Update app.py

app.py

@@ -1,96 +1,149 @@
-import streamlit as st
-import cv2
-from ultralytics import YOLO
-import cvzone
-import math
-import pygame
-
-# Initialize pygame mixer
-pygame.mixer.init()
-
-# Load sound
-alert_sound = pygame.mixer.Sound('siren-alert-96052.mp3')  # Using raw string
-
-# Load the model
-model = YOLO('best.pt')
-
-# Reading the classes
-classnames = ['Drowsy', 'Awake']
-
-# Streamlit UI
-st.title("Real-Time Drowsiness Detection")
-
-# Layout
-col1, col2 = st.columns(2)
-
-with col1:
-    start_button = st.button('Start Webcam')
-
-with col2:
-    stop_button = st.button('Stop Webcam')
-
-stframe = st.empty()
-status_text = st.empty()
-message_text = st.empty()
-
-if start_button:
-    cap = cv2.VideoCapture(0)
-    drowsy_count = 0  # Counter for consecutive "Drowsy" detections
-    
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            status_text.write("Failed to grab frame")
-            break
-        
-        frame = cv2.resize(frame, (640, 480))
-
-        # Run the model on the frame
-        result = model(frame, stream=True)
-
-        # Flag to track if "Drowsy" is detected in this frame
-        drowsy_detected = False
-
-        # Getting bbox, confidence, and class name information to work with
-        for info in result:
-            boxes = info.boxes
-            for box in boxes:
-                confidence = box.conf[0]
-                confidence = math.ceil(confidence * 100)
-                Class = int(box.cls[0])
-                if confidence > 50:
-                    x1, y1, x2, y2 = box.xyxy[0]
-                    x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
-                    cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 5)
-                    cvzone.putTextRect(frame, f'{classnames[Class]} {confidence}%', [x1 + 8, y1 + 100],
-                                       scale=1.5, thickness=2)
-                    if classnames[Class] == 'Drowsy':
-                        drowsy_detected = True
-
-        # Increment the counter if "Drowsy" is detected, otherwise reset the counter
-        if drowsy_detected:
-            drowsy_count += 1
-            status_text.write("Drowsiness detected!")
-        else:
-            drowsy_count = 0
-            status_text.write("Monitoring...")
-
-        # Play alert sound and send message if "Drowsy" is detected 3 or more times
-        if drowsy_count >= 3:
-            pygame.mixer.Sound.play(alert_sound)
-            message_text.write("**Be careful!** Drowsiness detected multiple times!")
-            drowsy_count = 0  # Reset the counter after playing the sound
-
-        # Convert image back to RGB for Streamlit
-        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-
-        # Display the image
-        stframe.image(frame, channels="RGB")
-
-        # Check if stop button is pressed
-        if stop_button:
-            break
-
-    cap.release()
-    status_text.write("Webcam stopped.")
-    message_text.write("")
+import streamlit as st
+import cv2
+from ultralytics import YOLO
+import cvzone
+import math
+import pygame
+import numpy as np
+
+# Initialize pygame mixer
+pygame.mixer.init()
+
+# Load sound
+alert_sound = pygame.mixer.Sound('alarm.mp3')
+
+# Load the model
+model = YOLO('best.pt')
+
+# Reading the classes
+classnames = ['Drowsy', 'Awake']
+
+# Streamlit UI
+st.set_page_config(layout="wide")  # Set wide layout
+
+# Add the logo to the sidebar
+logo_path = "logo.png"  # Use the uploaded file path
+st.sidebar.empty()  # Add empty space
+st.sidebar.image(logo_path, use_column_width=True)
+
+# Create a sidebar for navigation
+st.sidebar.title("Options")
+page = st.sidebar.selectbox("Choose a page", ["Webcam Detection", "Image Upload"])
+
+st.title("Drowsiness Detection")
+
+if page == "Webcam Detection":
+    st.header("Real-Time Drowsiness Detection")
+
+    # Layout
+    col1, col2 = st.columns(2)
+
+    with col1:
+        start_button = st.button('Start Webcam')
+
+    with col2:
+        stop_button = st.button('Stop Webcam')
+
+    alert_placeholder = st.empty()  # Placeholder for alerts
+    stframe = st.empty()
+    status_text = st.empty()
+    message_text = st.empty()
+
+    if start_button:
+        cap = cv2.VideoCapture(0)
+        drowsy_count = 0  # Counter for consecutive "Drowsy" detections
+        
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                status_text.write("Failed to grab frame")
+                break
+            
+            frame = cv2.resize(frame, (640, 480))
+
+            # Run the model on the frame
+            result = model(frame, stream=True)
+
+            # Flag to track if "Drowsy" is detected in this frame
+            drowsy_detected = False
+
+            # Getting bbox, confidence, and class name information to work with
+            for info in result:
+                boxes = info.boxes
+                for box in boxes:
+                    confidence = box.conf[0]
+                    confidence = math.ceil(confidence * 100)
+                    Class = int(box.cls[0])
+                    if confidence > 50:
+                        x1, y1, x2, y2 = box.xyxy[0]
+                        x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+                        cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 5)
+                        cvzone.putTextRect(frame, f'{classnames[Class]} {confidence}%', [x1 + 8, y1 + 100],
+                                           scale=1.5, thickness=2)
+                        if classnames[Class] == 'Drowsy':
+                            drowsy_detected = True
+
+            # Increment the counter if "Drowsy" is detected, otherwise reset the counter
+            if drowsy_detected:
+                drowsy_count += 1
+                status_text.write("Drowsiness detected!")
+            else:
+                drowsy_count = 0
+                status_text.write("Monitoring...")
+
+            # Play alert sound and send message if "Drowsy" is detected 3 or more times
+            if drowsy_count >= 3:
+                pygame.mixer.Sound.play(alert_sound)
+                alert_placeholder.markdown(
+                    f'<div style="color: red; font-size: 24px; border: 2px solid red; padding: 10px;">**Be careful! Drowsiness detected!**</div>',
+                    unsafe_allow_html=True,
+                )
+                drowsy_count = 0  # Reset the counter after playing the sound
+
+            # Convert image back to RGB for Streamlit
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+            # Display the image
+            stframe.image(frame, channels="RGB")
+
+            # Check if stop button is pressed
+            if stop_button:
+                break
+
+        cap.release()
+        status_text.write("Webcam stopped.")
+        message_text.write("")
+        alert_placeholder.empty()
+
+elif page == "Image Upload":
+    st.header("Drowsiness Detection on Image")
+
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file is not None:
+        # Read the image
+        file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+        frame = cv2.imdecode(file_bytes, 1)
+
+        # Perform prediction
+        results = model(frame, stream=True)
+
+        # Process the results
+        for result in results:
+            boxes = result.boxes
+            for box in boxes:
+                confidence = box.conf[0]
+                confidence = math.ceil(confidence * 100)
+                Class = int(box.cls[0])
+                if confidence > 50:
+                    x1, y1, x2, y2 = box.xyxy[0]
+                    x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+                    cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 5)
+                    cv2.putText(frame, f'{classnames[Class]} {confidence}%', (x1 + 8, y1 + 100),
+                                cv2.FONT_HERSHEY_SIMPLEX, 1.5, (255, 255, 255), 2, cv2.LINE_AA)
+
+        # Convert image back to RGB for Streamlit
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+        # Display the image
+        st.image(frame, channels="RGB")