Update react_app.py

backend/react_app.py

 from dataclasses import dataclass
-from flask import Flask, request
+from flask import Flask, request, current_app
 # from flask_cors import CORS
-
 import subprocess
 import os
 import zipfile
@@ -19,83 +18,102 @@ from matplotlib import pyplot as plt
 from numpy import save, load
 from tqdm import trange
 torch.manual_seed(0)
-# import agents and its functions
 
+# import agents and its functions
 from ..library.MetaAugment import UCB1_JC_py as UCB1_JC
 from ..library.MetaAugment import Evo_learner as Evo
+print('@@@ import successful')
 
 app = Flask(__name__)
 
 
+# it is used to collect user input and store them in the app
 @app.route('/home', methods=["GET", "POST"])
 def home():
-    print('in flask home')
-    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
+    print('@@@ in Flask Home')
+    form_data = request.get_json() 
+    # form_data = request.files
+    # form_data = request.form.get('test')
+    print('@@@ this is form data', form_data)
+
+    # required input
+    ds = form_data['select_dataset'] # pick dataset (MNIST, KMNIST, FashionMNIST, CIFAR10, CIFAR100)
+    IsLeNet = form_data["select_network"]   # using LeNet or EasyNet or SimpleNet ->> default 
+    auto_aug_leanrer = form_data["select_learner"] # augmentation methods to be excluded
+
+    print('@@@ required user input:', 'ds', ds, 'IsLeNet:', IsLeNet, 'auto_aug_leanrer:',auto_aug_leanrer)
+    # advanced input
+    if 'batch_size' in form_data.keys(): 
+        batch_size = form_data['batch_size']       # size of batch the inner NN is trained with
+    else: 
+        batch_size = 1 # this is for demonstration purposes
+    if 'learning_rate' in form_data.keys(): 
+        learning_rate =  form_data['learning_rate']  # fix learning rate
+    else: 
+        learning_rate = 10-1
+    if 'toy_size' in form_data.keys(): 
+        toy_size = form_data['toy_size']      # total propeortion of training and test set we use
+    else: 
+        toy_size = 1 # this is for demonstration purposes
+    if 'iterations' in form_data.keys(): 
+        iterations = form_data['iterations']      # total iterations, should be more than the number of policies
+    else: 
+        iterations = 10
+    print('@@@ advanced search: batch_size:', batch_size, 'learning_rate:', learning_rate, 'toy_size:', toy_size, 'iterations:', iterations)
+
+    # default values 
     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_folder = request.files #['ds_upload'] 
+        print('!!!ds_folder', ds_folder)
         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}')
-
+        ds_folder.save('./datasets/'+ ds_name_zip)
+        with zipfile.ZipFile('./datasets/'+ ds_name_zip, 'r') as zip_ref:
+            zip_ref.extractall('./datasets/upload_dataset/')
+        if not current_app.debug:
+            os.remove(f'./datasets/{ds_name_zip}')
     else: 
         ds_name = None
 
-    for (dirpath, dirnames, filenames) in os.walk(f'./MetaAugment/datasets/upload_dataset/{ds_name}/'):
+    # test if uploaded dataset meets the criteria 
+    for (dirpath, dirnames, filenames) in os.walk(f'./datasets/upload_dataset/{ds_name}/'):
         for dirname in dirnames:
             if dirname[0:6] != 'class_':
-                return render_template("fail_dataset.html")
-            else:
-                pass
-
+                return None # neet to change render to a 'failed dataset webpage'
 
+    # save the user uploaded network
     if IsLeNet == 'Other':
         childnetwork = request.files['network_upload']
-        childnetwork.save('./MetaAugment/child_networks/'+childnetwork.filename)
+        childnetwork.save('./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)
+   
+    current_app.config['AAL'] = auto_aug_leanrer
+    current_app.config['NP'] = num_policies
+    current_app.config['NSP'] = num_sub_policies
+    current_app.config['BS'] = batch_size
+    current_app.config['LR'] = learning_rate
+    current_app.config['TS'] = toy_size
+    current_app.config['ME'] = max_epochs
+    current_app.config['ESN'] = early_stop_num
+    current_app.config['IT'] = iterations
+    current_app.config['ISLENET'] = IsLeNet
+    current_app.config['DSN'] = ds_name
+    current_app.config['ds'] = ds
 
-    print("DONE")
     
+    print("@@@ user input has all stored in the app")
 
-    return None
+    return {'try': 'Hello'}
 
 @app.route('/api')
 def index():
-    return {'name': 'Hello'}
\ No newline at end of file
+    return {'name': 'Hello'}