Add application file

app.py

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+import cv2
+import dlib
+import gradio as gr
+from PIL import Image
+from transformers import pipeline
+
+# Load pre-trained image classification model from transformers library
+model = pipeline("image-classification", model="0x70DA/down-syndrome-classifier")
+
+# Load face detector from dlib library
+detector = dlib.get_frontal_face_detector()
+
+
+def predict(img):
+    faces = detector(img)
+    if len(faces) > 0:
+        face = faces[0]  # Assuming there's only one face in the image
+        x, y, w, h = face.left(), face.top(), face.width(), face.height()
+        cropped_face = img[y : y + h, x : x + w]
+        # Convert the cropped image to a PIL image
+        pil_image = Image.fromarray(cv2.cvtColor(cropped_face, cv2.COLOR_BGR2RGB))
+    pred = model(pil_image)
+    return {o["label"]: o["score"] for o in pred}
+
+
+demo = gr.Interface(
+    fn=predict, inputs=gr.components.Image(), outputs=gr.components.Label()
+)
+demo.launch()

requirements.txt

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+pillow
+cv2
+dlib
+transfomers