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import streamlit as st |
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from PIL import Image |
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import pandas as pd |
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import numpy as np |
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from keras.models import Sequential |
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from keras.layers import Dense |
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from keras.layers import Dropout |
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from keras.layers import Flatten |
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from keras.layers.convolutional import Convolution2D |
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from keras.layers.convolutional import MaxPooling2D |
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from sklearn.preprocessing import LabelEncoder, OneHotEncoder |
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from tensorflow.keras.utils import img_to_array, load_img |
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from keras import backend as K |
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from subprocess import check_output |
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from sklearn import preprocessing |
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""" |
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# AI_ML |
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""" |
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uploaded_file = st.file_uploader("Choose a picture", type=["png","jpg","jpeg"]) |
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if uploaded_file is not None: |
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st.image(Image.open(uploaded_file),width=250) |
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train = pd.read_csv("sign_mnist_train.csv").values |
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test = pd.read_csv("sign_mnist_test.csv").values |
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trainX = train[:, 1:].reshape(train.shape[0], 1, 28, 28).astype('float32') |
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X_train = trainX / 255.0 |
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y_train = train[:, 0] |
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testX = test[:, 1:].reshape(test.shape[0], 1, 28, 28).astype('float32') |
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X_test = testX / 255.0 |
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y_test = test[:, 0] |
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lb = preprocessing.LabelBinarizer() |
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y_train = lb.fit_transform(y_train) |
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y_test = lb.fit_transform(y_test) |
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model = Sequential() |
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try: |
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if K.backend() == 'theano': |
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K.set_image_data_format('channels_first') |
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else: |
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K.set_image_data_format('channels_last') |
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except AttributeError: |
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if K._BACKEND == 'theano': |
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K.set_image_dim_ordering('th') |
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else: |
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K.set_image_dim_ordering('tf') |
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model.add(Convolution2D(30, 5, 5, padding='same', input_shape=(1, 28, 28), activation='relu')) |
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model.add(MaxPooling2D(pool_size=(1, 1))) |
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model.add(Convolution2D(15, 3, 3, padding='same', activation='relu')) |
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model.add(MaxPooling2D(pool_size=(2, 2), padding='same')) |
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model.add(Dropout(0.2)) |
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model.add(Flatten()) |
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model.add(Dense(24, activation='relu')) |
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model.add(Dense(24, activation='softmax')) |
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model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) |
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model.fit(X_train, y_train, epochs=20, batch_size=128) |
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score = model.evaluate(X_test, y_test, batch_size=128) |
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model.summary() |
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if uploaded_file is not None: |
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img = load_img(uploaded_file, grayscale=True, target_size=(28, 28)) |
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img = img_to_array(img) |
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img = img.reshape(1, 1, 28, 28) |
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img = img.astype('float32') |
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img = img / 255.0 |
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predict_x = model.predict(img) |
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classes_x = np.argmax(predict_x,axis=1) |
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sign = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y'] |
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st.write("Prediction : " + sign[classes_x[0]]) |
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