post request works

roadcast/app.py

@@ -1,75 +1,35 @@
-from flask import Flask, request, jsonify
-from dotenv import load_dotenv
-
-# Load environment variables from .env file
-load_dotenv()
-
-app = Flask(__name__)
-import os
-import threading
-import json
-
-# ML imports are lazy to avoid heavy imports on simple runs
-
-
-@app.route('/get-data', methods=['GET'])
-def get_data():
-    # Example GET request handler
-    data = {"message": "Hello from Flask!"}
-    return jsonify(data)
-
-@app.route('/post-data', methods=['POST'])
-def post_data():
-    # Example POST request handler
-    content = request.json
-    # Process content or call AI model here
-    response = {"you_sent": content}
-    return jsonify(response)
-
-
-@app.route('/train', methods=['POST'])
-def train_endpoint():
-    """Trigger training. Expects JSON: {"data_root": "path/to/data", "epochs": 3}
-    Training runs in a background thread and saves model to model.pth in repo root.
-    """
-    payload = request.json or {}
-    data_root = payload.get('data_root')
-    epochs = int(payload.get('epochs', 3))
-    if not data_root or not os.path.isdir(data_root):
-        return jsonify({"error": "data_root must be a valid directory path"}), 400
-
-    def _run_training():
-        from train import train
-        train(data_root, epochs=epochs)
-
-    t = threading.Thread(target=_run_training, daemon=True)
-    t.start()
-    return jsonify({"status": "training_started"})
-
-
 @app.route('/predict', methods=['POST', 'GET'])
 def predict_endpoint():
     """Predict route between two points given source and destination with lat and lon.
 
+
     Expectation:
     - POST with JSON: {"source": {"lat": .., "lon": ..}, "destination": {"lat": .., "lon": ..}}
     - GET returns usage instructions for quick browser testing.
     """
-    if request.method == 'GET':
-        return jsonify({
-            "info": "This endpoint expects a POST with JSON body.",
-            "example": {
+    example_payload = {
         "source": {"lat": 38.9, "lon": -77.0},
         "destination": {"lat": 38.95, "lon": -77.02}
-            },
-            "note": "Use POST to receive a prediction. Example: curl -X POST -H 'Content-Type: application/json' -d '{\"source\": {\"lat\": 38.9, \"lon\": -77.0}, \"destination\": {\"lat\": 38.95, \"lon\": -77.02}}' http://127.0.0.1:5000/predict"
-        }), 200
+    }
+    info = "This endpoint expects a POST with JSON body."
+    note = (
+        "Use POST to receive a prediction. Example: curl -X POST -H 'Content-Type: application/json' "
+        "-d '{\"source\": {\"lat\": 38.9, \"lon\": -77.0}, \"destination\": {\"lat\": 38.95, \"lon\": -77.02}}' "
+        "http://127.0.0.1:5000/predict"
+    )
+
+
+    if request.method == 'GET':
+        return jsonify({"info": info, "example": example_payload, "note": note}), 200
+
 
     data = request.json or {}
     source = data.get('source')
     destination = data.get('destination')
     if not source or not destination:
         return jsonify({"error": "both 'source' and 'destination' fields are required"}), 400
+
+
     try:
         src_lat = float(source.get('lat'))
         src_lon = float(source.get('lon'))
@@ -78,107 +38,93 @@ def predict_endpoint():
     except (TypeError, ValueError):
         return jsonify({"error": "invalid lat or lon values; must be numbers"}), 400
 
+
     # Ensure compute_reroute exists and is callable
     try:
-        from openweather_client import compute_reroute
+        from openmeteo_client import compute_reroute
     except Exception as e:
         return jsonify({
-            "error": "compute_reroute not found in openweather_client",
+            "error": "compute_reroute not found in openmeteo_client",
             "detail": str(e),
-            "hint": "Provide openweather_client.compute_reroute or implement a callable that accepts (src_lat, src_lon, dst_lat, dst_lon)"
+            "hint": "Provide openmeteo_client.compute_reroute "
+                    "(Open-Meteo does not need an API key)"
         }), 500
 
-    if not callable(compute_reroute):
-        return jsonify({"error": "openweather_client.compute_reroute is not callable"}), 500
 
-    # Call compute_reroute with fallback strategies
+    if not callable(compute_reroute):
+        return jsonify({"error": "openmeteo_client.compute_reroute is not callable"}), 500
+
+
+    def _extract_index(res):
+        if res is None:
+            return None
+        if isinstance(res, (int, float)):
+            return int(res)
+        if isinstance(res, dict):
+            for k in ('index', 'idx', 'cluster', 'cluster_idx', 'label_index', 'label_idx'):
+                if k in res:
                     try:
+                        return int(res[k])
+                    except Exception:
+                        return res[k]
+        return None
+
+
+    # Call compute_reroute (Open-Meteo requires no API key)
     try:
         result = compute_reroute(src_lat, src_lon, dst_lat, dst_lon)
-        except TypeError:
-            # fallback: single payload dict
-            payload = {'source': {'lat': src_lat, 'lon': src_lon}, 'destination': {'lat': dst_lat, 'lon': dst_lon}}
-            result = compute_reroute(payload)
+        called_with = "positional"
 
-        # Normalize response
-        if isinstance(result, dict):
-            return jsonify(result)
+
+        diagnostics = {"type": type(result).__name__}
+        try:
+            diagnostics["repr"] = repr(result)[:1000]
+        except Exception:
+            diagnostics["repr"] = "<unrepr-able>"
+
+
+        # Normalize return types
+        if isinstance(result, (list, tuple)):
+            idx = None
+            for el in result:
+                idx = _extract_index(el)
+                if idx is not None:
+                    break
+            prediction = {"items": list(result)}
+            index = idx
+        elif isinstance(result, dict):
+            index = _extract_index(result)
+            prediction = result
+        elif isinstance(result, (int, float, str)):
+            index = _extract_index(result)
+            prediction = {"value": result}
         else:
-            return jsonify({"result": result})
+            index = None
+            prediction = {"value": result}
+
+
+        response_payload = {
+            "index": index,
+            "prediction": prediction,
+            "called_with": called_with,
+            "diagnostics": diagnostics,
+            "example": example_payload,
+            "info": info,
+            "note": note
+        }
+
+
+        # Add warning if no routing/index info found
+        expected_keys = ('route', 'path', 'distance', 'directions', 'index', 'idx', 'cluster')
+        if (not isinstance(prediction, dict) or not any(k in prediction for k in expected_keys)) and index is None:
+            response_payload["warning"] = (
+                "No routing/index information returned from compute_reroute. "
+                "See diagnostics for details."
+            )
+
+
+        return jsonify(response_payload)
+
+
     except Exception as e:
         return jsonify({"error": "compute_reroute invocation failed", "detail": str(e)}), 500
-
-
-@app.route('/')
-def home():
-    return "<h1>Welcome to the Flask App</h1><p>Try /get-data or /health endpoints.</p>"
-
-@app.route('/predict-roadrisk', methods=['POST'])
-def predict_roadrisk():
-    """Proxy endpoint to predict a roadrisk cluster from lat/lon/datetime.
-
-    Expects JSON body with: {"lat": 38.9, "lon": -77.0, "datetime": "2025-09-27T12:00:00", "roadrisk_url": "https://..."}
-    If roadrisk_url is not provided the endpoint will call OpenWeather OneCall (requires API key via OPENWEATHER_KEY env var).
-    """
-    payload = request.json or {}
-    lat = payload.get('lat')
-    lon = payload.get('lon')
-    dt = payload.get('datetime')
-    street = payload.get('street', '')
-    roadrisk_url = payload.get('roadrisk_url')
-    # prefer explicit api_key in request, otherwise read from OPENWEATHER_API_KEY env var
-    api_key = payload.get('api_key') or os.environ.get('OPENWEATHER_API_KEY')
-
-    if lat is None or lon is None:
-        return jsonify({"error": "lat and lon are required fields"}), 400
-
-    try:
-        from openweather_inference import predict_from_openweather
-        # pass api_key (may be None) to the inference helper; helper will raise if a key is required
-        res = predict_from_openweather(
-            lat, lon,
-            dt_iso=dt,
-            street=street,
-            api_key=api_key,
-            train_csv=os.path.join(os.getcwd(), 'data.csv'),
-            preprocess_meta=None,
-            model_path=os.path.join(os.getcwd(), 'model.pth'),
-            centers_path=os.path.join(os.getcwd(), 'kmeans_centers_all.npz'),
-            roadrisk_url=roadrisk_url
-        )
-        return jsonify(res)
-    except Exception as e:
-        return jsonify({"error": str(e)}), 500
-
-
-@app.route('/health', methods=['GET'])
-def health():
-    """Return status of loaded ML artifacts (model, centers, preprocess_meta)."""
-    try:
-        from openweather_inference import init_inference
-        status = init_inference()
-        return jsonify({'ok': True, 'artifacts': status})
-    except Exception as e:
-        return jsonify({'ok': False, 'error': str(e)}), 500
-
-if __name__ == '__main__':
-    # eager load model/artifacts at startup (best-effort)
-    try:
-        from openweather_inference import init_inference
-        init_inference()
-    except Exception:
-        pass
-    app.run(debug=True)
-
-# @app.route('/post-data', methods=['POST'])
-# def post_data():
-#     content = request.json
-#     user_input = content.get('input')
-
-#     # Example: Simple echo AI (replace with real AI model code)
-#     ai_response = f"AI received: {user_input}"
-
-#     return jsonify({"response": ai_response})
-#     ai_response = f"AI received: {user_input}"
-
-#     return jsonify({"response": ai_response"})

roadcast/openmeteo_client.py

@@ -0,0 +1,339 @@
+"""Open-Meteo historical weather client + simple road-risk heuristics.
+
+Backwards-compatible API:
+- fetch_weather(lat, lon, params=None, api_key=None)
+- fetch_road_risk(lat, lon, extra_params=None, api_key=None, roadrisk_url=None)
+- get_risk_score(lat, lon, **fetch_kwargs)
+- compute_reroute(...)
+- compute_index_and_reroute(...)
+"""
+import os
+from typing import Tuple, Dict, Any, Optional, Callable, List
+import requests
+import heapq
+import math
+from datetime import date, timedelta
+
+# Open-Meteo archive endpoint (no API key required)
+BASE_ARCHIVE_URL = "https://archive-api.open-meteo.com/v1/archive"
+
+
+def fetch_weather(lat: float, lon: float, params: Optional[dict] = None, api_key: Optional[str] = None) -> dict:
+	"""Fetch historical weather from Open-Meteo archive API.
+
+	Params may include 'start_date', 'end_date' (YYYY-MM-DD) and 'hourly' (comma-separated vars).
+	Defaults to yesterday..today and hourly variables useful for road risk.
+	(api_key parameter is accepted for compatibility but ignored.)
+	"""
+	if params is None:
+		params = {}
+
+	today = date.today()
+	start = params.get("start_date", (today - timedelta(days=1)).isoformat())
+	end = params.get("end_date", today.isoformat())
+
+	hourly = params.get(
+		"hourly",
+		",".join(["temperature_2m", "relativehumidity_2m", "windspeed_10m", "precipitation", "weathercode"])
+	)
+
+	query = {
+		"latitude": lat,
+		"longitude": lon,
+		"start_date": start,
+		"end_date": end,
+		"hourly": hourly,
+		"timezone": params.get("timezone", "UTC"),
+	}
+
+	resp = requests.get(BASE_ARCHIVE_URL, params=query, timeout=15)
+	resp.raise_for_status()
+	return resp.json()
+
+
+def fetch_road_risk(
+	lat: float,
+	lon: float,
+	extra_params: Optional[dict] = None,
+	api_key: Optional[str] = None,
+	roadrisk_url: Optional[str] = None
+) -> Tuple[dict, Dict[str, Any]]:
+	"""
+	Compute a simple road risk estimation using Open-Meteo historical weather.
+
+	Returns (raw_data, features) where features includes 'road_risk_score' (float).
+	api_key and roadrisk_url are accepted for backward compatibility but ignored.
+	"""
+	params = {}
+	if extra_params:
+		params.update(extra_params)
+
+	# fetch weather via Open-Meteo archive
+	try:
+		data = fetch_weather(lat, lon, params=params)
+	except Exception as e:
+		features: Dict[str, Any] = {"road_risk_score": 0.0, "error": str(e)}
+		return {}, features
+
+	hourly = data.get("hourly", {}) if isinstance(data, dict) else {}
+
+	def _arr_mean(key):
+		arr = hourly.get(key)
+		if isinstance(arr, list) and arr:
+			valid = [float(x) for x in arr if x is not None]
+			return sum(valid) / max(1, len(valid)) if valid else None
+		return None
+
+	def _arr_max(key):
+		arr = hourly.get(key)
+		if isinstance(arr, list) and arr:
+			valid = [float(x) for x in arr if x is not None]
+			return max(valid) if valid else None
+		return None
+
+	precip_mean = _arr_mean("precipitation")
+	wind_mean = _arr_mean("windspeed_10m")
+	wind_max = _arr_max("windspeed_10m")
+	temp_mean = _arr_mean("temperature_2m")
+	humidity_mean = _arr_mean("relativehumidity_2m")
+	weathercodes = hourly.get("weathercode", [])
+
+	# heuristic risk scoring:
+	risk = 0.0
+	if precip_mean is not None:
+		risk += float(precip_mean) * 2.0
+	if wind_mean is not None:
+		risk += float(wind_mean) * 0.1
+	if wind_max is not None and float(wind_max) > 15.0:
+		risk += 1.0
+	if humidity_mean is not None and float(humidity_mean) > 85.0:
+		risk += 0.5
+	try:
+		# sample Open-Meteo weather codes that indicate precipitation/snow
+		if any(int(wc) in (51, 61, 63, 65, 80, 81, 82, 71, 73, 75, 85, 86) for wc in weathercodes if wc is not None):
+			risk += 1.0
+	except Exception:
+		pass
+
+	if not math.isfinite(risk):
+		risk = 0.0
+	if risk < 0:
+		risk = 0.0
+
+	features: Dict[str, Any] = {
+		"precipitation_mean": float(precip_mean) if precip_mean is not None else None,
+		"wind_mean": float(wind_mean) if wind_mean is not None else None,
+		"wind_max": float(wind_max) if wind_max is not None else None,
+		"temp_mean": float(temp_mean) if temp_mean is not None else None,
+		"humidity_mean": float(humidity_mean) if humidity_mean is not None else None,
+		"road_risk_score": float(risk),
+	}
+
+	# include some raw metadata if present
+	if "latitude" in data:
+		features["latitude"] = data.get("latitude")
+	if "longitude" in data:
+		features["longitude"] = data.get("longitude")
+	if "generationtime_ms" in data:
+		features["generationtime_ms"] = data.get("generationtime_ms")
+
+	return data, features
+
+
+def _haversine_km(a_lat: float, a_lon: float, b_lat: float, b_lon: float) -> float:
+	# returns distance in kilometers
+	R = 6371.0
+	lat1, lon1, lat2, lon2 = map(math.radians, (a_lat, a_lon, b_lat, b_lon))
+	dlat = lat2 - lat1
+	dlon = lon2 - lon1
+	h = math.sin(dlat / 2) ** 2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon / 2) ** 2
+	return 2 * R * math.asin(min(1.0, math.sqrt(h)))
+
+
+def risk_to_index(risk_score: float, max_risk: float = 10.0, num_bins: int = 10) -> int:
+	"""
+	Map a numeric risk_score to an integer index 1..num_bins (higher => more risky).
+	"""
+	if risk_score is None:
+		return 1
+	r = float(risk_score)
+	if r <= 0:
+		return 1
+	if r >= max_risk:
+		return num_bins
+	bin_width = max_risk / float(num_bins)
+	return int(r // bin_width) + 1
+
+
+def get_risk_score(lat: float, lon: float, **fetch_kwargs) -> float:
+	"""Wrapper: calls fetch_road_risk and returns features['road_risk_score'] (float)."""
+	_, features = fetch_road_risk(lat, lon, extra_params=fetch_kwargs)
+	return float(features.get("road_risk_score", 0.0))
+
+
+def compute_reroute(
+    start_lat: float,
+    start_lon: float,
+    end_lat: float,
+    end_lon: float,
+    risk_provider: Callable[[float, float], float] = None,
+    n_lat: int = 20,
+    n_lon: int = 20,
+    distance_weight: float = 0.1,
+    max_calls: Optional[int] = None
+) -> Dict[str, Any]:
+	"""
+	Plan a path from (start_lat, start_lon) to (end_lat, end_lon) that avoids risky areas.
+	Uses Dijkstra's algorithm over a lat/lon grid with cost = avg risk + distance_weight * distance.
+	"""
+	if risk_provider is None:
+		risk_provider = lambda lat, lon: get_risk_score(lat, lon)
+
+	min_lat = min(start_lat, end_lat)
+	max_lat = max(start_lat, end_lat)
+	min_lon = min(start_lon, end_lon)
+	max_lon = max(start_lon, end_lon)
+
+	lat_padding = (max_lat - min_lat) * 0.2
+	lon_padding = (max_lon - min_lon) * 0.2
+	min_lat -= lat_padding
+	max_lat += lat_padding
+	min_lon -= lon_padding
+	max_lon += lon_padding
+
+	lat_step = (max_lat - min_lat) / (n_lat - 1) if n_lat > 1 else 0.0
+	lon_step = (max_lon - min_lon) / (n_lon - 1) if n_lon > 1 else 0.0
+
+	coords = []
+	for i in range(n_lat):
+		for j in range(n_lon):
+			coords.append((min_lat + i * lat_step, min_lon + j * lon_step))
+
+	risks = []
+	calls = 0
+	for lat, lon in coords:
+		if max_calls is not None and calls >= max_calls:
+			risks.append(float('inf'))
+			continue
+		try:
+			risk = risk_provider(lat, lon)
+		except Exception:
+			risk = float('inf')
+		risks.append(float(risk))
+		calls += 1
+
+	def idx(i, j):
+		return i * n_lon + j
+
+	def find_closest(lat, lon):
+		i = round((lat - min_lat) / (lat_step if lat_step != 0 else 1e-9))
+		j = round((lon - min_lon) / (lon_step if lon_step != 0 else 1e-9))
+		i = max(0, min(n_lat - 1, i))
+		j = max(0, min(n_lon - 1, j))
+		return idx(i, j)
+
+	start_idx = find_closest(start_lat, start_lon)
+	end_idx = find_closest(end_lat, end_lon)
+
+	N = len(coords)
+	dist = [math.inf] * N
+	prev = [None] * N
+	dist[start_idx] = 0.0
+	pq = [(0.0, start_idx)]
+
+	while pq:
+		cost, u = heapq.heappop(pq)
+		if cost > dist[u]:
+			continue
+		if u == end_idx:
+			break
+
+		ui, uj = u // n_lon, u % n_lon
+		for di, dj in ((1, 0), (-1, 0), (0, 1), (0, -1)):
+			vi, vj = ui + di, uj + dj
+			if 0 <= vi < n_lat and 0 <= vj < n_lon:
+				v = idx(vi, vj)
+				if math.isinf(risks[v]) or math.isinf(risks[u]):
+					continue
+				lat_u, lon_u = coords[u]
+				lat_v, lon_v = coords[v]
+				d_km = _haversine_km(lat_u, lon_u, lat_v, lon_v)
+				edge_cost = (risks[u] + risks[v]) / 2 + distance_weight * d_km
+				new_cost = cost + edge_cost
+				if new_cost < dist[v]:
+					dist[v] = new_cost
+					prev[v] = u
+					heapq.heappush(pq, (new_cost, v))
+
+	if math.isinf(dist[end_idx]):
+		return {
+			"reroute_needed": False,
+			"reason": "no_path_found",
+			"start_coord": (start_lat, start_lon),
+			"end_coord": (end_lat, end_lon),
+			"calls_made": calls
+		}
+
+	path_indices = []
+	u = end_idx
+	while u is not None:
+		path_indices.append(u)
+		u = prev[u]
+	path_indices.reverse()
+
+	path_coords = [coords[i] for i in path_indices]
+	return {
+		"reroute_needed": True,
+		"start_coord": (start_lat, start_lon),
+		"end_coord": (end_lat, end_lon),
+		"path": path_coords,
+		"total_cost": dist[end_idx],
+		"start_risk": risks[start_idx],
+		"end_risk": risks[end_idx],
+		"calls_made": calls,
+		"grid_shape": (n_lat, n_lon)
+	}
+
+
+def compute_index_and_reroute(lat: float,
+                              lon: float,
+                              api_key: Optional[str] = None,
+                              roadrisk_url: Optional[str] = None,
+                              max_risk: float = 10.0,
+                              num_bins: int = 10,
+                              reroute_kwargs: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
+	"""
+	Get road risk, map to index (1..num_bins), and attempt reroute.
+	reroute_kwargs forwarded to compute_reroute.
+	api_key/roadrisk_url accepted for compatibility but ignored by Open-Meteo implementation.
+	"""
+	if reroute_kwargs is None:
+		reroute_kwargs = {}
+
+	data, features = fetch_road_risk(lat, lon, extra_params=reroute_kwargs, api_key=api_key, roadrisk_url=roadrisk_url)
+	road_risk = float(features.get("road_risk_score", 0.0))
+
+	accidents = features.get("accidents") or features.get("accident_count")
+	try:
+		if accidents is not None:
+			# fallback: map accident count to index if present
+			from .models import accidents_to_bucket  # may not exist; wrapped in try
+			idx = accidents_to_bucket(int(accidents), max_count=20000, num_bins=num_bins)
+		else:
+			idx = risk_to_index(road_risk, max_risk=max_risk, num_bins=num_bins)
+	except Exception:
+		idx = risk_to_index(road_risk, max_risk=max_risk, num_bins=num_bins)
+
+	def _rp(lat_, lon_):
+		return get_risk_score(lat_, lon_, api_key=api_key, roadrisk_url=roadrisk_url)
+
+	reroute_info = compute_reroute(lat, lon, risk_provider=_rp, **reroute_kwargs)
+	return {
+		"lat": lat,
+		"lon": lon,
+		"index": int(idx),
+		"road_risk_score": road_risk,
+		"features": features,
+		"reroute": reroute_info,
+		"raw_roadrisk_response": data,
+	}

roadcast/openmeteo_inference.py

@@ -0,0 +1,339 @@
+"""
+Fetch OpenWeather data for a coordinate/time and run the trained MLP to predict the k-means cluster label.
+
+Usage examples:
+  # with training CSV provided to compute preprocessing stats:
+  python openweather_inference.py --lat 38.9 --lon -77.0 --datetime "2025-09-27T12:00:00" --train-csv data.csv --model model.pth --centers kmeans_centers_all.npz --api-key $OPENWEATHER_KEY
+
+  # with precomputed preprocess meta (saved from training):
+  python openweather_inference.py --lat 38.9 --lon -77.0 --datetime "2025-09-27T12:00:00" --preprocess-meta preprocess_meta.npz --model model.pth --centers kmeans_centers_all.npz --api-key $OPENWEATHER_KEY
+
+Notes:
+- The script uses the same feature-engineering helpers in `data.py` so the model sees identical inputs.
+- You must either provide `--train-csv` (to compute feature columns & means/stds) or `--preprocess-meta` previously saved.
+- Provide the OpenWeather API key via --api-key or the OPENWEATHER_KEY environment variable.
+"""
+
+import os
+import argparse
+import json
+from datetime import datetime
+import numpy as np
+import pandas as pd
+import torch
+import torch.nn.functional as F
+
+# reuse helpers from your repo
+from data import _add_date_features, _add_latlon_bins, _add_hashed_street, CSVDataset
+from inference import load_model
+
+# module-level caches to avoid reloading heavy artifacts per request
+_CACHED_MODEL = None
+_CACHED_IDX_TO_CLASS = None
+_CACHED_CENTERS = None
+_CACHED_PREPROCESS_META = None
+
+
+OW_BASE = 'https://api.openweathermap.org/data/2.5/onecall'
+
+
+def fetch_openmeteo(lat, lon, api_key, dt_iso=None):
+    """Fetch weather from OpenWeather One Call API for given lat/lon. If dt_iso provided, we fetch current+hourly and pick closest timestamp."""
+    try:
+        import requests
+    except Exception:
+        raise RuntimeError('requests library is required to fetch OpenWeather data')
+    params = {
+        'lat': float(lat),
+        'lon': float(lon),
+        'appid': api_key,
+        'units': 'metric',
+        'exclude': 'minutely,alerts'
+    }
+    r = requests.get(OW_BASE, params=params, timeout=10)
+    r.raise_for_status()
+    payload = r.json()
+    # if dt_iso provided, find nearest hourly data point
+    if dt_iso:
+        try:
+            target = pd.to_datetime(dt_iso)
+        except Exception:
+            target = None
+        best = None
+        if 'hourly' in payload and target is not None:
+            hours = payload['hourly']
+            best = min(hours, key=lambda h: abs(pd.to_datetime(h['dt'], unit='s') - target))
+            # convert keys to a flat dict with prefix 'ow_'
+            d = {
+                'ow_temp': best.get('temp'),
+                'ow_feels_like': best.get('feels_like'),
+                'ow_pressure': best.get('pressure'),
+                'ow_humidity': best.get('humidity'),
+                'ow_wind_speed': best.get('wind_speed'),
+                'ow_clouds': best.get('clouds'),
+                'ow_pop': best.get('pop'),
+            }
+            return d
+    # fallback: use current
+    cur = payload.get('current', {})
+    d = {
+        'ow_temp': cur.get('temp'),
+        'ow_feels_like': cur.get('feels_like'),
+        'ow_pressure': cur.get('pressure'),
+        'ow_humidity': cur.get('humidity'),
+        'ow_wind_speed': cur.get('wind_speed'),
+        'ow_clouds': cur.get('clouds'),
+        'ow_pop': None,
+    }
+    return d
+
+
+def fetch_roadrisk(roadrisk_url, api_key=None):
+    """Fetch the RoadRisk endpoint (expects JSON). If `api_key` is provided, we'll attach it as a query param if the URL has no key.
+
+    We flatten top-level numeric fields into `rr_*` keys for the feature row.
+    """
+    # if api_key provided and url does not contain appid, append it
+    try:
+        import requests
+    except Exception:
+        raise RuntimeError('requests library is required to fetch RoadRisk data')
+    url = roadrisk_url
+    if api_key and 'appid=' not in roadrisk_url:
+        sep = '&' if '?' in roadrisk_url else '?'
+        url = f"{roadrisk_url}{sep}appid={api_key}"
+
+    r = requests.get(url, timeout=10)
+    r.raise_for_status()
+    payload = r.json()
+    # flatten numeric top-level fields
+    out = {}
+    if isinstance(payload, dict):
+        for k, v in payload.items():
+            if isinstance(v, (int, float)):
+                out[f'rr_{k}'] = v
+            # if nested objects contain simple numeric fields, pull them too (one level deep)
+            elif isinstance(v, dict):
+                for kk, vv in v.items():
+                    if isinstance(vv, (int, float)):
+                        out[f'rr_{k}_{kk}'] = vv
+    return out
+
+
+def build_row(lat, lon, dt_iso=None, street=None, extra_weather=None):
+    """Construct a single-row DataFrame with columns expected by the training pipeline.
+
+    It intentionally uses column names the original `data.py` looked for (REPORTDATE, LATITUDE, LONGITUDE, ADDRESS, etc.).
+    """
+    row = {}
+    # date column matching common names
+    row['REPORTDATE'] = dt_iso if dt_iso else datetime.utcnow().isoformat()
+    row['LATITUDE'] = lat
+    row['LONGITUDE'] = lon
+    row['ADDRESS'] = street if street else ''
+    # include some injury/fatality placeholders that the label generator expects
+    row['INJURIES'] = 0
+    row['FATALITIES'] = 0
+    # include weather features returned by OpenWeather (prefixed 'ow_')
+    if extra_weather:
+        for k, v in extra_weather.items():
+            row[k] = v
+    return pd.DataFrame([row])
+
+
+def prepare_features(df_row, train_csv=None, preprocess_meta=None, feature_engineer=True, lat_lon_bins=20):
+    """Given a one-row DataFrame, apply same feature engineering and standardization as training.
+
+    If preprocess_meta is provided (npz), use it. Otherwise train_csv must be provided to compute stats.
+    Returns a torch.FloatTensor of shape (1, input_dim) and the feature_columns list.
+    """
+    # apply feature engineering helpers
+    if feature_engineer:
+        try:
+            _add_date_features(df_row)
+        except Exception:
+            pass
+        try:
+            _add_latlon_bins(df_row, bins=lat_lon_bins)
+        except Exception:
+            pass
+        try:
+            _add_hashed_street(df_row)
+        except Exception:
+            pass
+
+    # if meta provided, load feature_columns, means, stds
+    if preprocess_meta and os.path.exists(preprocess_meta):
+        meta = np.load(preprocess_meta, allow_pickle=True)
+        feature_columns = meta['feature_columns'].tolist()
+        means = meta['means']
+        stds = meta['stds']
+    else:
+        if not train_csv:
+            raise ValueError('Either preprocess_meta or train_csv must be provided to derive feature stats')
+        # instantiate a CSVDataset on train_csv (feature_engineer True) to reuse its preprocessing
+        ds = CSVDataset(train_csv, feature_columns=None, label_column='label', generate_labels=True, n_buckets=10, label_method='kmeans', label_store=None, feature_engineer=feature_engineer, lat_lon_bins=lat_lon_bins, nrows=None)
+        feature_columns = ds.feature_columns
+        means = ds.feature_means
+        stds = ds.feature_stds
+        # save meta for reuse
+        np.savez_compressed('preprocess_meta.npz', feature_columns=np.array(feature_columns, dtype=object), means=means, stds=stds)
+        print('Saved preprocess_meta.npz')
+
+    # ensure all feature columns exist in df_row
+    for c in feature_columns:
+        if c not in df_row.columns:
+            df_row[c] = 0
+
+    # coerce and fill using means
+    features_df = df_row[feature_columns].apply(lambda c: pd.to_numeric(c, errors='coerce'))
+    features_df = features_df.fillna(pd.Series(means, index=feature_columns)).fillna(0.0)
+    # standardize
+    features_np = (features_df.values - means) / (stds + 1e-6)
+    import torch
+    return torch.tensor(features_np, dtype=torch.float32), feature_columns
+
+
+def predict_from_openmeteo(lat, lon, dt_iso=None, street=None, api_key=None, train_csv=None, preprocess_meta=None, model_path='model.pth', centers_path='kmeans_centers_all.npz', roadrisk_url=None):
+    api_key = api_key or os.environ.get('OPENWEATHER_KEY')
+    if api_key is None:
+        raise ValueError('OpenWeather API key required via --api-key or OPENWEATHER_KEY env var')
+
+    # gather weather/road-risk features
+    weather = {}
+    if roadrisk_url:
+        try:
+            rr = fetch_roadrisk(roadrisk_url, api_key=api_key)
+            weather.update(rr)
+        except Exception as e:
+            print('Warning: failed to fetch roadrisk URL:', e)
+    else:
+        try:
+            ow = fetch_openmeteo(lat, lon, api_key, dt_iso=dt_iso)
+            weather.update(ow)
+        except Exception as e:
+            print('Warning: failed to fetch openweather:', e)
+
+    df_row = build_row(lat, lon, dt_iso=dt_iso, street=street, extra_weather=weather)
+    x_tensor, feature_columns = prepare_features(df_row, train_csv=train_csv, preprocess_meta=preprocess_meta)
+
+    # load model (infer num_classes from centers file if possible)
+    global _CACHED_MODEL, _CACHED_IDX_TO_CLASS, _CACHED_CENTERS, _CACHED_PREPROCESS_META
+
+    # ensure we have preprocess_meta available (prefer supplied path, otherwise fallback to saved file)
+    if preprocess_meta is None:
+        candidate = os.path.join(os.getcwd(), 'preprocess_meta.npz')
+        if os.path.exists(candidate):
+            preprocess_meta = candidate
+
+    # load centers (cache across requests)
+    if _CACHED_CENTERS is None:
+        if centers_path and os.path.exists(centers_path):
+            try:
+                npz = np.load(centers_path)
+                _CACHED_CENTERS = npz['centers']
+            except Exception:
+                _CACHED_CENTERS = None
+        else:
+            _CACHED_CENTERS = None
+
+    num_classes = _CACHED_CENTERS.shape[0] if _CACHED_CENTERS is not None else 10
+
+    # load model once and cache it
+    if _CACHED_MODEL is None:
+        try:
+            _CACHED_MODEL, _CACHED_IDX_TO_CLASS = load_model(model_path, device=None, in_channels=3, num_classes=num_classes)
+            device = 'cuda' if torch.cuda.is_available() else 'cpu'
+            _CACHED_MODEL.to(device)
+        except Exception as e:
+            raise
+    model = _CACHED_MODEL
+    idx_to_class = _CACHED_IDX_TO_CLASS
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    x_tensor = x_tensor.to(device)
+    with torch.no_grad():
+        logits = model(x_tensor)
+        probs = F.softmax(logits, dim=1).cpu().numpy()[0]
+        pred_idx = int(probs.argmax())
+        confidence = float(probs.max())
+
+    # optionally provide cluster centroid info
+    centroid = _CACHED_CENTERS[pred_idx] if _CACHED_CENTERS is not None else None
+
+    return {
+        'pred_cluster': int(pred_idx),
+        'confidence': confidence,
+        'probabilities': probs.tolist(),
+        'centroid': centroid.tolist() if centroid is not None else None,
+        'feature_columns': feature_columns,
+        'used_preprocess_meta': preprocess_meta
+    }
+
+
+def init_inference(model_path='model.pth', centers_path='kmeans_centers_all.npz', preprocess_meta=None):
+    """Eagerly load model, centers, and preprocess_meta into module-level caches.
+
+    This is intended to be called at app startup to surface load errors early and avoid
+    per-request disk IO. The function is best-effort and will print warnings if artifacts
+    are missing.
+    """
+    global _CACHED_MODEL, _CACHED_IDX_TO_CLASS, _CACHED_CENTERS, _CACHED_PREPROCESS_META
+
+    # prefer existing saved preprocess_meta if not explicitly provided
+    if preprocess_meta is None:
+        candidate = os.path.join(os.getcwd(), 'preprocess_meta.npz')
+        if os.path.exists(candidate):
+            preprocess_meta = candidate
+
+    _CACHED_PREPROCESS_META = preprocess_meta
+
+    # load centers
+    if _CACHED_CENTERS is None:
+        if centers_path and os.path.exists(centers_path):
+            try:
+                npz = np.load(centers_path)
+                _CACHED_CENTERS = npz['centers']
+                print(f'Loaded centers from {centers_path}')
+            except Exception as e:
+                print('Warning: failed to load centers:', e)
+                _CACHED_CENTERS = None
+        else:
+            print('No centers file found at', centers_path)
+            _CACHED_CENTERS = None
+
+    num_classes = _CACHED_CENTERS.shape[0] if _CACHED_CENTERS is not None else 10
+
+    # load model
+    if _CACHED_MODEL is None:
+        try:
+            _CACHED_MODEL, _CACHED_IDX_TO_CLASS = load_model(model_path, device=None, in_channels=3, num_classes=num_classes)
+            device = 'cuda' if torch.cuda.is_available() else 'cpu'
+            _CACHED_MODEL.to(device)
+            print(f'Loaded model from {model_path}')
+        except Exception as e:
+            print('Warning: failed to load model:', e)
+            _CACHED_MODEL = None
+
+    return {
+        'model_loaded': _CACHED_MODEL is not None,
+        'centers_loaded': _CACHED_CENTERS is not None,
+        'preprocess_meta': _CACHED_PREPROCESS_META
+    }
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--lat', type=float, required=True)
+    parser.add_argument('--lon', type=float, required=True)
+    parser.add_argument('--datetime', default=None, help='ISO datetime string to query hourly weather (optional)')
+    parser.add_argument('--street', default='')
+    parser.add_argument('--api-key', default=None, help='OpenWeather API key or use OPENWEATHER_KEY env var')
+    parser.add_argument('--train-csv', default=None, help='Path to training CSV to compute preprocessing stats (optional if --preprocess-meta provided)')
+    parser.add_argument('--preprocess-meta', default=None, help='Path to precomputed preprocess_meta.npz (optional)')
+    parser.add_argument('--model', default='model.pth')
+    parser.add_argument('--centers', default='kmeans_centers_all.npz')
+    parser.add_argument('--roadrisk-url', default=None, help='Optional custom RoadRisk API URL (if provided, will be queried instead of OneCall)')
+    args = parser.parse_args()
+
+    out = predict_from_openmeteo(args.lat, args.lon, dt_iso=args.datetime, street=args.street, api_key=args.api_key, train_csv=args.train_csv, preprocess_meta=args.preprocess_meta, model_path=args.model, centers_path=args.centers, roadrisk_url=args.roadrisk_url)
+    print(json.dumps(out, indent=2))