added the model

roadcast/app.py

@@ -0,0 +1,123 @@
+from flask import Flask, request, jsonify
+
+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'])
+def predict_endpoint():
+    """Predict single uploaded image. Expects form-data with file field named 'image'."""
+    if 'image' not in request.files:
+        return jsonify({"error": "no image uploaded (field 'image')"}), 400
+    img = request.files['image']
+    tmp_path = os.path.join(os.getcwd(), 'tmp_upload.jpg')
+    img.save(tmp_path)
+    try:
+        from inference import load_model, predict_image
+        model_path = os.path.join(os.getcwd(), 'model.pth')
+        if not os.path.exists(model_path):
+            return jsonify({"error": "no trained model found (run /train first)"}), 400
+        model, idx_to_class = load_model(model_path)
+        idx, conf = predict_image(model, tmp_path)
+        label = idx_to_class.get(idx) if idx_to_class else str(idx)
+        return jsonify({"label": label, "confidence": conf})
+    finally:
+        try:
+            os.remove(tmp_path)
+        except Exception:
+            pass
+
+
+@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')
+    api_key = payload.get('api_key') or os.environ.get('OPENWEATHER_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
+        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})