136 lines
4.7 KiB
Python
136 lines
4.7 KiB
Python
from flask import Flask, request, jsonify
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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('/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('/predict', methods=['POST'])
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def predict_endpoint():
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"""Predict single uploaded image. Expects form-data with file field named 'image'."""
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if 'image' not in request.files:
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return jsonify({"error": "no image uploaded (field 'image')"}), 400
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img = request.files['image']
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tmp_path = os.path.join(os.getcwd(), 'tmp_upload.jpg')
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img.save(tmp_path)
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try:
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from inference import load_model, predict_image
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model_path = os.path.join(os.getcwd(), 'model.pth')
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if not os.path.exists(model_path):
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return jsonify({"error": "no trained model found (run /train first)"}), 400
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model, idx_to_class = load_model(model_path)
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idx, conf = predict_image(model, tmp_path)
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label = idx_to_class.get(idx) if idx_to_class else str(idx)
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return jsonify({"label": label, "confidence": conf})
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finally:
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try:
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os.remove(tmp_path)
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except Exception:
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pass
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@app.route('/predict-roadrisk', methods=['POST'])
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def predict_roadrisk():
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"""Proxy endpoint to predict a roadrisk cluster from lat/lon/datetime.
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Expects JSON body with: {"lat": 38.9, "lon": -77.0, "datetime": "2025-09-27T12:00:00", "roadrisk_url": "https://..."}
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If roadrisk_url is not provided the endpoint will call OpenWeather OneCall (requires API key via OPENWEATHER_KEY env var).
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"""
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payload = request.json or {}
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lat = payload.get('lat')
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lon = payload.get('lon')
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dt = payload.get('datetime')
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street = payload.get('street', '')
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roadrisk_url = payload.get('roadrisk_url')
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# prefer explicit api_key in request, otherwise read from OPENWEATHER_API_KEY env var
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api_key = payload.get('api_key') or os.environ.get('OPENWEATHER_API_KEY')
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if lat is None or lon is None:
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return jsonify({"error": "lat and lon are required fields"}), 400
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try:
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from openweather_inference import predict_from_openweather
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# pass api_key (may be None) to the inference helper; helper will raise if a key is required
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res = predict_from_openweather(
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lat, lon,
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dt_iso=dt,
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street=street,
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api_key=api_key,
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train_csv=os.path.join(os.getcwd(), 'data.csv'),
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preprocess_meta=None,
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model_path=os.path.join(os.getcwd(), 'model.pth'),
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centers_path=os.path.join(os.getcwd(), 'kmeans_centers_all.npz'),
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roadrisk_url=roadrisk_url
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)
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return jsonify(res)
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except Exception as e:
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return jsonify({"error": str(e)}), 500
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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 openweather_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 openweather_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('/post-data', methods=['POST'])
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# def post_data():
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# content = request.json
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# user_input = content.get('input')
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# # Example: Simple echo AI (replace with real AI model code)
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# ai_response = f"AI received: {user_input}"
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# return jsonify({"response": ai_response})
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