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)