Update app.py

app.py

@@ -3,47 +3,46 @@ import imutils
 import gradio as gr
 import numpy as np
 
-
+# ✅ Use OpenCV’s built-in Haar Cascade path (no local folder needed)
 face_detector = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
 
 def detect_faces(img, size, neighbours, scale):
-    frame = np.array(img) 
+    frame = np.array(img)
     frame = frame[:, :, ::-1].copy()
     frame = imutils.resize(frame, width=500)
     gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
 
+    # ✅ Use slider values safely
     faceRects = face_detector.detectMultiScale(
-        gray, scaleFactor=scale, minNeighbors=int(neighbours), minSize=(int(size), int(size))
+        gray,
+        scaleFactor=float(scale),
+        minNeighbors=int(neighbours),
+        minSize=(int(size), int(size))
     )
 
-    box_data = []
-    class_labels = {0: "face"}
-
     for (x, y, w, h) in faceRects:
-        frame = cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
-        midX = int(x + w / 2)
-        midY = int(y + h / 2)
-        box = {
-            "position": {"middle": [midX, midY], "width": float(w), "height": float(h)},
-            "domain": "pixel",
-            "class_id": 0
-        }
-        box_data.append(box)
+        cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
 
     re_im = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
     return re_im
 
-image = gr.components.Image()
-out_im = gr.components.Image()
+# ✅ Define Gradio components
+image = gr.Image(label="Upload an Image")
+out_im = gr.Image(label="Detected Faces")
 
-size_slider = gr.components.Slider(minimum=5, maximum=50, value=30, step=5, label="MinSize in Pixel")
-neighbour_slider = gr.components.Slider(minimum=1, maximum=20, value=5, step=1, label="Min Number of Neighbours")
-scale_slider = gr.components.Slider(minimum=1.1, maximum=2.0, value=1.3, step=0.1, label="Scale Factor")
+size_slider = gr.Slider(minimum=5, maximum=50, value=30, step=5, label="Min Face Size (pixels)")
+neighbour_slider = gr.Slider(minimum=1, maximum=20, value=5, step=1, label="Min Neighbours")
+scale_slider = gr.Slider(minimum=1.1, maximum=2.0, value=1.3, step=0.1, label="Scale Factor")
 
-Iface = gr.Interface(
+# ✅ Gradio Interface
+demo = gr.Interface(
     fn=detect_faces,
     inputs=[image, size_slider, neighbour_slider, scale_slider],
     outputs=out_im,
-    title="Haar Cascade Object Detection",
-    description="Face Detection with Haar Cascades using OpenCV"
-).launch()
+    title="Haar Cascade Face Detection",
+    description="Detect faces using OpenCV's Haar Cascade classifier. Adjust parameters to fine-tune detection."
+)
+
+# ✅ Hugging Face needs this to run the app
+if __name__ == "__main__":
+    demo.launch()