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import streamlit as st
import tensorflow as tf
from PIL import Image
from src.utils import *
from src.vertex import *
st.set_page_config(
page_title="Hip-Implant Image Classification",
page_icon=":robot:",
layout="centered",
initial_sidebar_state="expanded",
menu_items={
'How to use': "# Upload an image of a hip-implant (search <loose hip implant> on google), the app will classify the hip-implant as loose or in-control."
}
)
#creating session states
create_session_state()
image = Image.open('./image/title.jpg')
st.image(image)
st.title(":red[My AI Journey] :blue[Nishant Guvvada] X-ray Assistant")
with st.sidebar:
image = Image.open('./image/sidebar_image.jpg')
st.image(image)
st.markdown("<h2 style='text-align: center; color: red;'>Settings Tab</h2>", unsafe_allow_html=True)
st.write("Model Settings:")
#define the temeperature for the model
temperature_value = st.slider('Temperature :', 0.0, 1.0, 0.2)
st.session_state['temperature'] = temperature_value
#define the temeperature for the model
token_limit_value = st.slider('Token limit :', 1, 1024, 256)
st.session_state['token_limit'] = token_limit_value
#define the temeperature for the model
top_k_value = st.slider('Top-K :', 1,40,40)
st.session_state['top_k'] = top_k_value
#define the temeperature for the model
top_p_value = st.slider('Top-P :', 0.0, 1.0, 0.8)
st.session_state['top_p'] = top_p_value
if st.button("Reset Session"):
reset_session()
st.image(bytes_data, caption='User uploaded image')
st.balloons()
@st.cache(allow_output_mutation=True)
def load_model():
model=tf.keras.models.load_model('/Hip-Implant/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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