CHANGE: Enable training models

src/app.py

@@ -0,0 +1,94 @@
+from flask import Flask, jsonify, request
+import joblib
+import os
+from datetime import date
+
+from db import fetch_data
+import threading
+from model_registry import register, load_meta
+from models_train import train_demand_model, train_nulo_model
+
+app = Flask(__name__)
+MODEL_DIR = "models"
+
+def load_model(model_type):
+    meta = load_meta()
+    file = meta["current"].get(model_type)
+    if not file:
+        return None
+    return joblib.load(os.path.join(MODEL_DIR, file))
+
+def background_train():
+    print("Fetching data...")
+    df = fetch_data()
+    print(f"Data fetched: {len(df)} rows")
+    
+    today = date.today().isoformat()
+    os.makedirs(MODEL_DIR, exist_ok=True)
+
+    print("Training demand model...")
+    demand_model = train_demand_model(df)
+    demand_file = f"demand_xgb_{today}.joblib"
+    joblib.dump(demand_model, f"{MODEL_DIR}/{demand_file}")
+    register("demand", demand_file, len(df))
+    print("Demand model trained and saved.")
+
+    print("Training nulo model...")
+    nulo_model = train_nulo_model(df)
+    nulo_file = f"nulo_xgb_{today}.joblib"
+    joblib.dump(nulo_model, f"{MODEL_DIR}/{nulo_file}")
+    register("nulo", nulo_file, len(df))
+    print("Nulo model trained and saved.")
+
+@app.route("/train", methods=["POST"])
+def train():
+    threading.Thread(target=background_train).start()
+    return jsonify({"status": "training started"})
+
+@app.route("/predict_demand", methods=["GET"])
+def predict_demand():
+    h3 = int(request.args["h3"])
+    week = int(request.args["week"])
+    dow = int(request.args["dow"])
+    hour = int(request.args["hour"])
+
+    model = load_model("demand")
+    X = [[h3, week, dow, hour]]
+    pred = model.predict(X)[0]
+
+    return jsonify({"expected_demand": float(pred)})
+
+@app.route("/predict_nulo", methods=["GET"])
+def predict_nulo():
+    h3 = int(request.args["h3"])
+    week = int(request.args["week"])
+    dow = int(request.args["dow"])
+    hour = int(request.args["hour"])
+
+    model = load_model("nulo")
+    X = [[h3, week, dow, hour]]
+    prob = model.predict_proba(X)[0][1]
+
+    return jsonify({"nulo_probability": float(prob)})
+
+@app.route("/predict", methods=["GET"])
+def predict_legacy():
+    h3 = int(request.args["h3"])
+    week = int(request.args["week"])
+    dow = int(request.args["dow"])
+    hour = int(request.args["hour"])
+
+    model = load_model("nulo")
+    X = [[h3, week, dow, hour]]
+    prob = model.predict_proba(X)[0][1]
+
+    return jsonify({
+        "predicted_nulo_prob": float(prob)
+    })
+
+@app.route("/models", methods=["GET"])
+def models():
+    return jsonify(load_meta())
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=5000)