ppf30

.dockerignore

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

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 Predict
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -0,0 +1,97 @@
+# PREDICT
+Predict contiene un modelo preentrenado con ```TensorFlow```. Una vez confirma que el modelo está correctamente cargado y que los datos tienen el tamaño correcto (el que debe devolver acquire), se ejecuta obteniendo una predicción del consumo de energía. Este resultado se almacena en ```MongoDB```.
+
+## Repositorios del proyecto
+```bash 
+https://github.com/ppf30/acquire.git
+```
+```bash
+https://github.com/ppf30/orchestrator.git
+```
+```bash
+https://github.com/ppf30/predict.git
+```
+
+
+## Uso Local
+
+```bash
+# Iniciar el orquestador
+node server.js
+```
+
+## Uso docker
+Todo el proyecto está dockerizado, por lo tanto si queremos probarlo con contenedores debemos clonar los repositorios y con el ```docker-compose.yml``` en la carpeta, ejecutamos los siguientes comandos en la terminal:
+```bash
+docker-compose up -d --build
+```
+
+Al finalizar podemos eliminar los contenedores:
+```bash
+docker-compose down
+```
+## Pruebas en Postman
+GET http://localhost:3001/health
+
+GET http://localhost:3001/ready
+
+POST http://localhost:3001/predict
+```bash
+# Body
+{
+  "features": [1.315, 1.81, 1.27, 8, 0, 9, 30],
+  "meta": {
+    "source": "orchestrator",
+    "dataId": "6772c1f3e2a0b12345678901",
+    "featureCount": 7,
+    "scalerVersion": "v1",
+    "targetDate": "2025-11-26T22:00:00.000Z",
+    "dailyValues": [28.186, 27.809, 27.44],
+    "kunnaMeta": {
+      "alias": "6339651",
+      "name": "1d",
+      "daysUsed": ["2025-11-25", "2025-11-24", "2025-11-23"]
+    },
+    "fetchMeta": {
+      "timeStart": "2025-11-22T18:43:10.000Z",
+      "timeEnd": "2025-11-25T18:43:10.000Z"
+    }
+  }
+}
+```
+
+
+
+
+## Lenguaje 
+
+* Todo el código está en Java Scrip
+
+## Estructura del Proyecto
+
+```
+predict/
+│── controllers/
+│──│── predictControllers.js
+│── model/
+│──│── group1-shard1of1.bin
+│──│── model.json
+│──│── Prediction.js
+│── node_modules/
+│── routes/
+│──│── predictRoutes.js
+│── services/
+│──│── tfModelService.js
+│── dockerfile
+│── package-lock.json
+│── package.json
+│── server_all.js
+│── server.js
+│── README.md
+
+```
+
+
+## Licencia
+
+Este proyecto está bajo la licencia MIT.

dockerfile

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+# 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 +1,47 @@
 // 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 app = express();
+
 app.use(express.json());
 
+
+// conectar a Mongo
+mongoose
+  .connect(process.env.MONGO_URI)
+  .then(() => console.log("MongoDB conectado (PREDICT)"))
+  .catch((err) => {
+    console.error("Error al conectar MongoDB:", err);
+    process.exit(1);
+  });
+
 // 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

@@ -34,10 +34,7 @@ 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);
@@ -66,7 +63,6 @@ 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") {
@@ -83,10 +79,7 @@ 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");