streamlit_app.py

#Library imports
import numpy as np
import streamlit as st
import cv2
from keras.models import load_model
#import joblib
from keras.preprocessing import image
from io import BytesIO
from tensorflow.keras.applications import resnet50



#Loading the Model
model = load_model('object_classifier.h5', compile=False)

st.image('logo.png')
st.markdown("## Object Classifier App with Deep Learning")
st.markdown("""
This app uses Deep learning (ResNet50) libraries namely keras to identify objects from images.

ResNet-50 is a convolutional neural network that is 50 layers deep. You can load a pretrained version of the network trained on more than a million images from the ImageNet database. The pretrained network can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals.

**Made by Ifeanyi Nneji**

""")

#Name of Classes


#Uploading the dog image
object_image = st.file_uploader("Upload an image...", type=['png','jpg','webp','jpeg'])
submit = st.button('Predict')
#On predict button click
if submit:

    if object_image is not None:

        # Convert the file to an opencv image.
        file_bytes = np.asarray(bytearray(object_image.read()), dtype=np.uint8)
        opencv_image = cv2.imdecode(file_bytes, 1)
        st.image(opencv_image, channels="BGR")
        opencv_image = cv2.resize(opencv_image, (224,224))
        opencv_image.shape = (1,224,224,3)
        predictions = model.predict(opencv_image)
        predicted_classes = resnet50.decode_predictions(predictions, top=5)



        # Displaying the image
        #st.image(object_image, channels="BGR")
        st.markdown("""This is an image of: """)

        for imagenet_id, name, likelihood in predicted_classes[0]:
            st.text('- {}: {:.2f} likelihood'.format(name,likelihood))