Ppf30

.dockerignore

@@ -0,0 +1,8 @@
+node_modules
+npm-debug.log
+Dockerfile
+docker-compose.yml
+.vscode
+.git
+.gitignore
+.env

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",
+    });
   }
 }
 

dockerfile

@@ -0,0 +1,26 @@
+# Imagen base con Node 22
+FROM node:22-slim
+
+
+# Directorio de trabajo dentro del contenedor
+WORKDIR /usr/src/app
+
+
+# Copiamos primero manifiestos para cachear dependencias
+COPY package*.json ./
+
+
+# Instalamos dependencias de producción
+RUN npm ci --omit=dev
+
+
+# Copiamos el resto del código (incluye /model)
+COPY . .
+
+
+# El servicio escucha en 3002
+EXPOSE 3002
+
+
+# Comando de arranque
+CMD ["node", "server.js"]

model/Prediction.js

@@ -0,0 +1,23 @@
+//model/prediction.js
+'use strict'
+
+const mongoose = require('mongoose');
+const Schema = mongoose.Schema;
+
+const PredictionSchema = new Schema({
+    source: String,
+    timestamp: { type: Date, default: Date.now }, 
+    latencyMs: Number,                            
+    features: [Number],                           
+    prediction: Number,
+
+    featureCount: Number,
+    scalerVersion: String,
+    createdAt: { type: Date, default: Date.now },
+    targetDate: Date,
+    dailyValues: [Number], 
+
+
+});
+
+module.exports = mongoose.model("Prediction", PredictionSchema);

package.json

@@ -11,11 +11,14 @@
     },
     "author": "Iren Lorenzo Fonseca",
     "license": "ISC",
-    "bugs": {
-    },
+    "bugs": {},
     "dependencies": {
         "@tensorflow/tfjs": "^4.22.0",
         "@tensorflow/tfjs-backend-wasm": "^4.22.0",
-        "express": "^5.1.0"
+        "dotenv": "^17.2.3",
+        "express": "^5.1.0",
+        "mongodb": "^7.0.0",
+        "mongoose": "^9.0.1"
     }
-}
+}
+

server.js

@@ -1,32 +1,46 @@
 // server.js
 // Entry point del servicio PREDICT
+require("dotenv").config();
 
 const express = require("express");
+const app = express();
 const path = require("path");
+const mongoose = require("mongoose");
 const predictRoutes = require("./routes/predictRoutes");
 const { initModel } = require("./services/tfModelService");
 
 const PORT = process.env.PORT || 3002;
+const MONGO_URI = process.env.MONGO_URI;
+
+// conectar a Mongo
+mongoose
+  .connect(MONGO_URI)
+  .then(() => {console.log("MongoDB conectado (PREDICT)")})
+  .catch((err) => {
+    console.error("Error al conectar MongoDB:", err);
+    process.exit(1);
+  });
 
-const app = express();
 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));
 
-// Rutas del servicio PREDICT
+
 app.use("/", predictRoutes);
 
-// Arranque del servidor + carga del modelo
+
 app.listen(PORT, async () => {
   const serverUrl = `http://localhost:${PORT}`;
-  console.log(`[PREDICT] Servicio escuchando en ${serverUrl}`);
+  console.log(`PREDICT escuchando en ${serverUrl}`);
 
   try {
+    // Inicializa el modelo predictivo
     await initModel(serverUrl);
+    console.log(" Modelo predictivo cargado correctamente.");
   } catch (err) {
-    console.error("Error al inicializar modelo:", err);
+    console.error("Error al inicializar el modelo predictivo:", err);
     process.exit(1);
   }
 });

services/tfModelService.js

@@ -122,4 +122,4 @@ module.exports = {
   initModel,
   getModelInfo,
   predict
-};
+};