Uploaded app.py

app.py

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+import numpy as np
+import cv2
+import gradio as gr
+
+def detect_face(img):
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    path = "haarcascade_frontalface_default.xml"
+    face_cascade = cv2.CascadeClassifier(path)
+    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.05, minNeighbors=17, minSize=(40, 40))
+    return len(faces)
+
+def face_detector(image):
+    img_np = np.array(image)
+    img = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
+    # img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    num_faces = detect_face(img)
+    return num_faces
+
+title = "Face Detector: Counts the Number of Faces in an Image\n\n"
+title += "<span style='font-size: smaller;'>Subtitle: The face detector counts the number of faces and returns it. It works fairly well. This is built based on Haar Cascade Algorithm.</span>"
+
+iface = gr.Interface(
+    fn=face_detector,
+    inputs=gr.inputs.Image(type="pil", label="Upload an Image"),
+    outputs="text",
+    title=title,
+    examples=[
+        ["sample_faces/angry_face.jpg"],
+        ["sample_faces/beard_man.jpg"],
+        ["sample_faces/bw_face.jpg"],
+        ["sample_faces/faces.jpeg"],
+        ["sample_faces/glass_man.jpg"],
+        ["sample_faces/normal_face.jpg"],
+        ["sample_faces/red_dress.jpg"]
+    ],
+    allow_flagging=False,
+)
+
+if __name__ == "__main__":
+    iface.launch()