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multi-hazard-warning-system / src /inference /predict.py

Deploy Multi-Hazard Warning System - MTL model for wildfire risk + AQI forecasting

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"""
predict.py — End-to-end inference pipeline given latitude, longitude, and date.
Orchestrates the full prediction workflow:
1. Fetch satellite/fire data for the given location
2. Fetch 7-day weather + AQI history
3. Preprocess both inputs using saved scalers
4. Run MTL model inference
5. Generate Grad-CAM overlay
6. Return structured prediction results
"""

import base64
import logging
import time
from datetime import datetime
from pathlib import Path
from typing import Dict, Optional, Tuple

import cv2
import numpy as np
import torch

from src.training.config import (
    DEVICE, PATCH_SIZE, TIMESERIES_WINDOW, TIMESERIES_FEATURES,
    AQI_FORECAST_HOURS, BEST_MODEL_PATH, PROCESSED_DIR,
    RISK_THRESHOLDS, AQI_CATEGORIES,
)

logger = logging.getLogger(__name__)


class Predictor:
    """
    End-to-end inference engine for wildfire risk + AQI predictions.
    Loads the trained MTL model, preprocesses inputs, runs inference,
    generates Grad-CAM overlays, and returns structured results.
    """

    def __init__(self, model=None, device: str = DEVICE):
        """
        Args:
            model: Pre-loaded model instance. If None, loads from checkpoint.
            device: Computation device.
        """
        self.device = device

        if model is not None:
            self.model = model.to(device).eval()
        else:
            self.model = self._load_model()

        # Load preprocessing scalers if available
        self.img_normalizer = self._load_image_normalizer()
        self.ts_scaler = self._load_timeseries_scaler()

        logger.info("Predictor initialized.")

    def _load_model(self):
        """Load trained model from checkpoint."""
        from src.models.fusion_model import load_model
        return load_model(str(BEST_MODEL_PATH), device=self.device)

    def _load_image_normalizer(self):
        """Load image normalizer if saved."""
        from src.data.preprocess import ImageNormalizer
        path = PROCESSED_DIR / "image_normalizer.npz"
        normalizer = ImageNormalizer()
        if path.exists():
            normalizer.load(path)
            logger.info("Loaded image normalizer from disk.")
        else:
            logger.warning("No saved image normalizer found — using identity transform.")
            normalizer.channel_means = np.zeros(4)
            normalizer.channel_stds = np.ones(4)
        return normalizer

    def _load_timeseries_scaler(self):
        """Load time-series scaler if saved."""
        from src.data.preprocess import TimeSeriesScaler
        path = PROCESSED_DIR / "timeseries_scaler.npz"
        scaler = TimeSeriesScaler()
        if path.exists():
            scaler.load(path)
            logger.info("Loaded timeseries scaler from disk.")
        else:
            logger.warning("No saved timeseries scaler found — using identity transform.")
            scaler._is_fitted = True
            scaler.scaler.data_min_ = np.zeros(TIMESERIES_FEATURES)
            scaler.scaler.data_max_ = np.ones(TIMESERIES_FEATURES)
            scaler.scaler.scale_ = np.ones(TIMESERIES_FEATURES)
            scaler.scaler.min_ = np.zeros(TIMESERIES_FEATURES)
            scaler.scaler.n_features_in_ = TIMESERIES_FEATURES
        return scaler

    def fetch_inputs(
        self,
        latitude: float,
        longitude: float,
        date: str,
    ) -> Tuple[np.ndarray, np.ndarray]:
        """
        Fetch and preprocess inputs from data sources.
        Args:
            latitude: Location latitude.
            longitude: Location longitude.
            date: Target date (YYYY-MM-DD).
        Returns:
            Tuple of (image_patch, timeseries):
              - image_patch: (1, 4, 128, 128)
              - timeseries: (1, 7, 6)
        """
        from src.data.fetch_firms import generate_synthetic_fire_data
        from src.data.fetch_weather import fetch_weather_for_location
        from src.data.fetch_aqi import fetch_aqi_for_location

        # Fetch satellite/fire data
        # For inference, generate a single synthetic patch seeded by location
        seed = int(abs(latitude * 1000 + longitude * 100)) % (2**31)
        np.random.seed(seed)
        image, _ = generate_synthetic_fire_data(num_samples=1)
        image = image[0:1]  # (1, 4, 128, 128)

        # Fetch weather data
        weather = fetch_weather_for_location(latitude, longitude, date)
        # weather shape: (7, 5)

        # Fetch AQI data
        aqi_hist, _ = fetch_aqi_for_location(latitude, longitude, date)
        # aqi_hist shape: (7, 1)

        # Combine weather + AQI into timeseries
        timeseries = np.concatenate([weather, aqi_hist], axis=1)  # (7, 6)
        timeseries = timeseries[np.newaxis, ...]  # (1, 7, 6)

        # Normalize
        image = self.img_normalizer.transform(image)
        timeseries = self.ts_scaler.transform(timeseries)

        return image, timeseries

    @torch.no_grad()
    def predict(
        self,
        latitude: float,
        longitude: float,
        date: str,
    ) -> Dict:
        """
        Run end-to-end prediction for a given location and date.
        Args:
            latitude: Location latitude (-90 to 90).
            longitude: Location longitude (-180 to 180).
            date: Target date (YYYY-MM-DD).
        Returns:
            Dict with all prediction results.
        """
        start_time = time.time()
        logger.info(f"Predicting for ({latitude}, {longitude}) on {date}...")

        # Step 1: Fetch and preprocess inputs
        image_np, timeseries_np = self.fetch_inputs(latitude, longitude, date)

        # Step 2: Convert to tensors
        image_tensor = torch.tensor(image_np, dtype=torch.float32).to(self.device)
        ts_tensor = torch.tensor(timeseries_np, dtype=torch.float32).to(self.device)

        # Step 3: Run MTL model inference
        output = self.model(image_tensor, ts_tensor)

        risk_map = output["risk_map"][0].cpu().numpy()       # (128, 128)
        aqi_forecast = output["aqi_forecast"][0].cpu().numpy() * 500.0  # (72,)

        # Step 4: Compute risk level
        risk_score = float(risk_map.mean())
        risk_level = self._classify_risk(risk_score)

        # Step 5: Generate Grad-CAM overlay
        heatmap_b64, gradcam_b64 = self._generate_visualizations(
            image_tensor, ts_tensor, risk_map, image_np
        )

        # Step 6: AQI analysis
        peak_hour = int(np.argmax(aqi_forecast))
        peak_value = float(aqi_forecast[peak_hour])

        elapsed = time.time() - start_time
        logger.info(f"Prediction complete in {elapsed:.2f}s — "
                     f"Risk: {risk_level} ({risk_score:.3f})")

        return {
            "risk_level": risk_level,
            "risk_score": round(risk_score, 4),
            "risk_map": risk_map,
            "heatmap_base64": heatmap_b64,
            "gradcam_base64": gradcam_b64,
            "aqi_forecast": aqi_forecast.tolist(),
            "forecast_hours": list(range(1, AQI_FORECAST_HOURS + 1)),
            "peak_aqi_hour": peak_hour + 1,
            "peak_aqi_value": round(peak_value, 1),
            "latitude": latitude,
            "longitude": longitude,
            "date": date,
            "prediction_timestamp": datetime.now().isoformat(),
            "inference_time_ms": round(elapsed * 1000, 1),
        }

    def _classify_risk(self, score: float) -> str:
        """Classify risk score into level."""
        for level, (low, high) in RISK_THRESHOLDS.items():
            if low <= score < high:
                return level
        return "Extreme"

    def _generate_visualizations(
        self,
        image_tensor: torch.Tensor,
        ts_tensor: torch.Tensor,
        risk_map: np.ndarray,
        image_np: np.ndarray,
    ) -> Tuple[str, str]:
        """
        Generate heatmap and Grad-CAM base64 images.
        Returns:
            Tuple of (heatmap_base64, gradcam_base64).
        """
        # Heatmap: risk map as colored image
        heatmap_uint8 = np.uint8(255 * np.clip(risk_map, 0, 1))
        heatmap_color = cv2.applyColorMap(heatmap_uint8, cv2.COLORMAP_JET)
        _, heatmap_buf = cv2.imencode(".png", heatmap_color)
        heatmap_b64 = base64.b64encode(heatmap_buf).decode("utf-8")

        # Grad-CAM overlay
        try:
            from src.models.gradcam import GradCAM
            gradcam = GradCAM(self.model)
            overlay = gradcam.generate_overlay(image_tensor, ts_tensor)
            _, gradcam_buf = cv2.imencode(".png", overlay)
            gradcam_b64 = base64.b64encode(gradcam_buf).decode("utf-8")
        except Exception as e:
            logger.warning(f"Grad-CAM generation failed: {e}")
            gradcam_b64 = heatmap_b64  # Fallback to regular heatmap

        return heatmap_b64, gradcam_b64


# Module-level singleton predictor
_predictor: Optional[Predictor] = None


def get_predictor(model=None) -> Predictor:
    """Get or create singleton Predictor instance."""
    global _predictor
    if _predictor is None:
        _predictor = Predictor(model=model)
    return _predictor


def predict(latitude: float, longitude: float, date: str) -> Dict:
    """
    Convenience function for single prediction.
    Args:
        latitude: Location latitude.
        longitude: Location longitude.
        date: Target date (YYYY-MM-DD).
    Returns:
        Prediction results dict.
    """
    predictor = get_predictor()
    return predictor.predict(latitude, longitude, date)


if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)

    result = predict(37.5, -120.3, "2024-06-15")

    print(f"\nRisk Level: {result['risk_level']}")
    print(f"Risk Score: {result['risk_score']}")
    print(f"Peak AQI: {result['peak_aqi_value']} at hour {result['peak_aqi_hour']}")
    print(f"Inference Time: {result['inference_time_ms']}ms")
    print(f"Forecast length: {len(result['aqi_forecast'])} hours")

	"""
	predict.py — End-to-end inference pipeline given latitude, longitude, and date.

	Orchestrates the full prediction workflow:
	1. Fetch satellite/fire data for the given location
	2. Fetch 7-day weather + AQI history
	3. Preprocess both inputs using saved scalers
	4. Run MTL model inference
	5. Generate Grad-CAM overlay
	6. Return structured prediction results
	"""

	import base64
	import logging
	import time
	from datetime import datetime
	from pathlib import Path
	from typing import Dict, Optional, Tuple

	import cv2
	import numpy as np
	import torch

	from src.training.config import (
	DEVICE, PATCH_SIZE, TIMESERIES_WINDOW, TIMESERIES_FEATURES,
	AQI_FORECAST_HOURS, BEST_MODEL_PATH, PROCESSED_DIR,
	RISK_THRESHOLDS, AQI_CATEGORIES,
	)

	logger = logging.getLogger(__name__)


	class Predictor:
	"""
	End-to-end inference engine for wildfire risk + AQI predictions.

	Loads the trained MTL model, preprocesses inputs, runs inference,
	generates Grad-CAM overlays, and returns structured results.
	"""

	def __init__(self, model=None, device: str = DEVICE):
	"""
	Args:
	model: Pre-loaded model instance. If None, loads from checkpoint.
	device: Computation device.
	"""
	self.device = device

	if model is not None:
	self.model = model.to(device).eval()
	else:
	self.model = self._load_model()

	# Load preprocessing scalers if available
	self.img_normalizer = self._load_image_normalizer()
	self.ts_scaler = self._load_timeseries_scaler()

	logger.info("Predictor initialized.")

	def _load_model(self):
	"""Load trained model from checkpoint."""
	from src.models.fusion_model import load_model
	return load_model(str(BEST_MODEL_PATH), device=self.device)

	def _load_image_normalizer(self):
	"""Load image normalizer if saved."""
	from src.data.preprocess import ImageNormalizer
	path = PROCESSED_DIR / "image_normalizer.npz"
	normalizer = ImageNormalizer()
	if path.exists():
	normalizer.load(path)
	logger.info("Loaded image normalizer from disk.")
	else:
	logger.warning("No saved image normalizer found — using identity transform.")
	normalizer.channel_means = np.zeros(4)
	normalizer.channel_stds = np.ones(4)
	return normalizer

	def _load_timeseries_scaler(self):
	"""Load time-series scaler if saved."""
	from src.data.preprocess import TimeSeriesScaler
	path = PROCESSED_DIR / "timeseries_scaler.npz"
	scaler = TimeSeriesScaler()
	if path.exists():
	scaler.load(path)
	logger.info("Loaded timeseries scaler from disk.")
	else:
	logger.warning("No saved timeseries scaler found — using identity transform.")
	scaler._is_fitted = True
	scaler.scaler.data_min_ = np.zeros(TIMESERIES_FEATURES)
	scaler.scaler.data_max_ = np.ones(TIMESERIES_FEATURES)
	scaler.scaler.scale_ = np.ones(TIMESERIES_FEATURES)
	scaler.scaler.min_ = np.zeros(TIMESERIES_FEATURES)
	scaler.scaler.n_features_in_ = TIMESERIES_FEATURES
	return scaler

	def fetch_inputs(
	self,
	latitude: float,
	longitude: float,
	date: str,
	) -> Tuple[np.ndarray, np.ndarray]:
	"""
	Fetch and preprocess inputs from data sources.

	Args:
	latitude: Location latitude.
	longitude: Location longitude.
	date: Target date (YYYY-MM-DD).

	Returns:
	Tuple of (image_patch, timeseries):
	- image_patch: (1, 4, 128, 128)
	- timeseries: (1, 7, 6)
	"""
	from src.data.fetch_firms import generate_synthetic_fire_data
	from src.data.fetch_weather import fetch_weather_for_location
	from src.data.fetch_aqi import fetch_aqi_for_location

	# Fetch satellite/fire data
	# For inference, generate a single synthetic patch seeded by location
	seed = int(abs(latitude * 1000 + longitude * 100)) % (2**31)
	np.random.seed(seed)
	image, _ = generate_synthetic_fire_data(num_samples=1)
	image = image[0:1] # (1, 4, 128, 128)

	# Fetch weather data
	weather = fetch_weather_for_location(latitude, longitude, date)
	# weather shape: (7, 5)

	# Fetch AQI data
	aqi_hist, _ = fetch_aqi_for_location(latitude, longitude, date)
	# aqi_hist shape: (7, 1)

	# Combine weather + AQI into timeseries
	timeseries = np.concatenate([weather, aqi_hist], axis=1) # (7, 6)
	timeseries = timeseries[np.newaxis, ...] # (1, 7, 6)

	# Normalize
	image = self.img_normalizer.transform(image)
	timeseries = self.ts_scaler.transform(timeseries)

	return image, timeseries

	@torch.no_grad()
	def predict(
	self,
	latitude: float,
	longitude: float,
	date: str,
	) -> Dict:
	"""
	Run end-to-end prediction for a given location and date.

	Args:
	latitude: Location latitude (-90 to 90).
	longitude: Location longitude (-180 to 180).
	date: Target date (YYYY-MM-DD).

	Returns:
	Dict with all prediction results.
	"""
	start_time = time.time()
	logger.info(f"Predicting for ({latitude}, {longitude}) on {date}...")

	# Step 1: Fetch and preprocess inputs
	image_np, timeseries_np = self.fetch_inputs(latitude, longitude, date)

	# Step 2: Convert to tensors
	image_tensor = torch.tensor(image_np, dtype=torch.float32).to(self.device)
	ts_tensor = torch.tensor(timeseries_np, dtype=torch.float32).to(self.device)

	# Step 3: Run MTL model inference
	output = self.model(image_tensor, ts_tensor)

	risk_map = output["risk_map"][0].cpu().numpy() # (128, 128)
	aqi_forecast = output["aqi_forecast"][0].cpu().numpy() * 500.0 # (72,)

	# Step 4: Compute risk level
	risk_score = float(risk_map.mean())
	risk_level = self._classify_risk(risk_score)

	# Step 5: Generate Grad-CAM overlay
	heatmap_b64, gradcam_b64 = self._generate_visualizations(
	image_tensor, ts_tensor, risk_map, image_np
	)

	# Step 6: AQI analysis
	peak_hour = int(np.argmax(aqi_forecast))
	peak_value = float(aqi_forecast[peak_hour])

	elapsed = time.time() - start_time
	logger.info(f"Prediction complete in {elapsed:.2f}s — "
	f"Risk: {risk_level} ({risk_score:.3f})")

	return {
	"risk_level": risk_level,
	"risk_score": round(risk_score, 4),
	"risk_map": risk_map,
	"heatmap_base64": heatmap_b64,
	"gradcam_base64": gradcam_b64,
	"aqi_forecast": aqi_forecast.tolist(),
	"forecast_hours": list(range(1, AQI_FORECAST_HOURS + 1)),
	"peak_aqi_hour": peak_hour + 1,
	"peak_aqi_value": round(peak_value, 1),
	"latitude": latitude,
	"longitude": longitude,
	"date": date,
	"prediction_timestamp": datetime.now().isoformat(),
	"inference_time_ms": round(elapsed * 1000, 1),
	}

	def _classify_risk(self, score: float) -> str:
	"""Classify risk score into level."""
	for level, (low, high) in RISK_THRESHOLDS.items():
	if low <= score < high:
	return level
	return "Extreme"

	def _generate_visualizations(
	self,
	image_tensor: torch.Tensor,
	ts_tensor: torch.Tensor,
	risk_map: np.ndarray,
	image_np: np.ndarray,
	) -> Tuple[str, str]:
	"""
	Generate heatmap and Grad-CAM base64 images.

	Returns:
	Tuple of (heatmap_base64, gradcam_base64).
	"""
	# Heatmap: risk map as colored image
	heatmap_uint8 = np.uint8(255 * np.clip(risk_map, 0, 1))
	heatmap_color = cv2.applyColorMap(heatmap_uint8, cv2.COLORMAP_JET)
	_, heatmap_buf = cv2.imencode(".png", heatmap_color)
	heatmap_b64 = base64.b64encode(heatmap_buf).decode("utf-8")

	# Grad-CAM overlay
	try:
	from src.models.gradcam import GradCAM
	gradcam = GradCAM(self.model)
	overlay = gradcam.generate_overlay(image_tensor, ts_tensor)
	_, gradcam_buf = cv2.imencode(".png", overlay)
	gradcam_b64 = base64.b64encode(gradcam_buf).decode("utf-8")
	except Exception as e:
	logger.warning(f"Grad-CAM generation failed: {e}")
	gradcam_b64 = heatmap_b64 # Fallback to regular heatmap

	return heatmap_b64, gradcam_b64


	# Module-level singleton predictor
	_predictor: Optional[Predictor] = None


	def get_predictor(model=None) -> Predictor:
	"""Get or create singleton Predictor instance."""
	global _predictor
	if _predictor is None:
	_predictor = Predictor(model=model)
	return _predictor


	def predict(latitude: float, longitude: float, date: str) -> Dict:
	"""
	Convenience function for single prediction.

	Args:
	latitude: Location latitude.
	longitude: Location longitude.
	date: Target date (YYYY-MM-DD).

	Returns:
	Prediction results dict.
	"""
	predictor = get_predictor()
	return predictor.predict(latitude, longitude, date)


	if __name__ == "__main__":
	logging.basicConfig(level=logging.INFO)

	result = predict(37.5, -120.3, "2024-06-15")

	print(f"\nRisk Level: {result['risk_level']}")
	print(f"Risk Score: {result['risk_score']}")
	print(f"Peak AQI: {result['peak_aqi_value']} at hour {result['peak_aqi_hour']}")
	print(f"Inference Time: {result['inference_time_ms']}ms")
	print(f"Forecast length: {len(result['aqi_forecast'])} hours")