Update app.py

app.py

@@ -1,4 +1,3 @@
-import cv2
 import numpy as np
 import streamlit as st
 from tensorflow.keras.preprocessing.image import img_to_array
@@ -29,42 +28,31 @@ 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()
+# Webcam input using Streamlit's camera_input widget
+if run:
+    camera_input = st.camera_input("Capture image")
+    
+    if camera_input:
+        # Convert the image to RGB format and resize it for prediction
+        img_resized = cv2.resize(camera_input, (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 webcam feed
+        FRAME_WINDOW.image(camera_input)