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import cv2
import numpy as np
import streamlit as st
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.models import load_model
# Load your trained model
model = load_model('/Users/abhinavyadav/Downloads/DSMP 1.0/eye_detection.h5')
IMG_SIZE = 224 # Resize the image to the input size of your model (e.g., 224x224)
# Streamlit App Title
st.title("👁️ Real-Time Eye Detection")
st.write("Detect whether eyes are open or closed in real-time using your webcam.")
# Sidebar
st.sidebar.title("🔧 Controls")
run = st.sidebar.checkbox("Start Webcam")
st.sidebar.write("Toggle the checkbox to start/stop the webcam.")
st.sidebar.write("Press 'Stop' to end the app.")
st.sidebar.info("Tip: Ensure your webcam is properly connected and accessible.")
# Create a container for video feed (first row)
with st.container():
st.header("📹 Webcam Feed")
FRAME_WINDOW = st.image([])
# Create a container for status display (second row)
with st.container():
st.header("🔍 Eye Status")
status_placeholder = st.markdown("**Status:** Waiting for webcam input...")
# Initialize webcam
cap = cv2.VideoCapture(0)
while run:
ret, frame = cap.read()
if not ret:
status_placeholder.error("Failed to capture image. Please check your webcam.")
break
# Convert frame to RGB
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Resize the frame for model input
img_resized = cv2.resize(frame_rgb, (IMG_SIZE, IMG_SIZE))
# Preprocess the image
img_array = img_to_array(img_resized) / 255.0
img_array = np.expand_dims(img_array, axis=0)
# Predict eye status
prediction = model.predict(img_array)
# Update prediction status
if prediction[0][0] > 0.8:
status = "Eye is Open 👀"
status_color = "green"
else:
status = "Eye is Closed 😴"
status_color = "red"
# Update UI with the prediction status
status_placeholder.markdown(f"**Status:** <span style='color:{status_color}'>{status}</span>", unsafe_allow_html=True)
# Display the video feed
FRAME_WINDOW.image(frame_rgb)
# Release resources when the checkbox is unchecked
cap.release()
cv2.destroyAllWindows()
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