can request data from the form, now need to adjust reac_app.py

backend/react_app.py

+from dataclasses import dataclass
 from flask import Flask, request
 # from flask_cors import CORS
 
+import subprocess
+import os
+import zipfile
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import torch.utils.data as data_utils
+import torchvision
+import torchvision.datasets as datasets
+
+from matplotlib import pyplot as plt
+from numpy import save, load
+from tqdm import trange
+torch.manual_seed(0)
+# import agents and its functions
+
+from ..library.MetaAugment import UCB1_JC_py as UCB1_JC
+from ..library.MetaAugment import Evo_learner as Evo
+
 app = Flask(__name__)
 
 
 @app.route('/home', methods=["GET", "POST"])
 def home():
     print('in flask home')
-    data = request.get('')
+    form_data = request.get_json()
+    batch_size = 1       # size of batch the inner NN is trained with
+    learning_rate = 1e-1  # fix learning rate
+    ds = form_data['select_dataset']      # pick dataset (MNIST, KMNIST, FashionMNIST, CIFAR10, CIFAR100)
+    toy_size = form_data['toy_size']      # total propeortion of training and test set we use
+    max_epochs = 10      # max number of epochs that is run if early stopping is not hit
+    early_stop_num = 10   # max number of worse validation scores before early stopping is triggered
+    num_policies = 5      # fix number of policies
+    num_sub_policies = 5  # fix number of sub-policies in a policy
+    iterations = 5      # total iterations, should be more than the number of policies
+    IsLeNet = form_data["network_selection"]   # using LeNet or EasyNet or SimpleNet ->> default 
+    
+    # if user upload datasets and networks, save them in the database
+    if ds == 'Other':
+        ds_folder = request.files['dataset_upload']
+        ds_name_zip = ds_folder.filename
+        ds_name = ds_name_zip.split('.')[0]
+        ds_folder.save('./MetaAugment/datasets/'+ ds_name_zip)
+        with zipfile.ZipFile('./MetaAugment/datasets/'+ ds_name_zip, 'r') as zip_ref:
+            zip_ref.extractall('./MetaAugment/datasets/upload_dataset/')
+        os.remove(f'./MetaAugment/datasets/{ds_name_zip}')
+
+    else: 
+        ds_name = None
+
+    for (dirpath, dirnames, filenames) in os.walk(f'./MetaAugment/datasets/upload_dataset/{ds_name}/'):
+        for dirname in dirnames:
+            if dirname[0:6] != 'class_':
+                return render_template("fail_dataset.html")
+            else:
+                pass
+
+
+    if IsLeNet == 'Other':
+        childnetwork = request.files['network_upload']
+        childnetwork.save('./MetaAugment/child_networks/'+childnetwork.filename)
+
+    
+    # generate random policies at start
+    auto_aug_leanrer = request.form.get("auto_aug_selection")
+
+    if auto_aug_leanrer == 'UCB':
+        policies = UCB1_JC.generate_policies(num_policies, num_sub_policies)
+        q_values, best_q_values = UCB1_JC.run_UCB1(policies, batch_size, learning_rate, ds, toy_size, max_epochs, early_stop_num, iterations, IsLeNet, ds_name)
+    elif auto_aug_leanrer == 'Evolutionary Learner':
+        learner = Evo.Evolutionary_learner(fun_num=num_funcs, p_bins=1, mag_bins=1, sub_num_pol=1, ds_name=ds_name, exclude_method=exclude_method)
+        learner.run_instance()
+    elif auto_aug_leanrer == 'Random Searcher':
+        pass 
+    elif auto_aug_leanrer == 'Genetic Learner':
+        pass
+
+    plt.figure()
+    plt.plot(q_values)
+
+
+    best_q_values = np.array(best_q_values)
+
+    # save('best_q_values_{}_{}percent_{}.npy'.format(IsLeNet, int(toy_size*100), ds), best_q_values)
+    # best_q_values = load('best_q_values_{}_{}percent_{}.npy'.format(IsLeNet, int(toy_size*100), ds), allow_pickle=True)
+
+    print("DONE")
+    
+
+    return None
 
-    return data
+@app.route('/api')
+def index():
+    return {'name': 'Hello'}
\ No newline at end of file

src/App.js

@@ -2,21 +2,28 @@ import Home from './pages/Home'
 import './App.css';
 import React, { useState, useEffect } from "react";
 import axios from "axios";
-import logo from './logo.svg';
 
 // import {BrowerRouter as Router, Route, Switch} from 'react-router-dom';
 
 function App() {
-  useEffect(() => {
-    console.log('print here')
-    fetch('/home', {
-      method: 'POST',
-      headers: { 'Content-Type': 'application/json' },
-      body: JSON.stringify({ })
-    })
-      .then((response) => console.log(response.text()))
-      .then((data) => console.log(data));
-  }, []);
+  // useEffect(() => {
+  //   console.log('print here')
+  //   fetch('/home', {
+  //     method: 'POST',
+  //     headers: { 'Content-Type': 'application/json' },
+  //     body: JSON.stringify({ })
+  //   })
+  //     .then((response) => response.text())
+  //     .then((data) => console.log('data', data));
+  // }, []);
+
+  // useEffect(() => {
+  //   fetch('/api').then(response =>{
+  //     if(response.ok){
+  //       return response.json()
+  //     }
+  //   }).then(data => console.log(data))
+  // }, [])
 
   return (
     <div>

src/pages/Home.js

@@ -29,9 +29,18 @@ export default function Home() {
     // for form submission  
     const {register, control, handleSubmit, setValue, watch, formState: { errors, dirtyFields}} = useForm();
     const watchFileds = watch(['select_dataset', 'select_network']);
-    const onSubmit = data => console.log('data', data); 
-    
-    console.log('errors', errors); 
+    const onSubmit = data => {
+        console.log('data', data);
+        fetch('/home', {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify(data)
+        })
+        .then((response) => response.text())
+        .then((method) => console.log('method', method));
+    }
+    // console.log('errors', errors); 
+    // console.log('handleSubmit', handleSubmit)
 
 
     // handling learner selection