Update app.py

app.py

@@ -1,9 +1,15 @@
 import streamlit as st
 import cv2
 import numpy as np
+import urllib.request
+
+# Download the Haar cascade XML file
+cascade_file_url = "https://raw.githubusercontent.com/opencv/opencv/master/data/haarcascades/haarcascade_frontalface_default.xml"
+cascade_file_path = "haarcascade_frontalface_default.xml"
+urllib.request.urlretrieve(cascade_file_url, cascade_file_path)
 
 # Load the pre-trained object detection model
-model = cv2.CascadeClassifier('path/to/haarcascade.xml')
+model = cv2.CascadeClassifier(cascade_file_path)
 
 # Function to perform object recognition
 def detect_objects(image):
@@ -14,15 +20,23 @@ def detect_objects(image):
     return image
 
 # Streamlit app
-st.title('Object Recognition')
+st.title('Face Detection')
 uploaded_file = st.file_uploader('Upload an image', type=['jpg', 'jpeg', 'png'])
 
 if uploaded_file is not None:
     # Read the uploaded image
     image = cv2.imdecode(np.fromstring(uploaded_file.read(), np.uint8), 1)
 
-    # Perform object recognition
-    result = detect_objects(image)
+    if image is None:
+        st.write('Error: Unable to read the image. Please make sure the uploaded file is a valid image.')
+
+    else:
+        # Perform object recognition
+        result = detect_objects(image)
+
+        if result is None:
+            st.write('Error: Face detection failed.')
 
-    # Display the result
-    st.image(result, channels='BGR')
+        else:
+            # Display the result
+            st.image(result, channels='BGR')