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import streamlit as st
import tensorflow as tf
from tensorflow.keras.applications.imagenet_utils import decode_predictions
import cv2
from PIL import Image, ImageOps
import numpy as np
@st.cache(allow_output_mutation=True)
def load_model():
model=tf.keras.models.load_model('keras.h5')
return model
with st.spinner('Model is being loaded..'):
model=load_model()
st.write("""
# Image Classification
"""
)
file = st.file_uploader("Upload the image to be classified U0001F447", type=["jpg", "png"])
st.set_option('deprecation.showfileUploaderEncoding', False)
def upload_predict(upload_image, model):
size = (224,224)
image = ImageOps.fit(upload_image, size, Image.ANTIALIAS)
image = np.asarray(image)
img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
img_resize = cv2.resize(img, dsize=(224, 224),interpolation=cv2.INTER_CUBIC)
image = np.asarray(img_resize, dtype=np.float32).reshape(1, 224, 224, 3)
# Normalize the image array
image = (image / 127.5) - 1
# Predicts the model
prediction = model.predict(image)
index = np.argmax(prediction)
classname = ['Glass', 'NoGlass']
class_name = classname[index]
return class_name
if file is None:
st.text("Please upload an image file")
else:
image = Image.open(file)
st.image(image, use_column_width=True)
predictions = upload_predict(image, model)
image_class = str(predictions)
st.write("The image is classified as",image_class)
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