added link for predictAPI + integrated model

api/app.js

@@ -25,8 +25,8 @@ const predictRouter = require('./routes/predict/predict')
 app.use(webRouter);     
 //path[POST]:  <host>/esp/
 app.use(sensorRouter);  
-//path[POST]:  <host>/predict/
-app.use(predictRouter);
+//path[POST]:  http://127.0.0.1:8023/temp/
+app.use(predictRouter)
 
 
 

api/db/schema/HourStats.js

@@ -0,0 +1,5 @@
+const mongoose  = require('mongoose')
+
+
+
+

api/package.json

@@ -10,6 +10,7 @@
   "author": "",
   "license": "ISC",
   "dependencies": {
+    "axios": "^1.6.7",
     "body-parser": "^1.20.2",
     "cors": "^2.8.5",
     "dotenv": "^16.3.2",

api/predictAPI/main.py

@@ -1,23 +1,44 @@
 from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
+import pandas as pd
 import joblib
+import warnings
+
+warnings.filterwarnings("ignore")
+model = joblib.load('./model/new_model.pkl');
+
+app = FastAPI();
+origins = ["*"]
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["GET", "POST", "PUT", "DELETE"],
+    allow_headers=["*"],
+)
+
 
 
 class ReqBody(BaseModel):
+    item: str
     temp: float
 
-app = FastAPI();
+
+
+
+def predictor(foodItem: str, temp: float):
+    testFd = pd.DataFrame([[foodItem, temp]], columns=["Food Item", "Temp.(in degree C)"])
+    preds = model.predict(testFd)
+    return preds[0];
 
 
 @app.post('/temp/')
 async def postReq(request: ReqBody):
-    model = joblib.load('./model/fsp_model.pkl');
-    prediction = model.predict([[request.temp]])[0];
-    print(prediction)
-
+    preds = predictor(request.item, request.temp)
     return {
         "success": True,
-        "output": ""
+        "output": preds
     }
 
 

api/predictAPI/model/README.md

@@ -0,0 +1,2 @@
+Input:   [[Food Item: String, Temperature: Float]]
+Output:  [Prediction(Days): Int]

api/predictAPI/test.py

@@ -1,9 +1,11 @@
 import joblib
-import warnings
-warnings.filterwarnings("ignore")
+import pandas as pd
 
 
 
-model = joblib.load('./model/fsp_model.pkl');
-prediction = model.predict([[2, 23,5]]);
-print(prediction)
+
+
+pred = model.predict(testFd)[0]
+print(pred)
+
+

api/routes/predict/predict.js

@@ -1,39 +1,16 @@
-const express = require('express')
-const predictionRouter = express.Router();
-const { InferenceSession, Tensor } = require('onnxruntime-node');
+const express  = require('express')
+const predictRouter = express.Router();
+const axios = require('axios')
 
+const predictPATH = 'http://127.0.0.1:8023/temp/'
 
 
-async function predictSpoilage(temp, responder){
-    // Create a new InferenceSession
-    const session = new InferenceSession();
-
-    // Load the ONNX model
-    await session.loadModel('./../../model/fsp_model.onnx');
-
-    // Prepare input data (replace inputData with your actual input data)
-    const inputData = new Float32Array([32.65]);
-
-    // Create an input Tensor
-    const inputTensor = new Tensor('float32', inputData, [1, inputData.length]);
-
-    // Run the model
-    const outputMap = await session.run([inputTensor]);
-
-    // Get the output tensor
-    const outputTensor = outputMap.values().next().value;
-
-    // Access the predictions
-    const predictions = outputTensor.data;
-    console.log(predictions);
-}
-
-
-predictionRouter.post('/predict/', (req,res)=>{
-    const temp = req.body.temp;
-    predictSpoilage(temp, res)
+predictRouter.post('/temp/', (req,res)=>{
+    axios.post(predictPATH, req.body).then(result=>{
+        res.json(result.data)
+    })
 })
 
 
 
-module.exports = predictionRouter;
+module.exports = predictRouter

api/server.bat

@@ -0,0 +1,3 @@
+@echo off
+start cmd /k "cd predictAPI && uvicorn main:app --reload --port 8023"
+npm run server

model/v2/README.md

@@ -0,0 +1,2 @@
+Input:   [[Food Item: String, Temperature: Float]]
+Output:  [Prediction(Days): Int]

web/src/connect/requests.js

@@ -101,14 +101,7 @@ export default class Requests{
     }
 
 
-    static async fetchDevice_currWeek(callback){
-
-    }
-
-
-    static async fetchDevice_currMonth(callback){
-
-    }
+
 
 
 }