fixed flask run
This commit is contained in:
samarthjain2023
122
roadcast/app.py
122
roadcast/app.py
@@ -1,8 +1,78 @@
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from flask import Flask, request, jsonify
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from dotenv import load_dotenv
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from openmeteo_client import compute_index
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# Load environment variables from .env file
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load_dotenv()
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app = Flask(__name__)
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import os
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import threading
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import json
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# ML imports are lazy to avoid heavy imports on simple runs
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@app.route('/')
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def home():
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return "<h1>Welcome to the Flask App</h1><p>Try /get-data or /health endpoints.</p>"
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@app.route('/get-data', methods=['GET'])
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def get_data():
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# Example GET request handler
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data = {"message": "Hello from Flask!"}
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return jsonify(data)
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@app.route('/post-data', methods=['POST'])
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def post_data():
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# Example POST request handler
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content = request.json
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# Process content or call AI model here
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response = {"you_sent": content}
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return jsonify(response)
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@app.route('/train', methods=['POST'])
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def train_endpoint():
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"""Trigger training. Expects JSON: {"data_root": "path/to/data", "epochs": 3}
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Training runs in a background thread and saves model to model.pth in repo root.
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"""
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payload = request.json or {}
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data_root = payload.get('data_root')
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epochs = int(payload.get('epochs', 3))
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if not data_root or not os.path.isdir(data_root):
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return jsonify({"error": "data_root must be a valid directory path"}), 400
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def _run_training():
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from train import train
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train(data_root, epochs=epochs)
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t = threading.Thread(target=_run_training, daemon=True)
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t.start()
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return jsonify({"status": "training_started"})
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@app.route('/health', methods=['GET'])
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def health():
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"""Return status of loaded ML artifacts (model, centers, preprocess_meta)."""
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try:
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from openmeteo_inference import init_inference
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status = init_inference()
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return jsonify({'ok': True, 'artifacts': status})
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except Exception as e:
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return jsonify({'ok': False, 'error': str(e)}), 500
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if __name__ == '__main__':
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# eager load model/artifacts at startup (best-effort)
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try:
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from openmeteo_inference import init_inference
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init_inference()
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except Exception:
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pass
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app.run(debug=True)
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@app.route('/predict', methods=['POST', 'GET'])
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def predict_endpoint():
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"""Predict route between two points given source and destination with lat and lon.
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Expectation:
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- POST with JSON: {"source": {"lat": .., "lon": ..}, "destination": {"lat": .., "lon": ..}}
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- GET returns usage instructions for quick browser testing.
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@@ -18,18 +88,46 @@ def predict_endpoint():
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"http://127.0.0.1:5000/predict"
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)
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if request.method == 'GET':
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return jsonify({"info": info, "example": example_payload, "note": note}), 200
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# Return the same structure as POST but without prediction
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# response_payload = {
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# "index": None,
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# "prediction": {},
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# "called_with": "GET",
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# "diagnostics": {},
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# "example": example_payload,
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# "info": info,
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# "note": note
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# }
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# For GET request, compute the road risk index using the example coordinates
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src_lat = example_payload['source']['lat']
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src_lon = example_payload['source']['lon']
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dst_lat = example_payload['destination']['lat']
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dst_lon = example_payload['destination']['lon']
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# Use the compute_index function to get the road risk index
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index = compute_index(src_lat, src_lon)
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# Prepare the response payload
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response_payload = {
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"index": index, # The computed index here
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"prediction": {},
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"called_with": "GET",
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"diagnostics": {},
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"example": example_payload,
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"info": info,
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"note": note
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}
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return jsonify(response_payload), 200
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# POST request logic
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data = request.json or {}
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source = data.get('source')
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destination = data.get('destination')
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if not source or not destination:
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return jsonify({"error": "both 'source' and 'destination' fields are required"}), 400
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try:
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src_lat = float(source.get('lat'))
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src_lon = float(source.get('lon'))
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@@ -38,7 +136,6 @@ def predict_endpoint():
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except (TypeError, ValueError):
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return jsonify({"error": "invalid lat or lon values; must be numbers"}), 400
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# Ensure compute_reroute exists and is callable
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try:
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from openmeteo_client import compute_reroute
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@@ -50,11 +147,9 @@ def predict_endpoint():
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"(Open-Meteo does not need an API key)"
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}), 500
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if not callable(compute_reroute):
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return jsonify({"error": "openmeteo_client.compute_reroute is not callable"}), 500
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def _extract_index(res):
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if res is None:
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return None
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@@ -69,20 +164,17 @@ def predict_endpoint():
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return res[k]
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return None
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# Call compute_reroute (Open-Meteo requires no API key)
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try:
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result = compute_reroute(src_lat, src_lon, dst_lat, dst_lon)
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called_with = "positional"
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diagnostics = {"type": type(result).__name__}
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try:
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diagnostics["repr"] = repr(result)[:1000]
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except Exception:
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diagnostics["repr"] = "<unrepr-able>"
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# Normalize return types
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if isinstance(result, (list, tuple)):
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idx = None
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@@ -102,7 +194,6 @@ def predict_endpoint():
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index = None
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prediction = {"value": result}
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response_payload = {
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"index": index,
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"prediction": prediction,
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@@ -113,7 +204,6 @@ def predict_endpoint():
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"note": note
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}
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# Add warning if no routing/index info found
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expected_keys = ('route', 'path', 'distance', 'directions', 'index', 'idx', 'cluster')
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if (not isinstance(prediction, dict) or not any(k in prediction for k in expected_keys)) and index is None:
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@@ -122,9 +212,13 @@ def predict_endpoint():
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"See diagnostics for details."
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)
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return jsonify(response_payload), 200
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return jsonify(response_payload)
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except Exception as e:
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return jsonify({
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"error": "Error processing the request",
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"detail": str(e)
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}), 500
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except Exception as e:
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return jsonify({"error": "compute_reroute invocation failed", "detail": str(e)}), 500
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@@ -294,46 +294,41 @@ def compute_reroute(
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"grid_shape": (n_lat, n_lon)
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}
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def compute_index_and_reroute(lat: float,
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def compute_index(lat: float,
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lon: float,
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api_key: Optional[str] = None,
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roadrisk_url: Optional[str] = None,
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max_risk: float = 10.0,
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num_bins: int = 10,
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reroute_kwargs: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
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risk_provider: Optional[Callable[[float, float], float]] = None) -> int:
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"""
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Get road risk, map to index (1..num_bins), and attempt reroute.
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reroute_kwargs forwarded to compute_reroute.
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api_key/roadrisk_url accepted for compatibility but ignored by Open-Meteo implementation.
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Computes and returns an index based on road risk and accident information.
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Args:
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lat: Latitude of the location.
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lon: Longitude of the location.
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max_risk: Maximum possible road risk score.
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num_bins: Number of bins to divide the risk range into.
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reroute_kwargs: Optional dictionary passed to reroute logic.
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risk_provider: Optional custom risk provider function.
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Returns:
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An integer index (1 to num_bins) based on computed road risk.
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"""
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if reroute_kwargs is None:
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reroute_kwargs = {}
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# If a risk provider is not provided, use a default one
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if risk_provider is None:
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risk_provider = lambda lat, lon: get_risk_score(lat, lon)
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data, features = fetch_road_risk(lat, lon, extra_params=reroute_kwargs, api_key=api_key, roadrisk_url=roadrisk_url)
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road_risk = float(features.get("road_risk_score", 0.0))
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# Fetch road risk score using the provided risk provider
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road_risk = float(risk_provider(lat, lon))
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accidents = features.get("accidents") or features.get("accident_count")
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try:
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if accidents is not None:
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# fallback: map accident count to index if present
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from .models import accidents_to_bucket # may not exist; wrapped in try
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idx = accidents_to_bucket(int(accidents), max_count=20000, num_bins=num_bins)
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else:
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idx = risk_to_index(road_risk, max_risk=max_risk, num_bins=num_bins)
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except Exception:
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idx = risk_to_index(road_risk, max_risk=max_risk, num_bins=num_bins)
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# Compute the index based on road risk score and the max_risk, num_bins parameters
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# The formula will divide the risk score into `num_bins` bins
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# The index will be a number between 1 and num_bins based on the risk score
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def _rp(lat_, lon_):
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return get_risk_score(lat_, lon_, api_key=api_key, roadrisk_url=roadrisk_url)
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# Normalize the risk score to be between 0 and max_risk
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normalized_risk = min(road_risk, max_risk) / max_risk
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reroute_info = compute_reroute(lat, lon, risk_provider=_rp, **reroute_kwargs)
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return {
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"lat": lat,
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"lon": lon,
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"index": int(idx),
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"road_risk_score": road_risk,
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"features": features,
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"reroute": reroute_info,
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"raw_roadrisk_response": data,
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}
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# Compute the index based on the normalized risk score
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index = int(normalized_risk * num_bins)
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# Ensure the index is within the expected range of 1 to num_bins
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return max(1, min(index + 1, num_bins)) # Adding 1 because index is 0-based
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