Upload app.py

app.py

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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()