app.py

import streamlit as st
import tensorflow as tf
import cv2
import numpy as np
from PIL import Image, ImageOps
import imageio.v3 as iio

@st.cache_resource()
def load_model():
    model=tf.keras.models.load_model('./hip_impant_model.h5')
    return model

st.write("""
         # Image Classification
         """
         )
image = Image.open('./title.jpg')
st.image(image)
st.title(":red[My AI Journey] :blue[Nishant Guvvada] X-ray Assistant")

file = st.file_uploader("Upload an X-ray image", type= ['png', 'jpg'])

def model_prediction(path):
    resize = tf.image.resize(path, (256,256))
    with st.spinner('Model is being loaded..'):
        model=load_model()
    yhat = model.predict(np.expand_dims(resize/255, 0))
    if(yhat>0.5):
        result = "Prediction is loose"
    else:
        result = "Prediction is control"
    return result

def on_click():
    if file is None:
        st.text("Please upload an image file")
    else:
        image = Image.open(file)
        st.image(image, use_column_width=True)
        image = image.convert('RGB')
        predictions = model_prediction(np.array(image))
        st.write(predictions)
        print(
            "This image most likely belongs to {}."
            .format(predictions)
)

st.button('Predict', on_click=on_click)