Update app.py

app.py

@@ -1,81 +1,54 @@
-cascade_path = 'path/to/haarcascade_frontalface_default.xml'
-
-import streamlit as st
+import os
 import cv2
-import numpy as np
+import streamlit as st
 from PIL import Image
-import os
 
-# Ensure Haar Cascade file is loaded correctly
 def load_cascade():
-    # Path to the Haar Cascade file
+    # Specify the path to the Haar Cascade XML file
     cascade_path = 'haarcascade_frontalface_default.xml'
 
     # Check if the Haar Cascade file exists
     if not os.path.exists(cascade_path):
-        st.error("Haar Cascade file not found! Please upload the file.")
-        raise Exception("Haar Cascade file not loaded!")
+        raise Exception(f"Haar Cascade file not found at {cascade_path}. Please upload the file.")
     
-    # Load the Haar Cascade file
+    # Load the Haar Cascade classifier
     face_cascade = cv2.CascadeClassifier(cascade_path)
+    
+    # Check if the file was successfully loaded
     if face_cascade.empty():
-        st.error("Haar Cascade file failed to load.")
-        raise Exception("Haar Cascade file not loaded properly!")
+        raise Exception(f"Failed to load Haar Cascade file from {cascade_path}.")
     
     return face_cascade
 
-# Detect faces in the uploaded image
-def detect_faces(image, face_cascade):
-    # Convert the image to grayscale
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    
-    # Perform face detection
-    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5)
-    
-    # Draw rectangles around detected faces
-    for (x, y, w, h) in faces:
-        cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)
-    
-    return image, len(faces)
-
-# Main Streamlit app
 def main():
     st.title("Face Detection App")
-    st.write("Upload an image, and the app will detect faces!")
 
-    # Load the Haar Cascade
-    try:
-        face_cascade = load_cascade()
-    except Exception as e:
-        st.error(f"Error: {e}")
-        return
+    # Load Haar Cascade for face detection
+    face_cascade = load_cascade()
 
-    # File uploader for image
-    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
-    if uploaded_file is not None:
-        # Save the uploaded image temporarily
-        img_path = '/tmp/uploaded_image.jpg'
-        with open(img_path, "wb") as f:
-            f.write(uploaded_file.getbuffer())
-
-        # Open the uploaded image and convert it to a NumPy array
-        image = np.array(Image.open(uploaded_file))
-        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    uploaded_file = st.file_uploader("Upload an Image", type=["jpg", "png", "jpeg"])
 
+    if uploaded_file is not None:
+        # Read the image file
+        image = Image.open(uploaded_file)
+        image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        
         # Detect faces in the image
-        result_image, face_count = detect_faces(image, face_cascade)
+        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
 
-        # Convert BGR to RGB for displaying in Streamlit
-        result_image = cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB)
+        # Draw rectangles around faces
+        for (x, y, w, h) in faces:
+            cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
 
-        # Display the image with face detection results
-        st.image(result_image, caption=f"Detected {face_count} face(s).", use_column_width=True)
+        # Convert image back to RGB and display it
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        st.image(image, caption="Processed Image", use_column_width=True)
 
-        # Display success or failure message
-        if face_count > 0:
-            st.success("Face Detection Successful!")
+        if len(faces) > 0:
+            st.success("Face detection successful!")
         else:
-            st.error("No faces detected. Please try again with a clearer image.")
+            st.warning("No faces detected.")
 
 if __name__ == "__main__":
     main()