Add application file

app.py

@@ -0,0 +1,41 @@
+import cv2
+import dlib
+import streamlit as st
+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()
+
+# Define the prediction function
+def predict(image):
+    img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    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}
+    return {"No Face Detected": 0.0}
+
+# Create the Streamlit app interface
+st.title("Down Syndrome Classifier")
+
+uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+if uploaded_image is not None:
+    image = Image.open(uploaded_image)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    st.write("Classifying...")
+
+    result = predict(image)
+
+    st.write("Classification Results:")
+    for label, score in result.items():
+        st.write(f"{label}: {score:.4f}")

requirements.txt

@@ -0,0 +1,6 @@
+pillow
+opencv-python-headless
+dlib
+transformers
+torch
+numpy