Create app.py

app.py

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+import base64
+import cv2
+import numpy as np
+import gradio as gr
+from io import BytesIO
+
+def crop_face(base64_image):
+    # Decode the base64 image
+    img_data = base64.b64decode(base64_image)
+    np_arr = np.frombuffer(img_data, np.uint8)
+    image = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
+    
+    if image is None:
+        print("Could not decode the image")
+        return None
+
+    # Load the pre-trained face detector
+    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+    # Convert the image to grayscale
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # Detect faces in the image
+    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+
+    # If no faces are detected, return None
+    if len(faces) == 0:
+        print("No faces found")
+        return None
+
+    # Crop the first face found
+    x, y, w, h = faces[0]
+    face_crop = image[y:y+h, x:x+w]
+
+    # Encode the cropped face to base64
+    _, buffer = cv2.imencode('.jpg', face_crop)
+    face_base64 = base64.b64encode(buffer).decode('utf-8')
+
+    return face_base64
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=crop_face,
+    inputs="text",
+    outputs="text",
+    title="Face Cropper",
+    description="Input a base64 encoded image to get a base64 encoded cropped face."
+)
+
+if __name__ == "__main__":
+    interface.launch(share=True)