Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+from PIL import Image
+import torch
+
+#function part
+
+def classify_image(image_path):
+    # Load the pre-trained image classification model
+    classifier = pipeline("image-classification", model="nateraw/vit-age-classifier")
+    image = Image.open(image_path)
+    predictions = classifier(image)
+    return predictions
+
+st.set_page_config(page_title="Age Classifier", page_icon="📷")
+st.header("Image Age Classification")
+
+uploaded_file = st.file_uploader("Upload an Image...", type=["png", "jpg", "jpeg"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    
+    # Perform image classification
+    st.text('Classifying image...')
+    predictions = classify_image(uploaded_file)
+    
+    # Display the top prediction
+    if predictions:
+        top_prediction = predictions[0]
+        st.write(f"**Predicted Age Group:** {top_prediction['label']}")
+        st.write(f"**Confidence:** {top_prediction['score']:.2f}")