Create app.py

app.py

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+import numpy as np
+import cv2
+import gradio as gr
+
+# Load Haar Cascade classifier
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
+
+# Face Detection Function
+def detect_faces(image_np):
+    # Convert image to grayscale
+    gray_image = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
+
+    # Detect faces
+    faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+
+    # Draw rectangles around faces
+    for (x, y, w, h) in faces:
+        cv2.rectangle(image_np, (x, y), (x + w, y + h), (0, 255, 0), 2)
+
+    return image_np
+
+# Create Gradio Interface
+iface = gr.Interface(
+    fn=detect_faces,
+    inputs="image",
+    outputs="image",
+    title="Face Detection",
+    description="Upload an image, and the model will detect faces and draw bounding boxes around them."
+)
+
+iface.launch()