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import streamlit as st
import tensorflow as tf
from PIL import Image
@st.cache(allow_output_mutation=True)
def load_model():
model=tf.keras.models.load_model('./hip_impant_model.h5')
return model
with st.spinner('Model is being loaded..'):
model=load_model()
st.write("""
# Image Classification
"""
)
file = st.file_uploader("Upload an X-ray image")
import cv2
from PIL import Image, ImageOps
import numpy as np
st.set_option('deprecation.showfileUploaderEncoding', False)
def model_prediction(img, model):
resize = tf.image.resize(img, (256,256))
yhat = model.predict(np.expand_dims(resize/255, 0))
if(yhat>0.5):
result = "Prediction is loose"
else:
result = "Prediction is control"
return result
if file is None:
st.text("Please upload an image file")
else:
image = Image.open(file)
st.image(image, use_column_width=True)
predictions = mode_prediction(image, model)
st.write(prediction)
print(
"This image most likely belongs to {}."
.format(prediction)
)
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