meteo: {e}") | |
| return {} | |
| # ========================================== | |
| # 6. STARTUP & LOAD MODEL | |
| # ========================================== | |
| async def load_assets(): | |
| global pipeline, model_gbr, df_history, events_data | |
| logger.info("⏳ Initializing multi-region AI models...") | |
| try: | |
| pipeline = ChronosPipeline.from_pretrained("amazon/chronos-t5-tiny", device_map="cpu", torch_dtype=torch.float32) | |
| logger.info("✅ Chronos pipeline loaded") | |
| if os.path.exists("model_sampah_advanced.pkl"): | |
| model_gbr = joblib.load("model_sampah_advanced.pkl") | |
| logger.info("✅ Upgraded GBR model loaded") | |
| if os.path.exists("model_sampah_advanced.pkl"): | |
| model_gbr = joblib.load("model_sampah_advanced.pkl") | |
| logger.info("✅ Gradient Boosting model loaded") | |
| else: | |
| logger.warning("⚠️ model_sampah_advanced.pkl not found") | |
| df_history = pd.read_csv("dataset_vibe_coder_2026.csv") | |
| df_history["TANGGAL"] = pd.to_datetime(df_history["TANGGAL"]).dt.strftime("%Y-%m-%d") | |
| logger.info(f"✅ Baseline dataset loaded: {len(df_history)} records") | |
| event_file = "event_jakarta_2026.txt" | |
| if os.path.exists(event_file): | |
| df_e = pd.read_csv(event_file) | |
| df_e.columns = [c.strip().lower() for c in df_e.columns] | |
| for _, r in df_e.iterrows(): | |
| if str(r.get("ada_event", "1")) == "1": | |
| dk = str(r.get("tanggal", "")).strip() | |
| if dk: | |
| events_data[dk] = { | |
| "event_name": str(r.get("nama_event", "")), | |
| "location": str(r.get("lokasi", "")), | |
| "crowd_scale": float(r.get("skala_keramaian", 0)) | |
| } | |
| logger.info(f"✅ Event calendar loaded: {len(events_data)} entries") | |
| except Exception as e: | |
| logger.error(f"❌ Startup failed: {e}") | |
| raise | |
| # ========================================== | |
| # 7. ROUTING & CONTROLLERS | |
| # ========================================== | |
| def serve_dashboard(): | |
| """Serve the Floodzy-style interactive dashboard.""" | |
| try: | |
| with open("static/index.html", "r", encoding="utf-8") as f: | |
| return HTMLResponse(content=f.read(), status_code=200) | |
| except FileNotFoundError: | |
| return HTMLResponse(content="<h1>Dashboard HTML not found. Please check your static directory.</h1>", status_code=404) | |
| def status_check(): | |
| return { | |
| "status": "Online", | |
| "model_chronos": "Chronos-T5 Tiny", | |
| "model_gbr": "Gradient Boosting Regressor (Upgraded)", | |
| "coverage": "44 Kecamatan DKI Jakarta", | |
| "calibrated": True | |
| } | |
| def get_latest_news(): | |
| """Returns the latest crawled news from latest_waste_news.json""" | |
| news_file = "latest_waste_news.json" | |
| if os.path.exists(news_file): | |
| try: | |
| with open(news_file, "r", encoding="utf-8") as f: | |
| return json.load(f) | |
| except Exception as e: | |
| logger.error(f"Error reading news file: {e}") | |
| return [ | |
| { | |
| "title": "DKI Uji Coba Penarikan Retribusi Sampah Pelayanan Kebersihan Harian", | |
| "source": "Antara News", | |
| "url": "https://www.antaranews.com/tag/sampah-jakarta", | |
| "date_fetched": str(datetime.now().date()), | |
| "summary": "Pemprov DKI Jakarta merencanakan uji coba penarikan retribusi pelayanan kebersihan/sampah." | |
| } | |
| ] | |
| def perform_inference(ctx, steps): | |
| forecast = pipeline.predict(ctx.unsqueeze(0), steps) | |
| return np.quantile(forecast[0].numpy(), 0.5, axis=0) | |
| async def predict_waste_volume(req: PredictionRequest): | |
| if df_history is None or pipeline is None: | |
| raise HTTPException(503, "Models not ready.") | |
| try: | |
| start_date = parse_flexible_date(req.start_date) if req.start_date else pd.Timestamp(datetime.now().date()) | |
| # Get location metadata | |
| config = KECAMATAN_DATABASE[req.location] | |
| # Fetch live weather forecast from Open-Meteo API | |
| weather_forecast = await fetch_rainfall_forecast(config["latitude"], config["longitude"], req.forecast_days) | |
| # Calibrations Setup | |
| dataset_mean = df_history["Volume_Total_Ton"].mean() | |
| real_baseline = config["normal_avg"] | |
| calibration_factor = real_baseline / dataset_mean | |
| o_r = (df_history["Vol_Sisa_Makanan_Ton"] / df_history["Volume_Total_Ton"]).mean() | |
| p_r = (df_history["Vol_Plastik_Ton"] / df_history["Volume_Total_Ton"]).mean() | |
| # Remaining ratios from official DLH Jakarta statistics: | |
| paper_r = 0.115 | |
| metal_r = 0.021 | |
| glass_r = 0.032 | |
| textile_r = 0.042 | |
| other_r = max(0.01, 1.0 - (o_r + p_r + paper_r + metal_r + glass_r + textile_r)) | |
| results = [] | |
| total_vol = 0.0 | |
| max_risk = "SAFE" | |
| # Chronos Forecasting Pipeline | |
| if req.model_type == "chronos": | |
| ctx = torch.tensor(df_history["Volume_Total_Ton"].values, dtype=torch.float32) | |
| forecast_vals = await run_in_threadpool(perform_inference, ctx, req.forecast_days) | |
| for i, base in enumerate(forecast_vals): | |
| curr_date = start_date + timedelta(days=i) | |
| d_str = curr_date.strftime("%Y-%m-%d") | |
| # Retrieve weather rain | |
| rain_val = req.rainfall_mm if (req.rainfall_mm > 0.0 and i == 0) else weather_forecast.get(d_str, 0.0) | |
| rain_m = 1.0 | |
| if rain_val > 20: | |
| rain_m = 1.02 + min((rain_val - 20) * 0.001, 0.03) | |
| # Events multiplier | |
| evt = events_data.get(d_str) | |
| evt_m = 1.0 | |
| info = None | |
| if evt and evt["crowd_scale"] > 0 and (req.location.lower() in evt["location"].lower() or evt["location"].lower() == "jakarta"): | |
| evt_m = 1.0 + 0.10 + min(evt["crowd_scale"] * 0.05, 0.25) | |
| info = f"{evt['event_name']} @ {evt['location']}" | |
| elif req.event_scale > 0: | |
| evt_m = 1.0 + req.event_scale * 0.10 | |
| raw_prediction = base * rain_m * evt_m | |
| calibrated_volume = round(float(raw_prediction * calibration_factor), 2) | |
| total_vol += calibrated_volume | |
| risk = get_risk_status(calibrated_volume, req.location) | |
| if risk == "CRITICAL": max_risk = "CRITICAL" | |
| elif risk == "WARNING" and max_risk != "CRITICAL": max_risk = "WARNING" | |
| hourly = distribute_to_hourly(calibrated_volume) if req.granularity == "hourly" else None | |
| results.append(PredictionResult( | |
| date=d_str, location=req.location, total_volume_ton=calibrated_volume, | |
| organic_waste_ton=round(calibrated_volume*o_r, 2), plastic_waste_ton=round(calibrated_volume*p_r, 2), | |
| paper_waste_ton=round(calibrated_volume*paper_r, 2), metal_waste_ton=round(calibrated_volume*metal_r, 2), | |
| glass_waste_ton=round(calibrated_volume*glass_r, 2), textile_waste_ton=round(calibrated_volume*textile_r, 2), | |
| other_waste_ton=round(calibrated_volume*other_r, 2), | |
| recommended_trucks=max(1, int(np.ceil(calibrated_volume/5))), | |
| risk_status=risk, event_info=info, hourly_breakdown=hourly | |
| )) | |
| # Gradient Boosting Regressor Pipeline | |
| elif req.model_type == "gradient_boosting": | |
| if model_gbr is None: | |
| raise HTTPException(503, "Gradient Boosting model not loaded.") | |
| for i in range(req.forecast_days): | |
| curr_date = start_date + timedelta(days=i) | |
| d_str = curr_date.strftime("%Y-%m-%d") | |
| rain_val = req.rainfall_mm if (req.rainfall_mm > 0.0 and i == 0) else weather_forecast.get(d_str, 0.0) | |
| rain_lag1 = req.rainfall_mm if (req.rainfall_mm > 0.0 and i == 1) else weather_forecast.get((curr_date - timedelta(days=1)).strftime("%Y-%m-%d"), 0.0) | |
| evt = events_data.get(d_str) | |
| has_event = 1 if (evt and (req.location.lower() in evt["location"].lower() or evt["location"].lower() == "jakarta")) else 0 | |
| crowd = float(evt["crowd_scale"]) if has_event else (float(req.event_scale) if i == 0 else 0.0) | |
| info = f"{evt['event_name']} @ {evt['location']}" if has_event else None | |
| # Fitur dataframe construction matching train.py | |
| features = pd.DataFrame([{ | |
| 'Penumpang_MRT': 85000, | |
| 'Ada_Event': has_event or (1 if (req.event_scale > 0 and i == 0) else 0), | |
| 'Curah_Hujan_mm': rain_val, | |
| 'Hujan_Kemarin': rain_lag1, | |
| 'Hari_Dalam_Minggu': curr_date.weekday(), | |
| 'Bulan': curr_date.month, | |
| 'Is_Weekend': 1 if curr_date.weekday() >= 5 else 0 | |
| }]) | |
| raw_pred = float(model_gbr.predict(features)[0]) | |
| calibrated_volume = round(float(raw_pred * calibration_factor), 2) | |
| total_vol += calibrated_volume | |
| risk = get_risk_status(calibrated_volume, req.location) | |
| if risk == "CRITICAL": max_risk = "CRITICAL" | |
| elif risk == "WARNING" and max_risk != "CRITICAL": max_risk = "WARNING" | |
| hourly = distribute_to_hourly(calibrated_volume) if req.granularity == "hourly" else None | |
| results.append(PredictionResult( | |
| date=d_str, location=req.location, total_volume_ton=calibrated_volume, | |
| organic_waste_ton=round(calibrated_volume*o_r, 2), plastic_waste_ton=round(calibrated_volume*p_r, 2), | |
| paper_waste_ton=round(calibrated_volume*paper_r, 2), metal_waste_ton=round(calibrated_volume*metal_r, 2), | |
| glass_waste_ton=round(calibrated_volume*glass_r, 2), textile_waste_ton=round(calibrated_volume*textile_r, 2), | |
| other_waste_ton=round(calibrated_volume*other_r, 2), | |
| recommended_trucks=max(1, int(np.ceil(calibrated_volume/5))), | |
| risk_status=risk, event_info=info, hourly_breakdown=hourly | |
| )) | |
| trucks = sum([r.recommended_trucks for r in results]) | |
| msg = f"CRITICAL at {req.location}!" if max_risk == "CRITICAL" else f"WARNING at {req.location}." if max_risk == "WARNING" else "Normal conditions." | |
| conf = 0.9828 if req.model_type == "gradient_boosting" else 0.92 | |
| # Return accuracy score dynamically (Chronos is default 0.92, GBR shows training test score ~0.93) | |
| conf = 0.9325 if req.model_type == "gradient_boosting" else 0.92 | |
| return APIResponse( | |
| status="success", message=msg, confidence_score=conf, | |
| data=PredictionData( | |
| prediction_results=results, | |
| logistics_plan=LogisticsPlan( | |
| trucks_needed=trucks, | |
| manpower=trucks*3, | |
| estimated_duration_hours=round(total_vol/5, 1), | |
| efficiency_rate="85% (Optimal)" | |
| ) | |
| ) | |
| ) | |
| except HTTPException: raise | |
| except Exception as e: | |
| logger.error(f"Prediction failed: {e}", exc_info=True) | |
| raise HTTPException(500, str(e)) | |
| async def predict_waste_volume_csv(req: PredictionRequest): | |
| res = await predict_waste_volume(req) | |
| output = io.StringIO() | |
| writer = csv.writer(output) | |
| # Write CSV Header | |
| writer.writerow([ | |
| "Date", "Location", "Total Volume (Tons)", | |
| "Organic Waste (Tons)", "Plastic Waste (Tons)", | |
| "Paper Waste (Tons)", "Metal Waste (Tons)", | |
| "Glass Waste (Tons)", "Textile Waste (Tons)", | |
| "Risk Status", "Event Info", "Recommended Trucks (5T)" | |
| ]) | |
| for r in res.data.prediction_results: | |
| writer.writerow([ | |
| r.date, r.location, r.total_volume_ton, | |
| r.organic_waste_ton, r.plastic_waste_ton, | |
| r.paper_waste_ton, r.metal_waste_ton, | |
| r.glass_waste_ton, r.textile_waste_ton, | |
| r.risk_status, r.event_info or "", r.recommended_trucks | |
| ]) | |
| output.seek(0) | |
| filename = f"waste_forecast_{req.location.replace(' ', '_')}_{req.forecast_days}d.csv" | |
| return StreamingResponse( | |
| io.BytesIO(output.getvalue().encode("utf-8")), | |
| media_type="text/csv", | |
| headers={"Content-Disposition": f"attachment; filename={filename}"} | |
| ) | |
| async def get_alerts(location: str = Query(None)): | |
| """Real-time alerts endpoint.""" | |
| if df_history is None: raise HTTPException(503, "Model not ready") | |
| alerts = [] | |
| today = datetime.now().date() | |
| for i in range(3): | |
| d = (today + timedelta(days=i)).strftime("%Y-%m-%d") | |
| evt = events_data.get(d) | |
| for loc, config in KECAMATAN_DATABASE.items(): | |
| if location and loc != location: continue | |
| baseline_vol = config["normal_avg"] | |
| if evt and evt["crowd_scale"] > 0 and (loc.lower() in evt["location"].lower() or evt["location"].lower() == "jakarta"): | |
| baseline_vol = config["normal_avg"] * 1.5 | |
| status = "CRITICAL" if baseline_vol > config["critical_threshold"] else "WARNING" if baseline_vol > config["warning_threshold"] else "SAFE" | |
| if status != "SAFE": | |
| alerts.append({ | |
| "date": d, "location": loc, "status": status, | |
| "estimated_volume_ton": baseline_vol, | |
| "message": f"Alert: {status} volume expected at {loc}" | |
| }) | |
| return AlertResponse(status="success", alert_count=len(alerts), alerts=alerts, last_updated=datetime.now().isoformat()) | |
| async def get_autopilot_data(): | |
| """Autonomous autopilot aggregator that predicts for all 44 kecamatan for today using GBR.""" | |
| if df_history is None: | |
| raise HTTPException(503, "Models not ready") | |
| today = datetime.now() | |
| d_str = today.strftime("%Y-%m-%d") | |
| total_vol = 0.0 | |
| total_trucks = 0 | |
| kecamatan_results = [] | |
| rainy_count = 0 | |
| # Check if there is an event today | |
| evt = events_data.get(d_str) | |
| for loc, config in KECAMATAN_DATABASE.items(): | |
| # Calibrations Setup | |
| dataset_mean = df_history["Volume_Total_Ton"].mean() | |
| real_baseline = config["normal_avg"] | |
| calibration_factor = real_baseline / dataset_mean | |
| # Check weather cache | |
| cache_key = f"{config['latitude']:.2f}_{config['longitude']:.2f}_7" | |
| rain_val = 0.0 | |
| if cache_key in WEATHER_CACHE: | |
| rain_val = WEATHER_CACHE[cache_key][0].get(d_str, 0.0) | |
| if rain_val > 1.0: rainy_count += 1 | |
| has_event = 1 if (evt and (loc.lower() in evt["location"].lower() or evt["location"].lower() == "jakarta")) else 0 | |
| # Build features for GBR | |
| features = pd.DataFrame([{ | |
| 'Penumpang_MRT': 85000, | |
| 'Ada_Event': has_event, | |
| 'Curah_Hujan_mm': rain_val, | |
| 'Hujan_Kemarin': 0.0, | |
| 'Hari_Dalam_Minggu': today.weekday(), | |
| 'Bulan': today.month, | |
| 'Is_Weekend': 1 if today.weekday() >= 5 else 0 | |
| }]) | |
| # Predict | |
| if model_gbr is not None: | |
| raw_pred = float(model_gbr.predict(features)[0]) | |
| else: | |
| raw_pred = dataset_mean # Fallback | |
| calibrated_volume = round(float(raw_pred * calibration_factor), 2) | |
| trucks = max(1, int(np.ceil(calibrated_volume / 5))) | |
| status = "CRITICAL" if calibrated_volume > config["critical_threshold"] else "WARNING" if calibrated_volume > config["warning_threshold"] else "SAFE" | |
| total_vol += calibrated_volume | |
| total_trucks += trucks | |
| kecamatan_results.append({ | |
| "location": loc, | |
| "volume_ton": calibrated_volume, | |
| "trucks": trucks, | |
| "status": status, | |
| "city": config["city"] | |
| }) | |
| # Sort by volume to get Top 5 | |
| kecamatan_results.sort(key=lambda x: x["volume_ton"], reverse=True) | |
| top_5 = kecamatan_results[:5] | |
| return { | |
| "status": "success", | |
| "date": d_str, | |
| "total_volume_ton": round(total_vol, 2), | |
| "total_trucks": total_trucks, | |
| "top_kecamatan": top_5, | |
| "rainy_regions": rainy_count, | |
| "event_today": evt["event_name"] if evt else None | |
| } |