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import streamlit as st
from transformers import ViTFeatureExtractor, ViTForImageClassification
from PIL import Image
import torch
# Load pre-trained model and feature extractor for CIFAR-10
model_name = "aaraki/vit-base-patch16-224-in21k-finetuned-cifar10"
feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)
model = ViTForImageClassification.from_pretrained(model_name)
# CIFAR-10 class names
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
# Streamlit app
st.title("CIFAR-10 Image Classification with Pre-trained Vision Transformer")
# Prediction on uploaded image
st.subheader("Make Predictions")
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Preprocess the uploaded image
image = Image.open(uploaded_file).convert("RGB")
st.image(image, caption='Uploaded Image', use_column_width=True)
inputs = feature_extractor(images=image, return_tensors="pt")
if st.button("Predict"):
with st.spinner("Classifying..."):
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
# Check if the predicted_class_idx is within bounds
if 0 <= predicted_class_idx < len(class_names):
st.write(f"Predicted Class: {predicted_class_idx} ({class_names[predicted_class_idx]})")
else:
st.error("Prediction index out of range.")
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