Create app.py

app.py

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+import streamlit as st
+import requests
+from io import BytesIO
+from PIL import Image
+from transformers import pipeline
+
+@st.cache_data(show_spinner=False)
+def load_age_classifier():
+    # Load and cache the image-classification pipeline for the age classifier
+    return pipeline("image-classification", model="nateraw/vit-age-classifier")
+
+def classify_age(image: Image.Image):
+    """
+    Classify the age of a person in an image using the nateraw/vit-age-classifier model.
+    
+    Args:
+        image (PIL.Image): The image to classify.
+    
+    Returns:
+        list: Predictions with labels and corresponding confidence scores.
+    """
+    age_classifier = load_age_classifier()
+    return age_classifier(image)
+
+def main():
+    st.title("Age Classification with ViT Age Classifier")
+    st.write("This demo uses the `nateraw/vit-age-classifier` model from Hugging Face to predict age categories from facial images.")
+    
+    # Let the user choose the input method
+    input_method = st.radio("Select input method:", ("Image URL", "Upload an Image"))
+
+    image = None
+
+    if input_method == "Image URL":
+        image_url = st.text_input(
+            "Enter the Image URL",
+            "https://github.com/dchen236/FairFace/blob/master/detected_faces/race_Asian_face0.jpg?raw=true"
+        )
+        if image_url:
+            try:
+                response = requests.get(image_url)
+                image = Image.open(BytesIO(response.content)).convert("RGB")
+                st.image(image, caption="Input Image from URL", use_column_width=True)
+            except Exception as e:
+                st.error(f"Error loading image from URL: {e}")
+    else:
+        uploaded_file = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])
+        if uploaded_file is not None:
+            try:
+                image = Image.open(uploaded_file).convert("RGB")
+                st.image(image, caption="Uploaded Image", use_column_width=True)
+            except Exception as e:
+                st.error(f"Error processing uploaded image: {e}")
+
+    if image is not None:
+        if st.button("Classify Age"):
+            with st.spinner("Classifying..."):
+                predictions = classify_age(image)
+                st.write("### Classification Results:")
+                for pred in predictions:
+                    st.write(f"**Label:** {pred['label']}  |  **Confidence:** {pred['score']:.2f}")
+                    
+if __name__ == "__main__":
+    main()