Update app.py

app.py

@@ -1,6 +1,19 @@
 
 import streamlit as st
 from PIL import Image
+import pandas as pd
+import numpy as np
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.layers import Dropout
+from keras.layers import Flatten
+from keras.layers.convolutional import Convolution2D
+from keras.layers.convolutional import MaxPooling2D
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder
+from keras.preprocessing.image import img_to_array, load_img
+from keras import backend as K
+from subprocess import check_output
+from sklearn import preprocessing
 
 """
 # AI_ML
@@ -8,7 +21,62 @@ from PIL import Image
 
 uploaded_file = st.file_uploader("Choose a picture", type=["png","jpg","jpeg"])
 
-st.write(uploaded_file)
-
 if uploaded_file is not None:
   st.image(Image.open(uploaded_file),width=250)
+  
+  
+
+train = pd.read_csv("./sign_mnist_train.csv").values
+test = pd.read_csv("./sign_mnist_test.csv").values
+
+trainX = train[:, 1:].reshape(train.shape[0], 1, 28, 28).astype('float32')
+X_train = trainX / 255.0
+y_train = train[:, 0]
+
+testX = test[:, 1:].reshape(test.shape[0], 1, 28, 28).astype('float32')
+X_test = testX / 255.0
+y_test = test[:, 0]
+
+lb = preprocessing.LabelBinarizer()
+y_train = lb.fit_transform(y_train)
+y_test = lb.fit_transform(y_test)
+
+
+model = Sequential()
+
+try:
+    if K.backend() == 'theano':
+        K.set_image_data_format('channels_first')
+    else:
+        K.set_image_data_format('channels_last')
+except AttributeError:
+    if K._BACKEND == 'theano':
+        K.set_image_dim_ordering('th')
+    else:
+        K.set_image_dim_ordering('tf')
+
+model.add(Convolution2D(30, 5, 5, padding='same', input_shape=(1, 28, 28), activation='relu'))
+model.add(MaxPooling2D(pool_size=(1, 1)))
+model.add(Convolution2D(15, 3, 3, padding='same', activation='relu'))
+model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
+model.add(Dropout(0.2))
+model.add(Flatten())
+model.add(Dense(24, activation='relu'))
+#model.add(Dense(50, activation='relu'))
+model.add(Dense(24, activation='softmax'))
+
+model.fit(X_train, y_train, epochs=20, batch_size=128)
+score = model.evaluate(X_test, y_test, batch_size=128)
+model.summary()
+
+
+
+img = load_img("o.jpg", grayscale=True, target_size=(28, 28))
+img = img_to_array(img)
+img = img.reshape(1, 1, 28, 28)
+img = img.astype('float32')
+img = img / 255.0
+
+predict_x = model.predict(img)
+classes_x = np.argmax(predict_x,axis=1)
+print(classes_x[0])