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#-----Make all the Necessary imports-----
from fastai.vision import *
from fastai.imports import *
from fastai.learner import *
from fastai.vision.all import *
import streamlit as st
import numpy as np
import matplotlib.image as mpimg
import os
import time
from PIL import Image
import requests
from io import BytesIO
import pathlib
# st.set_page_config(layout="wide")
#for windows deployment
# temp = pathlib.PosixPath
# pathlib.PosixPath = pathlib.WindowsPath
#For linux deployment
plt = platform.system()
if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath
path = Path('.')
with open('style.css') as f:
st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
#------Create/Define all functions
def get_x(r):
return image_path/r['train_image_name']
def get_y(r):
return r['class']
def load_model():
model = load_learner(path/'binary_model.pkl')
return model
model = load_model()
def display_image(display_img):
st.image(display_img, width=400)
# use_column_width=True
def make_pred(model, img):
# Temporarily displays a message while executing
with st.spinner('Performing Diagnosis...'):
time.sleep(1)
pred, pred_idx, prob = model.predict(img)
pred_prob = f'{prob[pred_idx]*100:.0f}%'
# Display the prediction
if pred == '1':
pred_state = 'Presence of Diabetic Retinopathy'
# st.success("Presence of Diabetic Retinopathy.")
if prob[pred_idx]*100 <= 85.0:
rec_action = 'Please visit an Opthalmologist to confirm your Daignosis'
else:
rec_action = 'Please visit an Opthalmologist for prompt treatment.'
else:
pred_state = 'Absence of Diabetic Retinopathy'
# st.success("Absence of Diabetic Retinopathy.")
if prob[pred_idx]*100 <= 85.0:
rec_action = 'Please visit an Opthalmologist to confirm your Daignosis'
else:
rec_action = 'Please be sure to go for Diagnosis once a year.'
return pred_state, rec_action, pred_prob
########--------Setup Diagnosis Page--------########
# if selected_nav == 'Diagnosis':
########-------Create Side Bar---------########
#For image upload
img_upload = st.sidebar.file_uploader(label = 'Upload a Fundus Image for Diagnosis',
type=['png', 'jpg', 'jpeg'])
# For image selection
test_images = os.listdir(path/'sample')
img_selected = st.sidebar.selectbox(
'Please Select a Sample Fundus Image:', test_images)
if img_selected:
# Read the image
file_path = path/'sample'/img_selected
# Get the image to display
display_img = Image.open(file_path)
# display_img = display_img.resize((244,224))
img = PILImage.create(file_path)
if img_upload:
display_img = Image.open(img_upload)
img = PILImage.create(img_upload)
st.markdown("""
<h3 style="text-align:center;color:#006ef5;">Diagnosing Diabetic Retinopathy with AI (DEMO)</h3>
""", unsafe_allow_html=True)
st.markdown("##")
st.markdown("""
<p> <b>Instruction:</b> Please upload a fundus image (using the sidebar) for diagnosis or select a sample image</p>
""", unsafe_allow_html=True)
with st.container():
left_col, right_col = st.columns((2,1))
with left_col:
display_image(display_img)
with right_col:
pred_state, rec_action, pred_prob = make_pred(model, img)
# st.markdown("##")
if pred_state=="Presence of Diabetic Retinopathy":
st.markdown(f"""
<p style="text-align:left;"> <b>Diagnosis Result:</b></p> <br>
<p style="color:red;text-align:left;font-size:16px;">{pred_state}</p>
"""
, unsafe_allow_html=True)
else:
st.markdown(f"""
<p style="text-align:left;"> <b>Diagnosis Result:</b></p>
<p style="color:green;text-align:left;font-size:16px;">{pred_state}</p>
"""
, unsafe_allow_html=True)
st.markdown(f"""
<p style="text-align:left;"> <b>Diagnosis Confidence:</b></b></p>
<p style="color:#006ef5;text-align:left;font-size:16px;">{pred_prob}</p>""", unsafe_allow_html=True)
# st.markdown(f" **Diagnosis Confidence:** {pred_prob}")
st.markdown(f"""
<p style="text-align:left;"> <b>Recommendaton:</b></p>
<p style="color:#006ef5;text-align:left;font-size:16px;">{rec_action}</p>
""", unsafe_allow_html=True)
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