delete app.py

app.py

@@ -1,39 +0,0 @@
-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}**")