Update app.py

app.py

@@ -1,5 +1,7 @@
 import numpy as np
 import streamlit as st
+import cv2                        # Added import for OpenCV
+from PIL import Image             # For image decoding
 from tensorflow.keras.preprocessing.image import img_to_array
 from tensorflow.keras.models import load_model
 
@@ -18,32 +20,26 @@ 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([])
+# Webcam feed and status placeholders
+FRAME_WINDOW = st.image([])
+status_placeholder = st.markdown("**Status:** Waiting for webcam input...")
 
-# Create a container for status display (second row)
-with st.container():
-    st.header("🔍 Eye Status")
-    status_placeholder = st.markdown("**Status:** Waiting for webcam input...")
-
-# Webcam input using Streamlit's camera_input widget
 if run:
+    # Capture an image from the webcam
     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
+    if camera_input:
+        # Decode to PIL Image and convert to RGB
+        img = Image.open(camera_input).convert('RGB')
+        # Resize and convert to array
+        img_resized = img.resize((IMG_SIZE, IMG_SIZE))
         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
+        # Determine status and color
         if prediction[0][0] > 0.8:
             status = "Eye is Open 👀"
             status_color = "green"
@@ -51,8 +47,11 @@ if run:
             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)
+        # Update UI
+        status_placeholder.markdown(
+            f"**Status:** <span style='color:{status_color}'>{status}</span>",
+            unsafe_allow_html=True
+        )
+        FRAME_WINDOW.image(img)
+else:
+    st.write("Webcam is stopped. Check the sidebar to start.")