Create app.py

app.py

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+# app.py
+import streamlit as st
+import cv2
+import numpy as np
+from PIL import Image
+
+# Load Haar Cascade
+@st.cache_resource
+def load_cascade():
+    cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+    if cascade.empty():
+        raise Exception("Haar Cascade file not loaded!")
+    return cascade
+
+# Detect faces
+def detect_faces(image, face_cascade):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5)
+    for (x, y, w, h) in faces:
+        cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)
+    return image, len(faces)
+
+# Streamlit app
+def main():
+    st.title("Real-Time Face Detection App")
+    st.write("Upload an image, and the app will detect faces!")
+
+    # Load Haar Cascade
+    face_cascade = load_cascade()
+
+    # File uploader
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+    if uploaded_file is not None:
+        # Read the image
+        image = np.array(Image.open(uploaded_file))
+        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+        # Detect faces
+        result_image, face_count = detect_faces(image, face_cascade)
+
+        # Convert BGR to RGB for display
+        result_image = cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB)
+
+        # Display the image
+        st.image(result_image, caption=f"Detected {face_count} face(s).", use_column_width=True)
+
+if __name__ == "__main__":
+    main()