Create app.py

app.py

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+import cv2
+import numpy as np
+import gradio as gr
+
+# Load the Haar Cascade model
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+def detect_faces(image):
+    # Convert image (PIL -> NumPy)
+    img = np.array(image)
+    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+
+    # Detect faces
+    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5)
+
+    # Draw rectangles around detected faces
+    for (x, y, w, h) in faces:
+        cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 3)
+
+    return img, f"✅ Faces Detected: {len(faces)}" if len(faces) > 0 else "😕 No Faces Detected"
+
+# Build Gradio Interface
+title = "🧠 Face Detection App"
+description = """
+Upload an image to detect faces automatically using OpenCV Haar Cascade.  
+Works with multiple faces and outputs an annotated image!
+"""
+
+iface = gr.Interface(
+    fn=detect_faces,
+    inputs=gr.Image(type="pil", label="Upload Your Image"),
+    outputs=[gr.Image(label="Detected Faces"), gr.Textbox(label="Result")],
+    title=title,
+    description=description,
+    theme="soft",  # You can try "gradio/soft", "gradio/dark", "gradio/base", etc.
+    examples=[
+        ["https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cat.png"],
+    ]
+)
+
+iface.launch()