Update app.py

app.py

@@ -1,47 +1,45 @@
 import streamlit as st
 import cv2
 import numpy as np
-from PIL import Image
 
 # Load the Haar Cascade face detector
 cascade_path = "haarcascade_frontalface_default.xml"
 detector = cv2.CascadeClassifier(cascade_path)
 
-# Streamlit app title
-st.title("Face Detection App")
+# Check if the cascade file is loaded
+if detector.empty():
+    st.error("Error: Could not load Haar Cascade. Ensure the XML file is in the correct location.")
+else:
+    # Streamlit app title
+    st.title("Face Detection App")
 
-# Sidebar instructions
-st.sidebar.title("Upload an Image")
-st.sidebar.write("Upload an image to detect faces.")
+    # File uploader
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 
-# Upload file option
-uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+    if uploaded_file is not None:
+        # Convert uploaded file to OpenCV format
+        file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+        image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
 
-if uploaded_file is not None:
-    # Convert uploaded file to OpenCV format
-    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
-    image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
+        if image is None:
+            st.error("Error: Could not process the uploaded image.")
+        else:
+            # Convert image to grayscale
+            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
 
-    # Check if the image was loaded successfully
-    if image is None:
-        st.error("Error: Could not load the image. Please try again.")
-    else:
-        # Convert image to grayscale
-        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+            # Perform face detection
+            rects = detector.detectMultiScale(
+                gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30)
+            )
+            st.write(f"Detected {len(rects)} face(s).")
 
-        # Perform face detection
-        rects = detector.detectMultiScale(
-            gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30)
-        )
-        
-        st.write(f"Detected {len(rects)} face(s).")
+            # Draw bounding boxes around detected faces
+            for (x, y, w, h) in rects:
+                cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
 
-        # Draw bounding boxes around detected faces
-        for (x, y, w, h) in rects:
-            cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
+            # Convert image to RGB for display
+            image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
-        # Convert the image back to RGB for display in Streamlit
-        image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            # Display the image
+            st.image(image_rgb, caption="Detected Faces", use_column_width=True)
 
-        # Display the image with detected faces
-        st.image(image_rgb, caption="Detected Faces", use_column_width=True)