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import streamlit as st
from PIL import Image
import numpy as np
import plotly.graph_objects as go
from models import predict
import cv2
google_form_link = 'https://docs.google.com/forms/d/1xKeveRFf90_wCX-tjMInFC48XmFF8HOsPSQ47ruOFk0/edit'
# Load the pre-trained Haar Cascade for eye detection
eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml')
# Function to check if the image contains an eye
def contains_eye(image):
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
eyes = eye_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
return len(eyes) > 0
# Function to load the image
def load_image(image_file):
img = Image.open(image_file)
return img
# Streamlit app title
st.title("Eye Cataract Detection")
# Streamlit header and subheader
st.header('Upload the image')
# File uploader widget
image_file = st.file_uploader("Upload Images", type=["png", "jpg", "jpeg"])
# Check if an image file is uploaded
if image_file is not None:
img = load_image(image_file)
# Display the uploaded image
st.image(img, width=250)
# Convert image to OpenCV format
open_cv_image = np.array(img)
if contains_eye(open_cv_image):
# Button for detection
if st.button('Detect'): # This line adds a 'Detect' button
# Predict the label and probability
label, prob = predict(open_cv_image)
# Use markdown to style the text and include emojis
if prob > 0.5:
st.markdown(f"<h2 style='color: red;'>Cataract Detected 😟</h2>", unsafe_allow_html=True)
#st.markdown(f"### Probability: **{prob:.2f}**")
else:
st.markdown(f"<h2 style='color: green;'>No Cataract Detected 😄</h2>", unsafe_allow_html=True)
#st.markdown(f"### Probability: **{prob:.2f}**")
# Pie chart visualization
fig = go.Figure(data=[go.Pie(labels=['Cataract', 'No Cataract'],
values=[prob, 1 - prob],
hoverinfo='label+percent',
pull=[0, 0])])
fig.update_layout(title_text='Cataract Detection Probability')
st.plotly_chart(fig)
st.subheader("Doctor's Verification")
st.markdown(f"[Click here to provide feedback on the cataract detection results]({google_form_link})", unsafe_allow_html=True)
else:
st.error("No eyes detected in the image. Please upload a relevant eye image.")
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