predict funciona pero no almacena las predicciones en MongoDB

controllers/predictController.js

@@ -1,5 +1,7 @@
+
 // controllers/predictController.js
 const { getModelInfo, predict } = require("../services/tfModelService");
+const Prediction = require("../model/Prediction");
 
 function health(req, res) {
   res.json({
@@ -60,20 +62,36 @@ async function doPredict(req, res) {
       });
     }
 
-    const prediction = await predict(features);
+    // Ejecutar el modelo
+    const predictionValue = await predict(features);
     const latencyMs = Date.now() - start;
-    const timestamp = new Date().toISOString();
+    const timestamp = new Date();
 
-    // De momento sin MongoDB → predictionId null
-    res.status(201).json({
-      predictionId: null,
-      prediction,
+    // Guardar en Mongo solo campos válidos
+    const responsePred = await Prediction.create({
+      features,
+      prediction: predictionValue,
       timestamp,
+      latencyMs,
+      featureCount: meta.featureCount,
+      scalerVersion: meta.scalerVersion || "v1",
+      createdAt: timestamp,
+      predictGroup: "predict"
+    });
+
+    res.status(201).json({
+      predictionId: responsePred._id,
+      prediction: predictionValue,
+      timestamp: timestamp.toISOString(),
       latencyMs
     });
+
   } catch (err) {
     console.error("Error en /predict:", err);
-    res.status(500).json({ error: "Internal error" });
+
+    res.status(500).json({
+      error: "Internal error",
+    });
   }
 }
 

model/Prediction.js

@@ -20,4 +20,4 @@ const PredictionSchema = new Schema({
 
 });
 
-module.exports = mongoose.model('Prediction', PredictionSchema);
+module.exports = mongoose.model("Prediction", PredictionSchema);

server.js

@@ -10,20 +10,19 @@ const predictRoutes = require("./routes/predictRoutes");
 const { initModel } = require("./services/tfModelService");
 
 const PORT = process.env.PORT || 3002;
-
-
-app.use(express.json());
-
+const MONGO_URI = process.env.MONGO_URI;
 
 // conectar a Mongo
 mongoose
-  .connect(process.env.MONGO_URI)
-  .then(() => console.log("MongoDB conectado (PREDICT)"))
+  .connect(MONGO_URI)
+  .then(() => {console.log("MongoDB conectado (PREDICT)")})
   .catch((err) => {
     console.error("Error al conectar MongoDB:", err);
     process.exit(1);
   });
 
+app.use(express.json());
+
 // Servir la carpeta del modelo TFJS (model/model.json + pesos)
 const modelDir = path.resolve(__dirname, "model");
 app.use("/model", express.static(modelDir));

services/tfModelService.js

@@ -34,7 +34,10 @@ function wasmFileDirUrl() {
   return pathToFileURL(distFsPath + path.sep).href;
 }
 
-
+/**
+ * Inicializa backend WASM y carga el GraphModel
+ * serverUrl: ej. http://localhost:3002
+ */
 async function initModel(serverUrl) {
   const wasmPath = wasmFileDirUrl();
   wasmBackend.setWasmPaths(wasmPath);
@@ -63,6 +66,7 @@ async function initModel(serverUrl) {
     throw new Error("No se ha podido detectar inputName/outputName/inputDim");
   }
 
+  // Warm-up
   const Xwarm = tf.zeros([1, inputDim], "float32");
   let out;
   if (typeof model.executeAsync === "function") {
@@ -79,7 +83,10 @@ async function initModel(serverUrl) {
   console.log("[TF] Modelo listo.");
 }
 
-
+/**
+ * Ejecuta el modelo con un vector de features
+ * Devuelve un escalar >= 0
+ */
 async function predict(features) {
   if (!ready || !model) {
     throw new Error("Model not ready");
@@ -115,4 +122,4 @@ module.exports = {
   initModel,
   getModelInfo,
   predict
-};
+};