Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+from PIL import Image
+import torch
+
+# Set Streamlit page config
+st.set_page_config(
+    page_title="ViT Image Classifier",
+    layout="centered",
+    page_icon="🖼️",
+)
+
+# Title
+st.title("🧠 ViT Image Classification")
+st.write("This app uses the **Vision Transformer (ViT)** model `google/vit-base-patch16-224` to classify uploaded images.")
+
+# Load pipeline only once using caching
+@st.cache_resource
+def load_pipeline():
+    return pipeline("image-classification", model="google/vit-base-patch16-224")
+
+pipe = load_pipeline()
+
+# Upload an image
+uploaded_file = st.file_uploader("📤 Upload an image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Display the image
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Run classification
+    with st.spinner("🔍 Classifying..."):
+        result = pipe(image)
+
+    # Display results
+    st.subheader("📊 Top Predictions")
+    for i, prediction in enumerate(result):
+        st.write(f"{i+1}. **{prediction['label']}** with score **{prediction['score']:.4f}**")