Update app.py

app.py

@@ -1,30 +1,11 @@
 from joblib import dump,load
-from tensorflow.keras.datasets import fashion_mnist
-from sklearn.model_selection import train_test_split
-import numpy as np
+import pandas as pd
 import warnings
-from sklearn.model_selection import StratifiedShuffleSplit
 import gradio as gr
 import cv2
 warnings.filterwarnings("ignore")
-
-(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()# Split the training data into training and validation sets
-X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=5000, random_state=42)
-data_split = StratifiedShuffleSplit(n_splits=1, test_size=0.9, random_state=0) # split data one time into two parts with ratio 10%/90%
-for i, (train_index, test_index) in enumerate(data_split.split(X_train, y_train)):
-   print(f"Fold {i}:")
-   print(f" Train: index={train_index}, size = {len(train_index)}")
-   print(f" Remaining: index={test_index}, size = {len(test_index)}")
-
-small_X_train = X_train[train_index]
-small_y_train = y_train[train_index]
-
-train_size, width, height = small_X_train.shape
-train_size_val, width, height = X_val.shape
-train_size_test, width, height = X_test.shape
-small_X_train_flatten = small_X_train.reshape(train_size, width * height)
-X_valid_flatten = X_val.reshape(train_size_val, width * height)
-X_test_flatten = X_test.reshape(train_size_test, width * height)
+small_X_train_flatten = pd.read_csv('Homework01_trainX_image_flatten.csv')
+small_y_train = pd.read_csv('Homework01_trainy_image_flatten.csv')
 best_knn = load("best_knn.joblib")
 best_log = load("best_log.joblib")
 best_knn.fit(small_X_train_flatten,small_y_train)